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diff --git a/deepTools/source/docs/_static/welcome_owl.carousel.theme.css b/deepTools/source/docs/_static/welcome_owl.carousel.theme.css
new file mode 100644
index 0000000000000000000000000000000000000000..2d8a2d456c3026f58170e384615d538062c7db79
--- /dev/null
+++ b/deepTools/source/docs/_static/welcome_owl.carousel.theme.css
@@ -0,0 +1 @@
+.owl-theme .owl-controls{margin-top:10px;text-align:center;-webkit-tap-highlight-color:transparent}.owl-theme .owl-controls .owl-nav [class*=owl-]{color:#fff;font-size:14px;margin:5px;padding:4px 7px;background:#d6d6d6;display:inline-block;cursor:pointer;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}.owl-theme .owl-controls .owl-nav [class*=owl-]:hover{background:#869791;color:#fff;text-decoration:none}.owl-theme .owl-controls .owl-nav .disabled{opacity:.5;cursor:default}.owl-theme .owl-dots .owl-dot{display:inline-block;zoom:1;*display:inline}.owl-theme .owl-dots .owl-dot span{width:10px;height:10px;margin:5px 7px;background:#d6d6d6;display:block;-webkit-backface-visibility:visible;-webkit-transition:opacity 200ms ease;-moz-transition:opacity 200ms ease;-ms-transition:opacity 200ms ease;-o-transition:opacity 200ms ease;transition:opacity 200ms ease;-webkit-border-radius:30px;-moz-border-radius:30px;border-radius:30px}.owl-theme .owl-dots .owl-dot.active span,.owl-theme .owl-dots .owl-dot:hover span{background:#869791}
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diff --git a/deepTools/source/docs/conf.py b/deepTools/source/docs/conf.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a741a451ec7e15ce09e2dbe814a15969cc54615
--- /dev/null
+++ b/deepTools/source/docs/conf.py
@@ -0,0 +1,334 @@
+# -*- coding: utf-8 -*-
+#
+# deepTools documentation build configuration file, created by
+# sphinx-quickstart on Wed Mar 25 12:08:19 2015.
+#
+# This file is execfile()d with the current directory set to its
+# containing dir.
+#
+# Note that not all possible configuration values are present in this
+# autogenerated file.
+#
+# All configuration values have a default; values that are commented out
+# serve to show the default.
+
+import sys
+import os
+import tomllib
+
+# to allow readthedocs to compile without installing some dependencies
+import mock
+
+# MOCK_MODULES = ['numpy', 'numpy.ma', 'scipy', 'pyBigWig']
+MOCK_MODULES = ['pyBigWig', 'py2bit', 'plotly', 'plotly.offline', 'plotly.graph_objs', 'plotly.figure_factory']
+
+for mod_name in MOCK_MODULES:
+ sys.modules[mod_name] = mock.Mock()
+
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+sys.path.insert(0, os.path.abspath('../..'))
+
+# -- General configuration ------------------------------------------------
+
+# If your documentation needs a minimal Sphinx version, state it here.
+# needs_sphinx = '1.0'
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+ 'sphinx.ext.autodoc',
+ 'sphinx.ext.doctest',
+ 'sphinx.ext.coverage',
+ 'sphinx.ext.mathjax',
+ 'sphinx.ext.viewcode',
+ 'sphinx.ext.autosummary',
+ 'sphinxarg.ext'
+]
+# 'numpydoc'
+
+# This is needed to suppress autosummary reordering
+numpydoc_show_class_members = False
+
+# 'sphinxcontrib.restbuilder',
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['source/_templates']
+
+# The suffix(es) of source filenames.
+# You can specify multiple suffix as a list of string:
+# source_suffix = ['.rst', '.md']
+source_suffix = '.rst'
+
+# The encoding of source files.
+# source_encoding = 'utf-8-sig'
+
+# The master toctree document.
+master_doc = 'index'
+
+# General information about the project.
+project = u'deepTools'
+author = u'Fidel Ramírez, Friederike Dündar, Björn Grüning, Thomas Manke, Devon Ryan, Fabian Kilpert, ' \
+ u'Andreas Richter, Vivek Bhardwaj, Steffen Heyne'
+
+# The version info for the project you're documenting, acts as replacement for
+# |version| and |release|, also used in various other places throughout the
+# built documents.
+#
+# The short X.Y version.
+
+
+def get_version():
+ with open('../pyproject.toml', 'rb') as f:
+ d = tomllib.load(f)
+ try:
+ return d['project']['version']
+ except:
+ return None
+
+
+version = get_version()
+# The full version, including alpha/beta/rc tags.
+release = version
+
+# An rst epilog to apper at the end of every page
+rst_epilog = """
++----------------------------------------------------------------+-------------------------------------------------------------+
+| `deepTools Galaxy `_. | `code @ github `_. |
++----------------------------------------------------------------+-------------------------------------------------------------+
+"""
+
+# The language for content autogenerated by Sphinx. Refer to documentation
+# for a list of supported languages.
+#
+# This is also used if you do content translation via gettext catalogs.
+# Usually you set "language" from the command line for these cases.
+language = 'en'
+
+# There are two options for replacing |today|: either, you set today to some
+# non-false value, then it is used:
+# today = ''
+# Else, today_fmt is used as the format for a strftime call.
+# today_fmt = '%B %d, %Y'
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+exclude_patterns = ['_build']
+
+# The reST default role (used for this markup: `text`) to use for all
+# documents.
+# default_role = None
+
+# If true, '()' will be appended to :func: etc. cross-reference text.
+# add_function_parentheses = True
+
+# If true, the current module name will be prepended to all description
+# unit titles (such as .. function::).
+# add_module_names = True
+
+# If true, sectionauthor and moduleauthor directives will be shown in the
+# output. They are ignored by default.
+# show_authors = False
+
+# The name of the Pygments (syntax highlighting) style to use.
+pygments_style = 'sphinx'
+
+# A list of ignored prefixes for module index sorting.
+# modindex_common_prefix = []
+
+# If true, keep warnings as "system message" paragraphs in the built documents.
+# keep_warnings = False
+
+# If true, `todo` and `todoList` produce output, else they produce nothing.
+todo_include_todos = False
+
+
+# -- Options for HTML output ----------------------------------------------
+
+# The theme to use for HTML and HTML Help pages. See the documentation for
+# a list of builtin themes.
+html_theme = 'sphinx_rtd_theme'
+# on_rtd = os.environ.get('READTHEDOCS', None) == 'True'
+
+# if not on_rtd: # only import and set the theme if we're building docs locally
+# import sphinx_rtd_theme
+# html_theme = 'sphinx_rtd_theme'
+# html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
+
+
+# Theme options are theme-specific and customize the look and feel of a theme
+# further. For a list of options available for each theme, see the
+# documentation.
+# html_theme_options = {}
+
+# Add any paths that contain custom themes here, relative to this directory.
+# html_theme_path = []
+
+# The name for this set of Sphinx documents. If None, it defaults to
+# " v documentation".
+# html_title = None
+
+# A shorter title for the navigation bar. Default is the same as html_title.
+# html_short_title = None
+
+# The name of an image file (relative to this directory) to place at the top
+# of the sidebar.
+# html_logo = None
+
+# The name of an image file (within the static path) to use as favicon of the
+# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
+# pixels large.
+# html_favicon = None
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
+
+# Add any extra paths that contain custom files (such as robots.txt or
+# .htaccess) here, relative to this directory. These files are copied
+# directly to the root of the documentation.
+# html_extra_path = []
+
+# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
+# using the given strftime format.
+# html_last_updated_fmt = '%b %d, %Y'
+
+# If true, SmartyPants will be used to convert quotes and dashes to
+# typographically correct entities.
+# html_use_smartypants = True
+
+# Custom sidebar templates, maps document names to template names.
+# html_sidebars = {}
+# html_sidebars = { '**': ['globaltoc.html', 'relations.html', 'sourcelink.html', 'searchbox.html'], }
+
+# Additional templates that should be rendered to pages, maps page names to
+# template names.
+# html_additional_pages = {}
+
+# If false, no module index is generated.
+# html_domain_indices = True
+
+# If false, no index is generated.
+# html_use_index = True
+
+# If true, the index is split into individual pages for each letter.
+# html_split_index = False
+
+# If true, links to the reST sources are added to the pages.
+# html_show_sourcelink = True
+
+# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
+# html_show_sphinx = True
+
+# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
+# html_show_copyright = True
+
+# If true, an OpenSearch description file will be output, and all pages will
+# contain a tag referring to it. The value of this option must be the
+# base URL from which the finished HTML is served.
+# html_use_opensearch = ''
+
+# This is the file name suffix for HTML files (e.g. ".xhtml").
+# html_file_suffix = None
+
+# Language to be used for generating the HTML full-text search index.
+# Sphinx supports the following languages:
+# 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja'
+# 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr'
+# html_search_language = 'en'
+
+# A dictionary with options for the search language support, empty by default.
+# Now only 'ja' uses this config value
+# html_search_options = {'type': 'default'}
+
+# The name of a javascript file (relative to the configuration directory) that
+# implements a search results scorer. If empty, the default will be used.
+# html_search_scorer = 'scorer.js'
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = 'deepToolsdoc'
+
+# -- Options for LaTeX output ---------------------------------------------
+
+latex_elements = {
+ # The paper size ('letterpaper' or 'a4paper').
+ # 'papersize': 'letterpaper',
+
+ # The font size ('10pt', '11pt' or '12pt').
+ # 'pointsize': '10pt',
+
+ # Additional stuff for the LaTeX preamble.
+ # 'preamble': '',
+
+ # Latex figure (float) alignment
+ # 'figure_align': 'htbp',
+}
+
+# Grouping the document tree into LaTeX files. List of tuples
+# (source start file, target name, title,
+# author, documentclass [howto, manual, or own class]).
+latex_documents = [
+ (master_doc, 'deepTools.tex', u'deepTools Documentation',
+ u'Fidel Ramírez, Friederike Dündar, Björn Grüning, Thomas Manke', 'manual'),
+]
+
+# The name of an image file (relative to this directory) to place at the top of
+# the title page.
+# latex_logo = None
+
+# For "manual" documents, if this is true, then toplevel headings are parts,
+# not chapters.
+# latex_use_parts = False
+
+# If true, show page references after internal links.
+# latex_show_pagerefs = False
+
+# If true, show URL addresses after external links.
+# latex_show_urls = False
+
+# Documents to append as an appendix to all manuals.
+# latex_appendices = []
+
+# If false, no module index is generated.
+# latex_domain_indices = True
+
+
+# -- Options for manual page output ---------------------------------------
+
+# One entry per manual page. List of tuples
+# (source start file, name, description, authors, manual section).
+man_pages = [
+ (master_doc, 'deeptools', u'deepTools Documentation',
+ [author], 1)
+]
+
+# If true, show URL addresses after external links.
+# man_show_urls = False
+
+
+# -- Options for Texinfo output -------------------------------------------
+
+# Grouping the document tree into Texinfo files. List of tuples
+# (source start file, target name, title, author,
+# dir menu entry, description, category)
+texinfo_documents = [
+ (master_doc, 'deepTools', u'deepTools Documentation',
+ author, 'deepTools', 'One line description of project.',
+ 'Miscellaneous'),
+]
+
+# Documents to append as an appendix to all manuals.
+# texinfo_appendices = []
+
+# If false, no module index is generated.
+# texinfo_domain_indices = True
+
+# How to display URL addresses: 'footnote', 'no', or 'inline'.
+# texinfo_show_urls = 'footnote'
+
+# If true, do not generate a @detailmenu in the "Top" node's menu.
+# texinfo_no_detailmenu = False
diff --git a/deepTools/source/docs/content/about.rst b/deepTools/source/docs/content/about.rst
new file mode 100644
index 0000000000000000000000000000000000000000..130b29928ef9f275c83f006ff689ef342168f2f6
--- /dev/null
+++ b/deepTools/source/docs/content/about.rst
@@ -0,0 +1,25 @@
+About
+======
+
+Please cite deepTools as follows:
+
+ Ramírez, Fidel, Devon P. Ryan, Björn Grüning, Vivek Bhardwaj, Fabian Kilpert,
+ Andreas S. Richter, Steffen Heyne, Friederike Dündar,
+ and Thomas Manke. `deepTools2: A next Generation Web Server for Deep-Sequencing Data Analysis `_.
+ Nucleic Acids Research (2016). `doi:10.1093/nar/gkw257 `_.
+
+Where deepTools are used:
+
+* DEEP consortium
+* public Galaxy server hosted at ``_.
+* public Galaxy instance hosted by the Max-Planck-Institute for Immunobiology and Epigenetics: deeptools.ie-freiburg.mpg.de
+* in-house Galaxy instance of the Max-Planck-Institute for Immunobiology and Epigenetics
+* Galaxy instance of the University of Freiburg, Germany
+* Galaxy instance of the ICGMB, Strasbourg, France
+* Galaxy instance of LCSB and HPC @ Uni.lu, Belval, Luxembourg
+
+.. image:: ../images/logo_mpi-ie.jpg
+
+This tool suite is developed by the `Bioinformatics Facility `_ at the
+`Max Planck Institute for Immunobiology and Epigenetics,
+Freiburg `_.
diff --git a/deepTools/source/docs/content/advanced_features.rst b/deepTools/source/docs/content/advanced_features.rst
new file mode 100644
index 0000000000000000000000000000000000000000..ea9143042dd35bba417e6518a90ff9b5c3b7317d
--- /dev/null
+++ b/deepTools/source/docs/content/advanced_features.rst
@@ -0,0 +1,13 @@
+Advanced features
+=================
+
+Some of the features of deepTools are not self-explanatory. Below, we provide links to longer expositions on these more advanced features:
+
+ * :doc:`feature/blacklist`
+ * :doc:`feature/metagene`
+ * :doc:`feature/read_extension`
+ * :doc:`feature/unscaled_regions`
+ * :doc:`feature/read_offsets`
+ * :doc:`feature/plotFingerprint_QC_metrics`
+ * :doc:`feature/plotly`
+ * :doc:`feature/effectiveGenomeSize`
diff --git a/deepTools/source/docs/content/api.rst b/deepTools/source/docs/content/api.rst
new file mode 100644
index 0000000000000000000000000000000000000000..d7d5f9e491536e20dd59020e5ee76aeacbd6c09d
--- /dev/null
+++ b/deepTools/source/docs/content/api.rst
@@ -0,0 +1,17 @@
+deepTools API
+=============
+
+deepTools consists of several command line and Galaxy wrappers for summarizing
+the information of Next Generation Sequencing data that can be mapped to a reference genome.
+Through the API, the engine powering the deepTools commands can be used for other purposes as well.
+
+Our :doc:`example_api_tutorial` explains step-by-step how to make use of some deepTools modules to achieve analyses outside the scope of the deepTools suite such as counting reads for certain genome regions and computing the FRiP score.
+
+
+.. toctree::
+ :maxdepth: 4
+
+ example_api_tutorial
+ ../source/deeptools
+
+Complete information can be found in the following links: :ref:`genindex` and :ref:`modindex`
diff --git a/deepTools/source/docs/content/changelog.rst b/deepTools/source/docs/content/changelog.rst
new file mode 100644
index 0000000000000000000000000000000000000000..66c863148db7474e6950bfa6ab0b9cda529a4501
--- /dev/null
+++ b/deepTools/source/docs/content/changelog.rst
@@ -0,0 +1,94 @@
+Changes in deepTools2.0
+========================
+
+.. contents::
+ :local:
+
+Major changes
+-------------
+
+.. note:: The major changes encompass features for **increased efficiency**, **new sequencing data types**, and **additional plots**, particularly for QC.
+
+Moreover, deepTools modules can now be used by other python programs.
+The :ref:`api` is part of the new documentation.
+
+Accommodating additional data types
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* correlation and comparisons can now be calculated for **bigWig files** (in addition to BAM files) using ``multiBigwigSummary`` and ``bigwigCompare``
+
+* **RNA-seq:** split-reads are now natively supported
+
+* **MNase-seq:** using the new option ``--MNase`` in ``bamCoverage``, one can now compute read coverage only taking the 2 central base pairs of each mapped fragment into account.
+
+Structural updates
+^^^^^^^^^^^^^^^^^^^
+
+* All modules have comprehensive and automatic tests that evaluate proper functioning after any modification of the code.
+* Virtualization for stability: we now provide a ``docker`` image and enable the easy deployment of deepTools via the Galaxy ``toolshed``.
+* Our documentation is now version-aware thanks to readthedocs and ``sphinx``.
+* The API is public and documented.
+
+Renamed tools
+^^^^^^^^^^^^^
+
+* **heatmapper** to :doc:`tools/plotHeatmap`
+* **profiler** to :doc:`tools/plotProfile`
+* **bamCorrelate** to :doc:`tools/multiBamSummary`
+* **bigwigCorrelate** to :doc:`tools/multiBigwigSummary`
+* **bamFingerprint** to :doc:`tools/plotFingerprint`
+
+
+Increased efficiency
+^^^^^^^^^^^^^^^^^^^^
+
+* We dramatically improved the **speed** of bigwig related tools (:doc:`tools/multiBigwigSummary` and ``computeMatrix``) by using the new `pyBigWig module `_.
+
+* It is now possible to generate one composite heatmap and/or meta-gene image based on **multiple bigwig files** in one go (see :doc:`tools/computeMatrix`, :doc:`tools/plotHeatmap`, and :doc:`tools/plotProfile` for examples)
+
+* ``computeMatrix`` now also accepts multiple input BED files. Each is treated as a group within a sample and is plotted independently.
+
+* We added **additional filtering options for handling BAM files**, decreasing the need for prior filtering using tools other than deepTools: The ``--samFlagInclude`` and ``--samFlagExclude`` parameters can, for example, be used to only include (or exclude) forward reads in an analysis.
+
+* We separated the generation of read count tables from the calculation of pairwise correlations that was previously handled by ``bamCorrelate``. Now, read counts are calculated first using ``multiBamSummary`` or ``multiBigWigCoverage`` and the resulting output file can be used for calculating and plotting pairwise correlations using ``plotCorrelation`` or for doing a principal component analysis using ``plotPCA``.
+
+New features and tools
+^^^^^^^^^^^^^^^^^^^^^^
+
+* Correlation analyses are no longer limited to BAM files -- bigwig files are possible, too! (see :doc:`tools/multiBigwigSummary`)
+
+* Correlation coefficients can now be computed even if the data contains NaNs.
+
+* Added **new quality control** tools:
+ - use :doc:`tools/plotCoverage` to plot the coverage over base pairs
+ - use :doc:`tools/plotPCA` for principal component analysis
+ - :doc:`tools/bamPEFragmentSize` can be used to calculate the average fragment size for paired-end read data
+
+* Added the possibility for **hierarchical clustering**, besides *k*-means to ``plotProfile`` and ``plotHeatmap``
+
+* ``plotProfile`` has many more options to make compelling summary plots
+
+
+Minor changes
+-------------
+
+Changed parameters names and settings
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* ``computeMatrix`` can now read files with DOS newline characters.
+* ``--missingDataAsZero`` was renamed to ``--skipNonCoveredRegions`` for clarity in ``bamCoverage`` and ``bamCompare``.
+* Read extension was made optional and we removed the need to specify a default fragment length for most of the tools: ``--fragmentLength`` was thus replaced by the new optional parameter ``--extendReads``.
+* Added option ``--skipChromosomes`` to ``multiBigwigSummary``, which can be used to, for example, skip all 'random' chromosomes.
+* Added the option for adding titles to QC plots.
+
+Bug fixes
+^^^^^^^^^
+
+* Resolved an error introduced by ``numpy version 1.10`` in ``computeMatrix``.
+* Improved plotting features for ``plotProfile`` when using as plot type: 'overlapped_lines' and 'heatmap'
+* Fixed problem with BED intervals in ``multiBigwigSummary`` and ``multiBamSummary`` that returned wrongly labeled raw counts.
+* ``multiBigwigSummary`` now also considers chromosomes as identical when the names between samples differ by 'chr' prefix, e.g. chr1 vs. 1.
+* Fixed problem with wrongly labeled proper read pairs in a BAM file. We now have additional checks to determine if a read pair is a proper pair: the reads must face each other and are not allowed to be farther apart than 4x the mean fragment length.
+* For ``bamCoverage`` and ``bamCompare``, the behavior of ``scaleFactor`` was updated such that now, if given in combination with the normalization options (``--normalizeTo1x`` or ``--normalizeUsingRPKM``), the given scaling factor will be multiplied with the factor computed by the respective normalization method.
+
+
diff --git a/deepTools/source/docs/content/example_api_tutorial.rst b/deepTools/source/docs/content/example_api_tutorial.rst
new file mode 100644
index 0000000000000000000000000000000000000000..1770be665bb85aed8556b78e3fcebb2f27584ef3
--- /dev/null
+++ b/deepTools/source/docs/content/example_api_tutorial.rst
@@ -0,0 +1,314 @@
+.. _api:
+
+deepTools API example
+=====================
+
+The following is a short overview of the most useful methods and classes
+from deepTools.
+Complete information can be found in the following links: :ref:`genindex` and :ref:`modindex`
+
+
+Finding read coverage over a region
+-----------------------------------
+
+With deepTools, the read coverage over multiple genomic regions and multiple files can be computed quite quickly using multiple processors.
+First, we start with a simple example that is later expanded upon to demonstrate
+the use of multipe processors.
+In this example we compute the coverage of reads over a small region for bins of 50bp. For this we need the :class:`deeptools.countReadsPerBin` class.
+
+
+.. code:: python
+
+ import deeptools.countReadsPerBin as crpb
+
+
+We also need a BAM file containing the aligned reads.
+The BAM file must be indexed to allow quick access to reads
+falling into the regions of interest.
+
+.. code:: python
+
+ bam_file = "file.bam"
+
+Now, the ``CountReadsPerBin`` object can be initialized.
+The first argument to the constructor is a list of BAM files,
+which in this case is just one file.
+We are going to use a ``binLength`` of 50 bases, with subsequent bins adjacent
+(i.e., the ``stepSize`` between bins is also 50 bases). Overlapping bin
+coverages can be used by setting a ``stepSize`` smaller than ``binLength``.
+
+.. code:: python
+
+ cr = crpb.CountReadsPerBin([bam_file], binLength=50, stepSize=50)
+
+
+Now, we can compute the coverage over a region in chromosome 2 from position 0
+to 1000.
+
+.. code:: python
+
+ cr.count_reads_in_region('chr2L', 0, 1000)
+
+.. parsed-literal::
+
+ (array([[ 2.],
+ [ 3.],
+ [ 1.],
+ [ 2.],
+ [ 3.],
+ [ 2.],
+ [ 4.],
+ [ 3.],
+ [ 2.],
+ [ 3.],
+ [ 4.],
+ [ 6.],
+ [ 4.],
+ [ 2.],
+ [ 2.],
+ [ 1.]]), '')
+
+The result is a tuple with the first element a numpy array with one row per bin and one column per bam file. Since only one BAM file was used, there is only one column. If a file name for saving the raw data had been specificied, then the temporary file name used for this would appear in the second item of the tuple.
+
+Filtering reads
+---------------
+
+If reads should be filtered, the relevant options simply
+need to be passed to the constructor. In the following code, the reads are filtered
+such that only those with a mapping quality of at least 20 and not aligned to the
+reverse strand are kept (samFlag_exclude=16, where 16 is the value for reverse reads, see
+the [SAM Flag Calculator](http://broadinstitute.github.io/picard/explain-flags.html)
+for more info).
+Furthermore, duplicated reads are ignored.
+
+.. code:: python
+
+ cr = crpb.CountReadsPerBin([bam_file], binLength=50, stepSize=50,
+ minMappingQuality=20,
+ samFlag_exclude=16,
+ ignoreDuplicates=True
+ )
+ cr.count_reads_in_region('chr2L', 1000000, 1001000)
+
+.. parsed-literal::
+
+ (array([[ 1.],
+ [ 1.],
+ [ 0.],
+ [ 0.],
+ [ 0.],
+ [ 0.],
+ [ 2.],
+ [ 3.],
+ [ 1.],
+ [ 0.],
+ [ 1.],
+ [ 2.],
+ [ 0.],
+ [ 0.],
+ [ 1.],
+ [ 2.],
+ [ 1.],
+ [ 0.],
+ [ 0.],
+ [ 0.]]), '')
+
+Sampling the genome
+-------------------
+
+Instead of adjacent bins, as in the previous cases, a genome can
+simply be sampled. This is useful to estimate some values,
+like depth of sequencing, without having to look at the complete genome. In the following example,
+10,000 positions of size 1 base are going to be queried from three bam files to compute the average depth of sequencing.
+For this, we set the `numberOfSamples` parameter in the object constructor.
+
+The `run()` method is used instead of `count_reads_in_region` to provide efficient sampling over the entire genome.
+
+.. code:: python
+
+ cr = crpb.CountReadsPerBin([bam_file1, bam_file2, bam_file3],
+ binLength=1, numberOfSamples=10000,
+ numberOfProcessors=10)
+ sequencing_depth = cr.run()
+ print sequencing_depth.mean(axis=0)
+
+.. parsed-literal::
+ [ 1.98923924 2.43743744 22.90102603]
+
+
+The `run()` method splits the computation over 10 processors and collates
+the results. When the parameter `numberOfSamples` is used, the regions selected
+for the computation of the coverage are not random. Instead, the genome is split into 'number-of-samples'
+equal parts and the start of each part is queried for its coverage. You can also compute coverage over selected regions by inputting a BED file.
+
+Now it is possible to make some diagnostic plots from the results:
+
+.. code:: python
+
+ fig, axs = plt.subplots(1, 2, figsize=(15,5))
+ # plot coverage
+ for col in res.T:
+ axs[0].plot(np.bincount(col.astype(int)).astype(float)/total_sites)
+ csum = np.bincount(col.astype(int))[::-1].cumsum()
+ axs[1].plot(csum.astype(float)[::-1] / csum.max())
+ axs[0].set_xlabel('coverage')
+ axs[0].set_ylabel('fraction of bases sampled')
+ # plot cumulative coverage
+
+ axs[1].set_xlabel('coverage')
+ axs[1].set_ylabel('fraction of bases sampled >= coverage')
+
+
+.. image:: ../images/plot_coverage.png
+
+
+Computing the FRiP score
+------------------------
+
+The FRiP score is defined as the fraction of reads that fall into a peak and is
+often used as a measure of ChIP-seq quality. For this example, we
+need a BED file containing the peak regions. Such files are
+usually computed using a peak caller. Also, two bam files are
+going to be used, corresponding to two biological replicates.
+
+.. code:: python
+
+ bed_files = ["peaks.bed"]
+ cr = countReadsPerBin.CountReadsPerBin([bam_file1, bam_file2],
+ bedFile=bed_files,
+ numberOfProcessors=10)
+ reads_at_peaks = cr.run()
+ print reads_at_peaks
+
+.. parsed-literal::
+
+ array([[ 322., 248.],
+ [ 231., 182.],
+ [ 112., 422.],
+ ...,
+ [ 120., 76.],
+ [ 235., 341.],
+ [ 246., 265.]])
+
+
+The result is a numpy array with a row for each peak region and a column for each BAM file.
+
+.. code:: python
+
+ reads_at_peaks.shape
+
+
+.. parsed-literal::
+
+ (6295, 2)
+
+Now, the total number of reads per peaks per bam file is computed:
+
+.. code:: python
+
+ total = reads_at_peaks.sum(axis=0)
+
+Next, we need to find the total number of mapped reads in each of the bam files. For
+this we use the pysam module.
+
+.. code:: python
+
+ import pysam
+ bam1 = pysam.AlignmentFile(bam_file1)
+ bam2 = pysam.AlignmentFile(bam_file2)
+
+Now, `bam1.mapped` and `bam2.mapped` contain the total number of mapped
+reads in each of the bam files, respectively.
+
+Finally, we can compute the FRiP score:
+
+.. code:: python
+
+ frip1 = float(total[0]) / bam1.mapped
+ frip2 = float(total[1]) / bam2.mapped
+ print frip1, frip2
+
+.. parsed-literal::
+
+ 0.170030741997, 0.216740390353
+
+
+
+Using mapReduce to sample paired-end fragment lengths
+------------------------------------------------------
+
+deepTools internally uses a map-reduce strategy, in which a computation is split into smaller
+parts that are sent to different processors. The output from the different processors is subsequently collated. The following
+example is based on the code available for `bamPEFragmentSize.py`
+
+Here, we retrieve the reads from a BAM file and collect the
+fragment length. Reads are retrieved using pysam, and the `read` object returned
+contains the `template_length` attribute, which is the number of bases from the
+leftmost to the rightmost mapped base in the read pair.
+
+First, we will create a function that can collect fragment lengths over a genomic
+position from a BAM file. As we will later call this function using
+mapReduce, the function accepts only one argument, namely
+a tuple with the parameters: chromosome name, start position, end position, and BAM file name.
+
+.. code:: python
+
+ import pysam
+ import numpy as np
+ def get_fragment_length(args):
+ chrom, start, end, bam_file_name = args
+ bam = pysam.AlignmentFile(bam_file_name)
+ f_lens_list = []
+ for fetch_start in range(start, end, 1000000):
+ # simply get the reads over a region of 10000 bases
+ fetch_end = min(end, fetch_start + 10000)
+
+ f_lens_list.append(np.array([abs(read.template_length)
+ for read in bam.fetch(chrom, fetch_start, fetch_end)
+ if read.is_proper_pair and read.is_read1]))
+
+ # concatenate all results
+ return np.concatenate(f_lens_list)
+
+
+Now, we can use `mapReduce` to call this function and compute fragment lengths
+over the whole genome. mapReduce needs to know the chromosome sizes, which
+can be easily retrieved from the BAM file. Furthermore, it needs to know
+the size of the region(s) sent to each processor. For this
+example, a region of 10 million bases is sent to each processor using the `genomeChunkLength` parameter.
+In other words, each processor executes the same `get_fragment_length` function to collect data over
+different 10 million base regions. The arguments to mapReduce are the list of arguments sent to the function, besides
+the first obligatory three (chrom start, end). In this case only one extra argument is passed
+to the function, the BAM file name. The next two positional arguments are the name of the function to call
+(`get_fragment_length`) and the chromosome sizes.
+
+.. code:: python
+
+ import deeptools.mapReduce
+ bam = pysam.AlignmentFile(bamFile)
+ chroms_sizes = list(zip(bam.references, bam.lengths))
+
+ result = mapReduce.mapReduce([bam_file_name],
+ get_fragment_length,
+ chroms_sizes,
+ genomeChunkLength=10000000,
+ numberOfProcessors=20,
+ verbose=True)
+
+ fragment_lengths = np.concatenate(result)
+
+ print("mean fragment length {}".format(fragment_lengths.mean()))
+ print("median fragment length {}".format(np.median(fragment_lengths)))
+
+
+.. parsed-literal::
+
+ 0.170030741997, 0.216740390353
+
+
+Indices and tables
+------------------
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
diff --git a/deepTools/source/docs/content/example_gallery.rst b/deepTools/source/docs/content/example_gallery.rst
new file mode 100644
index 0000000000000000000000000000000000000000..0d8169659051bbf3cef8b86a05aba52fba616a06
--- /dev/null
+++ b/deepTools/source/docs/content/example_gallery.rst
@@ -0,0 +1,274 @@
+Gallery of deepTools plots
+===========================
+
+.. contents:: Published example plots
+ :local:
+
+We're trying to collect a wide variety of plots generated using deepTools.
+For the plots that we created ourselves, we try to point out the options that were used to create
+each image, so perhaps these can serve as inspiration for you.
+
+Normalized ChIP-seq signals and peak regions
+--------------------------------------------
+
+This image was published by `Ibrahim et al., 2014
+(NAR) `__.
+They used deepTools to generate extended reads per kilobase per million
+reads at 10 base resolution and visualized the resulting coverage files in
+`IGV `__.
+
+.. image:: ../images/gallery/coverage_Ibrahim.png
+
+DNase accessibility at enhancers in murine ES cells
+---------------------------------------------------
+
+The following image demonstrates that enhancer regions are typically
+small stretches of highly accessible chromatin (more information on
+enhancers can be found, for example,
+`here `__). In the heatmap,
+yellow and blue tiles indicate a large numbers of reads that were
+sequenced (indicative of open chromatin) and black spots indicate
+missing data points. An appropriate labeling of the y-axis was
+neglected.
+
+.. image:: ../images/gallery/hm_DNase.png
+
+**Fast Facts:**
+
+* `computeMatrix` mode: reference-point
+* *regions file*: BED file with typical enhancer regions from `Whyte et al., 2013 `__ (download `here `__)
+* *signal file*: bigWig file with `DNase signal from UCSC `_
+* *heatmap cosmetics*: labels, titles, heatmap height
+
+**Command:**
+
+::
+
+ $ computeMatrix reference-point \
+ -S DNase_mouse.bigwig \
+ -R Whyte_TypicalEnhancers_ESC.bed \
+ --referencePoint center \
+ -a 2000 -b 2000 \ ## regions before and after the enhancer centers
+ -out matrix_Enhancers_DNase_ESC.tab.gz
+
+ $ plotHeatmap \
+ -m matrix_Enhancers_DNase_ESC.tab.gz\
+ -out hm_DNase_ESC.png \
+ --heatmapHeight 15 \
+ --refPointLabel enh.center \
+ --regionsLabel enhancers \
+ --plotTitle 'DNase signal' \
+
+
+TATA box enrichments around the TSS of mouse genes
+--------------------------------------------------
+
+Using the `TRAP `__
+suite, we produced a bigWig file that contained TRAP scores for the
+well-known TATA box motif along the mouse genome. The TRAP score is a
+measure for the strength of a protein-DNA interaction at a given DNA
+sequence; the higher the score, the closer the motif is to the consensus
+motif sequence. The following heatmap demonstrates that:
+
+- TATA-like motifs occur quite frequently
+- there is an obvious clustering of TATA motifs slightly upstream of
+ the TSS of many mouse genes
+- there are many genes that do not contain TATA-like motifs at their
+ promoter
+
+Note that the heatmap shows *all* mouse RefSeq genes, so ca. 15,000
+genes!
+
+.. image:: ../images/gallery/hm_TATApsem.png
+
+**Fast Facts:**
+
+* `computeMatrix mode`: reference-point
+* *regions file*: BED file with all mouse genes (from UCSC table browser)
+* *signal file*: bigWig file of TATA psem scores
+* *heatmap cosmetics*: color scheme, labels, titles, heatmap height, only showing heatmap + colorbar
+
+**Command:**
+
+::
+
+ $ computeMatrix reference-point \
+ -S TATA_01_pssm.bw \
+ -R RefSeq_genes.bed \
+ --referencePoint TSS \
+ -a 100 -b 100 \
+ --binSize 5 \
+
+ $ plotHeatmap \
+ -m matrix_Genes_TATA.tab.gz \
+ -out hm_allGenes_TATA.png \
+ --colorMap hot_r \
+ --missingDataColor .4 \
+ --heatmapHeight 7 \
+ --plotTitle 'TATA motif' \
+ --whatToShow 'heatmap and colorbar' \
+ --sortRegions ascend
+
+
+Visualizing the GC content for mouse and fly genes
+--------------------------------------------------
+
+It is well known that different species have different genome GC
+contents. Here, we used two bigWig files where the GC content was
+calculated for 50 base windows along the genome of mice and flies and
+the resulting scores visualized for gene regions.
+
+The images nicely illustrate the completely opposite GC distributions in
+flies and mice: while the gene starts of mammalian genomes are enriched
+for Gs and Cs, fly promoters show depletion of GC content.
+
+.. image:: ../images/gallery/hm_GC.png
+
++----------------------+--------------------------------------------------------------------------------------+
+| Fast Facts | |
++======================+======================================================================================+
+| computeMatrix mode | scale-regions |
++----------------------+--------------------------------------------------------------------------------------+
+| regions files | BED files with mouse and fly genes (from UCSC table browser) |
++----------------------+--------------------------------------------------------------------------------------+
+| signal file | bigwig files with GC content |
++----------------------+--------------------------------------------------------------------------------------+
+| heatmap cosmetics | color scheme, labels, titles, color for missing data was set to white, heatmap height|
++----------------------+--------------------------------------------------------------------------------------+
+
+Fly and mouse genes were scaled to different sizes due to the different
+median sizes of the two species' genes (genes of *D.melanogaster*
+contain many fewer introns and are considerably shorter than mammalian
+genes). Thus, computeMatrix had to be run with slightly different
+parameters while the plotHeatmap commands were virtually identical
+(except for the labels).
+
+::
+
+ $ computeMatrix scale-regions \
+ -S GCcontent_Mm9_50_5.bw \
+ -R RefSeq_genes_uniqNM.bed \
+ -bs 50
+ -m 10000 -b 3000 -a 3000 \
+ -out matrix_GCcont_Mm9_scaledGenes.tab.gz \
+ --skipZeros \
+ --missingDataAsZero
+
+ $ computeMatrix scale-regions \
+ -S GCcontent_Dm3_50_5.bw \
+ -R Dm530.genes.bed \
+ -bs 50
+ -m 3000 -b 1000 -a 1000 \
+ -out matrix_GCcont_Dm3_scaledGenes.tab.gz \
+ --skipZeros --missingDataAsZero
+
+ $ plotHeatmap \
+ -m matrix_GCcont_Dm3_scaledGenes.tab.gz \
+ -out hm_GCcont_Dm3_scaledGenes.png \
+ --colorMap YlGnBu \
+ --regionsLabel 'fly genes' \
+ --heatmapHeight 15 \
+ --plotTitle 'GC content fly' &
+
+ $ plotHeatmap \
+ -m matrix_GCcont_Mm9_scaledGenes.tab.gz \
+ -out hm_GCcont_Mm9_scaledGenes.png \
+ --colorMap YlGnBu \
+ --regionsLabel 'mouse genes' \
+ --heatmapHeight 15 \
+ --plotTitle 'GC content mouse' &
+
+
+CpG methylation around murine transcription start sites in two different cell types
+-----------------------------------------------------------------------------------
+
+In addition to the methylation of histone tails, the cytosines
+can also be methylated (for more information on CpG methylation,
+read
+`here `__).
+In mammalian genomes, most CpGs are methylated unless they are in
+gene promoters that need to be kept unmethylated to allow full
+transcriptional activity. In the following heatmaps, we used genes
+expressed primarily in ES cells and checked the
+percentages of methylated cytosines around their transcription start
+sites. The blue signal indicates that very few methylated cytosines are
+found. When you compare the CpG methylation signal between ES cells and
+neuronal progenitor (NP) cells, you can see that the majority of genes remain unmethylated,
+but the general amount of CpG methylation around the TSS increases, as
+indicated by the stronger red signal and the slight elevation of the CpG
+methylation signal in the summary plot. This supports the notion that
+genes stored in the BED file indeed tend to be more expressed in ES
+than in NP cells.
+
+This image was taken from `Chelmicki & Dündar et al. (2014),
+eLife `__.
+
+.. image:: ../images/gallery/hm_CpG.png
+
++----------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| Fast Facts | |
++======================+===================================================================================================================================================================================================+
+| computeMatrix mode | reference-point |
++----------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| regions files | :ref:`BED ` file mouse genes expressed in ES cells |
++----------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| signal file | :ref:`bigWig ` files with fraction of methylated cytosins (from `Stadler et al., 2011 `__) |
++----------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+| heatmap cosmetics | color scheme, labels, titles, color for missing data was set to customized color, y-axis of profiles were changed, heatmap height |
++----------------------+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
+
+The commands for the bigWig files from the ES and NP cells
+were the same:
+
+::
+
+ $ computeMatrix reference-point \
+ -S GSE30202_ES_CpGmeth.bw \
+ -R activeGenes_ESConly.bed \
+ --referencePoint TSS \
+ -a 2000 -b 2000 \
+ -out matrix_Genes_ES_CpGmeth.tab.gz
+
+ $ plotHeatmap \
+ -m matrix_Genes_ES_CpGmeth.tab.gz \
+ -out hm_activeESCGenes_CpG_ES_indSort.png \
+ --colorMap jet \
+ --missingDataColor "#FFF6EB" \
+ --heatmapHeight 15 \
+ --yMin 0 --yMax 100 \
+ --plotTitle 'ES cells' \
+ --regionsLabel 'genes active in ESC'
+
+
+Histone marks for genes of the mosquito *Anopheles gambiae*
+-----------------------------------------------------------
+
+This figure was taken from `Gómez-Díaz et al. (2014): Insights into the
+epigenomic landscape of the human malaria vector *Anopheles gambiae*.
+From Genet
+Aug15;5:277 `__.
+It shows the distribution of H3K27Me3 (left) and H3K27Ac (right) over
+gene features in *A. gambiae* midguts. The enrichment or
+depletion is shown relative to chromatin input. The regions in the map
+comprise gene bodies flanked by a segment of 200 bases at the 5′ end of
+TSSs and TTSs. Average profile across gene regions ±200 bases for each
+histone modification are shown on top.
+
+.. image:: ../images/gallery/hm_histonesGomez.png
+
+Signals of repressive chromatin marks, their enzymes and repeat element conservation scores
+-------------------------------------------------------------------------------------------
+
+This image is from `Bulut-Karsliogu and De La Rosa-Velázquez et al.
+(2014), Mol
+Cell. `__
+The heatmaps depict various signal types for unscaled peak regions of
+proteins and histone marks associated with repressed chromatin. The
+peaks were separated into those containing long interspersed elements
+(LINEs) on the forward and reverse strand. The signals include
+normalized ChIP-seq signals for H3K9Me3, Suv39h1, Suv39h2, Eset, and
+HP1alpha-EGFP, followed by LINE and ERV content and repeat conservation
+scores.
+
+.. image:: ../images/gallery/hm_Bulut.png
+
diff --git a/deepTools/source/docs/content/example_step_by_step.rst b/deepTools/source/docs/content/example_step_by_step.rst
new file mode 100644
index 0000000000000000000000000000000000000000..fe14cd204d4eaf3236b1f656b6a53e0d3b655b19
--- /dev/null
+++ b/deepTools/source/docs/content/example_step_by_step.rst
@@ -0,0 +1,161 @@
+Step-by-step protocols
+========================
+
+This section should give you an overview of how to do many common tasks. We're using **screenshots from Galaxy** here.
+If you're using the command-line version you can easily follow the given examples since the vast majority of parameters is either indicated in Galaxy, too. Otherwise, just type the program name and the help option (e.g. ``/deepTools/bin/bamCoverage --help``), which will show you all the parameters and options available. Alternatively, you can follow the respective link to the tool documentation here on readthedocs.
+
+.. note:: For support or questions please post to `Biostars `__. For bug reports and feature requests please open an issue ``__.
+
+
+All protocols assume that you have uploaded your files into a Galaxy instance with a deepTools installation, e.g., `deepTools Galaxy `_. If you need help to get started with Galaxy in general, e.g. to upload your data, see :doc:`help_galaxy_intro` and :doc:`help_galaxy_dataup`.
+
+.. tip:: If you would like to try out the protocols with **sample data**, go to `deepTools Galaxy `__ --> "Shared Data" --> "Data Libraries" --> "deepTools Test Files". Simply select BED/BAM/bigWig files and click, "to History". You can also download the test data sets to your computer by clicking "Download" at the top.
+
+.. contents:: How to do...?
+ :local:
+
+-----------------------------------
+
+QC and data processing
+-----------------------
+
+I have downloaded/received a BAM file - how do I generate a file I can look at in a genome browser?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* tool: :doc:`tools/bamCoverage`
+* input: your :ref:`BAM` file with aligned reads
+
+Of course, you could also look at your BAM file in the genome browser.
+However, generating a bigWig file of read coverages will drastically reduce the size of the file, it also allows you to normalize the coverage to 1x sequencing depth, which makes a visual comparison of multiple files more feasible.
+
+.. image:: ../images/GalHow_bamCoverage.png
+
+-----------------------------------------
+
+How can I assess the reproducibility of my sequencing replicates?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Typically, you're going to be interested in the correlation of the read coverages for different replicates and different samples. What you want to see is that replicates should correlate better than non-replicates.
+The `ENCODE consortium recommends `_ that *for messenger RNA,
+(...) biological replicates [should] display 0.9 correlation for transcripts/features*. For more information about correlation calculations, see the background description for :doc:`tools/plotCorrelation`.
+
+* tools: :doc:`tools/multiBamSummary` followed by :doc:`tools/plotCorrelation`
+* input: BAM files
+ - you can compare as many samples as you want, though the more you use the longer the computation will take
+
+.. image:: ../images/GalHow_multiBamSummary.png
+
+.. image:: ../images/GalHow_plotCorrelation.png
+
+.. tip:: If you would like to do a similar analysis based on bigWig files, use the tool ``multiBigwigSummary`` instead.
+
+-----------------------------------------
+
+How do I know whether my sample is GC biased? And if it is, how do I correct for it?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* input: :ref:`BAM` file
+* use the tool :doc:`tools/computeGCBias` on that BAM file (default settings, just **make sure your reference genome and genome size are matching**)
+
+.. image:: ../images/GalHow_computeGCbias.png
+
+
+* have a look at the image that is produced and compare it to the examples :ref:`here `
+* if your sample shows an almost linear increase in exp/obs coverage (on the log scale of the lower plot), then you should consider correcting the GC bias - *if* you think that the biological interpretation of this data would otherwise be compromised (e.g. by comparing it to another sample that does not have an inherent GC bias)
+
+ + the GC bias can be corrected with the tool :doc:`tools/correctGCBias` using the second output of the computeGCbias tool that you had to run anyway
+
+.. image:: ../images/GalHow_correctGCbias.png
+
+.. warning:: ``correctGCbias`` will add reads to otherwise depleted regions (typically GC-poor regions), that means that you should **not** remove duplicates in any downstream analyses based on the GC-corrected BAM file. We therefore recommend removing duplicates before doing the correction so that only those duplicate reads are kept that were produced by the GC correction procedure.
+
+-----------------------------------------
+
+How do I get an input-normalized ChIP-seq coverage file?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* input: you need two BAM files, one for the input and one for the ChIP-seq experiment
+* tool: :doc:`tools/bamCompare` with ChIP = treatment, input = control sample
+
+.. image:: ../images/GalHow_bamCompare.png
+
+
+-----------------------------------------
+
+How can I compare the ChIP strength for different ChIP experiments?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* tool: :doc:`tools/plotFingerprint`
+* input: as many BAM files of ChIP-seq samples as you'd like to compare (it is helpful to include the input control to see what a hopefully non-enriched sample looks like)
+
+.. image:: ../images/GalHow_plotFingerprint.png
+
+.. tip:: For more details on the interpretation of the plot, see :doc:`tools/plotFingerprint` or select the tool within the deepTools Galaxy and scroll down for more information.
+
+-----------------------------------------
+
+Heatmaps and summary plots
+---------------------------
+
+How do I get a (clustered) heatmap of sequencing-depth-normalized read coverages around the transcription start site of all genes?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* tools: :doc:`tools/computeMatrix`, then :doc:`tools/plotHeatmap`
+* inputs:
+ * 1 :ref:`bigWig` file of normalized read coverages (e.g. the output of :doc:`tools/bamCoverage` or :doc:`tools/bamCompare`)
+ * 1 :ref:`BED` or INTERVAL file of genes, e.g. obtained through Galaxy via "Get Data" --> "UCSC main table browser" --> group: "Genes and Gene Predictions" --> (e.g.) "RefSeqGenes" --> send to Galaxy (see screenshots below)
+
+.. image:: ../images/GalHow_clustHM01.png
+
+
+* use :doc:`tools/computeMatrix` with the bigWig file and the BED file
+* indicate ``reference-point`` (and whatever other option you would like to tune, see screenshot below)
+
+.. image:: ../images/GalHow_clustHM02.png
+
+* use the output from :doc:`tools/computeMatrix` with :doc:`tools/plotHeatmap`
+ * if you would like to cluster the signals, choose ``k-means clustering`` (last option of "advanced options") with a reasonable number of clusters (usually between 2 to 7)
+
+.. image:: ../images/GalHow_clustHM03.png
+
+-------------------------------------------------------------------
+
+How can I compare the average signal for X-specific and autosomal genes for 2 or more different sequencing experiments?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Make sure you're familiar with computeMatrix and plotProfile before using this protocol.
+
+* tools:
+ * Filter data on any column using simple expressions
+ * computeMatrix
+ * plotProfile
+ * (plotting the summary plots for multiple samples)
+
+* inputs:
+ * several bigWig files (one for each sequencing experiment you would like to compare)
+ * two BED files, one with X-chromosomal and one with autosomal genes
+
+How to obtain a BED file for X chromosomal and autosomal genes each
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+1. download a full list of genes via "Get Data" --> "UCSC main table browser" --> group:"Genes and Gene Predictions" --> tracks: (e.g.) "RefSeqGenes" --> send to Galaxy
+
+2. filter the list twice using the tool **"Filter data on any column using simple expressions"**
+
+ - first use the expression: c1=="chrX" to filter the list of all genes --> this will generate a list of X-linked genes
+ - then re-run the filtering, now with c1!="chrX", which will generate a list of genes that do not belong to chromosome X (!= indicates "not matching")
+
+Compute the average values for X and autosomal genes
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+* use :doc:`tools/computeMatrix` for all of the signal files (bigWig format) at once
+
+ * supply both filtered BED files (click on "Add new regions to plot" once) and label them
+ * indicate the corresponding signal files
+
+* now use :doc:`tools/plotProfile` on the resulting file
+
+ * important: display the "advanced output options" and select "save the data underlying the average profile" --> this will generate a table in addition to the summary plot images
+
+.. image:: ../images/GalHow_profiles_XvsA02.png
+ :target: ../images/GalHow_profiles_XvsA02.png
diff --git a/deepTools/source/docs/content/example_usage.rst b/deepTools/source/docs/content/example_usage.rst
new file mode 100644
index 0000000000000000000000000000000000000000..57e37452db17c56d877af9b36c80f3b8ed0952ba
--- /dev/null
+++ b/deepTools/source/docs/content/example_usage.rst
@@ -0,0 +1,84 @@
+Example usage
+=============
+
+.. contents::
+ :local:
+.. toctree::
+ :maxdepth: 1
+
+ example_step_by_step
+ example_gallery
+
+How we use deepTools for ChIP-seq analyses
+-------------------------------------------
+
+
+To get a feeling for what deepTools can do, we'd like to give you a brief glimpse into how we typically use deepTools for ChIP-seq analyses. For more detailed exampes and descriptions of the tools, simply follow the respective links.
+
+.. note:: While some tools, such as :doc:`tools/plotFingerprint`, specifically address ChIP-seq-issues, the majority of tools is widely applicable to deep-sequencing data, including RNA-seq.
+
+.. image:: ../images/start_workflow.png
+
+As shown in the flow chart above, our work usually begins with one or
+more :ref:`FASTQ `
+file(s) of deeply-sequenced samples. After preliminary quality control using
+`FASTQC `__,
+we align the reads to the reference genome, e.g., using
+`bowtie2 `__.
+The standard output of bowtie2 (and other mapping tools) is in the form of sorted and indexed :ref:`BAM` files
+that provide the common input and starting point for all subsequent deepTools analyses.
+We then use deepTools to assess the quality of the aligned reads:
+
+1. **Correlation between BAM files** (:doc:`tools/multiBamSummary` and :doc:`tools/plotCorrelation`).
+ Together, these two modules perform a very basic test to see whether
+ the sequenced and aligned reads meet your expectations. We use this
+ check to assess reproducibility - either between replicates
+ and/or between different experiments that might have used the same
+ antibody or the same cell type, etc. For instance, replicates should
+ correlate better than differently treated samples.
+
+ .. tip:: You can also assess the correlation of :ref:`bigWig` files using :doc:`tools/multiBigwigSummary`.
+
+.. image:: ../images/test_plots/heatmap_SpearmanCorr_readCounts.png
+ :width: 70%
+
+2. **Coverage check** (:doc:`tools/plotCoverage`). To see how many bp in the genome are actually covered by (a good number) of sequencing reads, we use :doc:`tools/plotCoverage` which generates two diagnostic plots that help us decide whether we need to sequence deeper or not. The option ``--ignoreDuplicates`` is particularly useful here!
+
+.. image:: ../images/test_plots/ExamplePlotCoverage.png
+ :width: 70%
+
+For paired-end samples, we often additionally check whether the fragment sizes are more or less what we would expected based on the library preparation. The module :doc:`tools/bamPEFragmentSize` can be used for that.
+
+.. image:: ../images/test_plots/fragmentSize.png
+ :width: 60%
+
+3. **GC-bias check** (:doc:`tools/computeGCBias`). Many sequencing protocols
+ require several rounds of PCR-based DNA amplification, which often introduces notable bias, due to many DNA polymerases preferentially amplifying GC-rich templates. Depending on the sample (preparation), the GC-bias can vary significantly and we routinely check its extent. When we need to compare files with different GC biases, we use the :doc:`tools/correctGCBias` module.
+ See the paper by `Benjamini and Speed `__ for many insights into this problem.
+
+.. image:: ../images/test_plots/ExampleCorrectGCBias.png
+ :width: 50%
+
+4. **Assessing the ChIP strength**. We do this quality control step to get a
+ feeling for the signal-to-noise ratio in samples from ChIP-seq
+ experiments. It is based on the insights published by `Diaz et
+ al. `_
+
+.. image:: ../images/test_plots/fingerprints.png
+ :width: 70%
+
+Once we're satisfied with the basic quality checks, we normally **convert**
+the large :ref:`BAM ` files into a leaner data format, typically
+:ref:`bigWig `.
+bigWig files have several advantages over BAM files, mainly stemming
+from their significantly decreased size:
+
+- useful for data sharing and storage
+- intuitive visualization in Genome Browsers (e.g.
+ `IGV `__)
+- more efficient downstream analyses are possible
+
+The deepTools modules :doc:`tools/bamCompare` and :doc:`tools/bamCoverage` not only allow for simple conversion of BAM to bigWig (or :ref:`bedGraph` for that matter), but also for normalization, such that different samples can be compared despite differences in their sequencing depth.
+
+Finally, once all the converted files have passed our visual inspections (e.g., using the `Integrative Genomics Viewer `_), the fun
+of downstream analysis with :doc:`tools/computeMatrix`, :doc:`tools/plotHeatmap` and :doc:`tools/plotProfile` can begin!
diff --git a/deepTools/source/docs/content/feature/blacklist.rst b/deepTools/source/docs/content/feature/blacklist.rst
new file mode 100644
index 0000000000000000000000000000000000000000..78562ab930d7767ca338fb83f7bd6467793c8cfe
--- /dev/null
+++ b/deepTools/source/docs/content/feature/blacklist.rst
@@ -0,0 +1,14 @@
+Blacklist Regions
+=================
+
+There are many sources of bias in ChIPseq experiments. Among the most prevalent of these is signal arising from "blacklist" regions (see `Carroll et al. `__ and the references therein for historical context). Blacklisted regions show notably enriched signal across many ChIP experiment types (e.g., regardless of what is being IPed or the experimental conditions). Including these regions can lead not only to false-positive peaks, but can also throw off between-sample normalization. An example of this is found below:
+
+.. image:: ../../images/feature-blacklist0.png
+
+The region on chromosome 9 starting around position 3 million marks the start of an annotated satellite repeat. As this region contains vastly more reads than expected, slight differences in enrichment here between samples can cause errors in between-sample scaling, thereby masking signal in non-repetitive regions. This can be seen in the IGV screenshot below, where the blacklisted region is just off the side of the screen.
+
+.. image:: ../../images/feature-blacklist1.png
+
+Note that the signal outside of the blacklisted region is slightly depressed due to the blacklisted region. Using the `--blackListFileName` option available throughout deepTools. The subtraction of these regions is accounted for in all normalizations.
+
+.. note:: Some programs, such as ``bamCoverage``, can use the number of reads in a file to estimate coverage for the purposes of normalization. Reads in blacklisted regions are subtracted for this purpose, but do note that the number of reads in blacklisted regions is computed by counting the number of reads fully contained within each region. Consequently, if you use regions that are overlapping, it's possible to double count the number of reads that will be blacklisted and thereby decrease the reliability of the normalization. As of version 2.5.5, deepTools will print a warning and quit if there are overlapping blacklist regions. It will also issue a warning if any of them are closer than 1kb, as this is unlikely to be biologically reasonable (in practice it will likely underestimate the number of reads and regions that actually SHOULD be blacklisted).
diff --git a/deepTools/source/docs/content/feature/effectiveGenomeSize.rst b/deepTools/source/docs/content/feature/effectiveGenomeSize.rst
new file mode 100644
index 0000000000000000000000000000000000000000..73c41ffaaf1df3897abac8fd66990785b5c38168
--- /dev/null
+++ b/deepTools/source/docs/content/feature/effectiveGenomeSize.rst
@@ -0,0 +1,63 @@
+Effective Genome Size
+=====================
+
+A number of tools can accept an "effective genome size". This is defined as the length of the "mappable" genome. There are two common alternative ways to calculate this::
+
+ 1. The number of non-N bases in the genome.
+ 2. The number of regions (of some size) in the genome that are uniquely mappable (possibly given some maximal edit distance).
+
+Option 1 can be computed using ``faCount`` from `Kents tools `__.
+The effective genome size for a number of genomes using this method is given below:
+
+
++---------------+------------------+
+| Genome | Effective size |
++===============+==================+
+|GRCh37 | 2864785220 |
++---------------+------------------+
+|GRCh38 | 2913022398 |
++---------------+------------------+
+|T2T/CHM13CAT_v2| 3117292070 |
++---------------+------------------+
+|GRCm37 | 2620345972 |
++---------------+------------------+
+|GRCm38 | 2652783500 |
++---------------+------------------+
+|GRCm39 | 2654621783 |
++---------------+------------------+
+|dm3 | 162367812 |
++---------------+------------------+
+|dm6 | 142573017 |
++---------------+------------------+
+|GRCz10 | 1369631918 |
++---------------+------------------+
+|GRCz11 | 1368780147 |
++---------------+------------------+
+|WBcel235 | 100286401 |
++---------------+------------------+
+|TAIR10 | 119482012 |
++---------------+------------------+
+
+
+
+These values only appropriate if multimapping reads are included. If they are excluded (or there's any MAPQ filter applied),
+then values derived from option 2 are more appropriate.
+These are then based on the read length.
+We can approximate these values for various read lengths using the `khmer program `__ program and ``unique-kmers.py`` in particular.
+A table of effective genome sizes given a read length using this method is provided below:
+
++-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+
+|Read length | GRCh37 | GRCh38 | T2T/CHM13CAT_v2 | GRCm37 | GRCm38 | GRCm39 | dm3 | dm6 | GRCz10 | GRCz11 | WBcel235 | TAIR10 |
++=================+=================+=================+=================+=================+=================+=================+=================+=================+=================+=================+=================+=================+
+|50 | 2685511454 | 2701495711 | 2725240337 | 2304947876 | 2308125299 | 2309746861 | 130428510 | 125464678 | 1195445541 | 1197575653 | 95159402 | 114339094 |
++-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+
+|75 | 2736124898 | 2747877702 | 2786136059 | 2404646149 | 2407883243 | 2410055689 | 135004387 | 127324557 | 1251132611 | 1250812288 | 96945370 | 115317469 |
++-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+
+|100 | 2776919708 | 2805636231 | 2814334875 | 2462480910 | 2467481008 | 2468088461 | 139647132 | 129789773 | 1280188944 | 1280354977 | 98259898 | 118459858 |
++-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+
+|150 | 2827436883 | 2862010428 | 2931551487 | 2489384085 | 2494787038 | 2495461690 | 144307658 | 129940985 | 1312207019 | 1311832909 | 98721103 | 118504138 |
++-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+
+|200 | 2855463800 | 2887553103 | 2936403235 | 2513019076 | 2520868989 | 2521902382 | 148523810 | 132508963 | 1321355041 | 1322366338 | 98672558 | 117723393 |
++-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+
+|250 | 2855044784 | 2898802627 | 2960856300 | 2528988583 | 2538590322 | 2538633971 | 151901455 | 132900923 | 1339205109 | 1342093482 | 101271756 | 119585546 |
++-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+-----------------+
diff --git a/deepTools/source/docs/content/feature/metagene.rst b/deepTools/source/docs/content/feature/metagene.rst
new file mode 100644
index 0000000000000000000000000000000000000000..202ff22ab90f06726d23d52332c269189036dd84
--- /dev/null
+++ b/deepTools/source/docs/content/feature/metagene.rst
@@ -0,0 +1,12 @@
+Metagene analyses
+=================
+
+By default, `computeMatrix` uses the signal over entire contiguous regions (e.g., transcripts) for computing its output. While this is typically quite useful, in case such as RNAseq the results are less than ideal. Take, for example, the gene model and coverage profile below:
+
+.. image:: ../../images/feature-metagene0.png
+
+If clustering were done using such blocky coverage then the results would be biased by the number of exons and their positions. Instead, it's normally desired to ignore intronic regions and instead use only the signal in exons (denoted by blocks in the gene model). This can be accomlished by using the `--metagene` option in `computeMatrix` and supplying a BED12 or GTF file as a set of regions:
+
+.. image:: ../../images/feature-metagene1.png
+
+Note that for GTF files the regions used to define exons can be easily modified. For example, for RiboSeq samples it's preferable to use annotated coding regions, so specifying `--exonID CDS`. Likewise, entire genes can be used rather than transcripts by specifying `--transcriptID gene --transcript_id_designator gene_id`.
diff --git a/deepTools/source/docs/content/feature/plotFingerprint_QC_metrics.rst b/deepTools/source/docs/content/feature/plotFingerprint_QC_metrics.rst
new file mode 100644
index 0000000000000000000000000000000000000000..7d84a56e9ee5448dca5c27ffec6a5e0ec3434e95
--- /dev/null
+++ b/deepTools/source/docs/content/feature/plotFingerprint_QC_metrics.rst
@@ -0,0 +1,53 @@
+plotFingerprint QC metrics
+==========================
+
+As of version 2.4.0, plotFingerprint can optionally output a number of metrics useful for assisting the interpretation of fingerprint plots. A number of these metrics require a matched input sample, which can be specified by ``--JSDsample``.
+
+Sequencing-depth dependence
+---------------------------
+
+An important caveat with all of the QC metrics, except "Synthetic JS distance", is that sequencing depth plays a roll in the background assumption of what a good value is. This is visually demonstrated below, where the various curves represent what a perfectly behaved input sample should look like with a variety of average sequencing depths ("lambda").
+
+.. image:: ../../images/plotFingerprintQC2.png
+
+This plot is equivalent to those generated by ``plotFingerprint``, with the axes labeled differently. Note that with low coverage, the background expectation is less a diagonal line from the lower-left to the upper-right and more of a convex curve or, at very low coverage, a straight line with a given X-intercept. For this reason, a number of "Synthetic" metrics are produced for each sample. These represent the metrics for artificial plotFingerprint results like those above. These are useful for putting the results you observe for your samples into perspective. For example, your elbow point of 0.95 for a ChIP sample might not be so impressive if, because of low sequencing depth, an input sample would be expected to have an elbow point of 0.85.
+
+The metrics
+-----------
+
+.. note:: There are many QC metrics, of which we find the "JS distance" the most useful!
+
+- AUC: The "area under the curve", with a maximum value of 0.5. Lower values generally indicate higher and more focal enrichment.
+
+.. image:: ../../images/plotFingerprintQC1.png
+
+- Synthetic AUC: The expected area under the curve of a perfectly behaved input sample having the same mean sequencing depth of a given sample. This is useful to put the observed AUC into perspective.
+- X-intercept: The point (on the X-axis) at which the curve is not 0. This is approximately the percentage of the genome that was not sequenced in a particular sample. Ideally, this value will be near 0. In practice, however, this can be quite high, due to things like low sequencing depth (see above) or extremely high enrichment resulting in peaks sponging up all of the available reads.
+- Synthetic X-intercept: The expected X-intercept of a perfectly behaved input sample having the same mean sequencing depth of a given sample. This is useful to put the observed X-intercept into perspective.
+- Elbow Point: The elbow point attempts to measure the position at which the line turns upward. In practice, this is the point at which the plotted line is furthest from the line from the lower-left to the upper-right corner of the graph (equivalent to a perfect input sample with infinite coverage). The point returned is the position on the X-axis of this elbow point and higher values indicate more enrichment.
+- Synthetic Elbow Point: The expected elbow point of a perfectly behaved input sample having the same mean sequencing depth of a given sample. This is useful to put the observed elbow point into perspective.
+- JS distance: This is the Jensen-Shannon distance between a given sample and that specified by ``--JSDsample`` and is based on work from Sitanshu Gakkhar. The Jensen-Shannon divergence is defined as follows:
+
+.. math::
+ \begin{align}
+ JSD(P \parallel Q) = \frac{1}{2} D_{KL}(P \parallel M) + \frac{1}{2} D_{KL}(Q \parallel M) \\
+ M = \frac{1}{2} (P + Q) \\
+ D_{KL}({X} \parallel {Y}) = \sum_{i} X_i log\Big(\frac{X_i}{Y_i}\Big)
+ \end{align}
+
+Here, ``D`` is the Kullback-Leibler divergence. ``P`` and ``Q`` are the probability mass functions underlying the lines in the plots. The JS distance is the square root of the JS divergence shown above. Higher values indicate greater difference between the two curves, with minimum and maximum values of 0 and 1, respectively.
+
+- Synthetic JS distance: As shown above, the expected distribution of a perfect input sample is dependent on its sequencing depth, meaning that if a sample and its matched control have very different depths then the JS distance between them is misleading. Consequently, rather than displaying the JS distance between two samples, this metric shows the JS distance between a given sample and a perfect input sample with the same coverage depth (i.e., the plot generated from the Poisson probability mass function with lambda equal to the mean coverage in the sample). Ideally, this metric and that above will be very similar, but may not be if sequencing depth is very different (in which case, this metric is likely more reliable). Note also that this metric is printed even for the sample indicated by the ``--JSDsample`` option, which is useful to assess the level of bias present in the input sample, which should ideally have coverage with a Poisson distribution.
+- % genome enriched: This is a metric originating from the `CHANCE `__ tool. This is computed by first finding the elbow point (essentially as described above), and then computing 1 minus that. This then represents the approximate percentage of the genome enriched in signal (e.g., bound by a transcription factor or having a certain histone modification).
+- diff. enrichment: The differential enrichment between a given sample and that indicated by ``--JSDsample`` at the elbow point. This is also a metric introduced by the CHANCE tool. Higher percentages are generally better.
+- CHANCE divergence: The divergence metric introduced by the CHANCE tools (and seemingly undocumented). In many ways, this is similar to the JS distance metric. The computation starts with the values at the elbow point of a given sample (``P``) and the sample given by ``--JSDsample`` (``Q``). Given ``P`` and ``Q``, the "CHANCE divergence" is calculated as follows:
+
+.. math::
+ \begin{align}
+ CHANCE divergence = \sqrt{\frac{1}{2} (binRelEntropy(P, M) + binRelEntropy(Q, M))} \\
+ M = \frac{1}{2} (P + Q) \\
+ binRelEntropy(X, Y) = X log_2 \Big(\frac{X}{Y}\Big) + (1 - X) log_2 \Big(\frac{1 - X}{1 - Y} \Big)
+ \end{align}
+
+
+The ``binRelEntropy`` function is similar to a mixture of binary entropy and Kullback-Leibler divergence. Note that if ``X`` is 0, the ``X * log2(X/Y)`` is 0. Similarly, if ``X`` is 1, then ``(1 - X) * log2((1 - X) / (1 - Y))`` is 0.
diff --git a/deepTools/source/docs/content/feature/plotly.rst b/deepTools/source/docs/content/feature/plotly.rst
new file mode 100644
index 0000000000000000000000000000000000000000..1d9cf8ff9469f82a49a25c9501add9fe62c40440
--- /dev/null
+++ b/deepTools/source/docs/content/feature/plotly.rst
@@ -0,0 +1,6 @@
+Plotly
+======
+
+`Plotly `__ is a tool for interactive visualization of datasets that deepTools has supported since version 2.6. The output of this "image format" is a web page that you can load in your browser of choice. Generally the resulting webpages will look essentially like the default images, but you can do things such as mouse-over an area to get the sample label or value and zoom in/pan around images. These web pages can grow to be **very** large, particularly for `plotHeatmap`. Further, not all deepTools options will be supported. In particular, in `plotHeatmap`, only a single colorbar is supported. These limitations are due to limitations in plotly itself and may be removed as plotly itself matures.
+
+Note that plotly output is saved locally and remote servers are not used. This is due to most users likely wishing to keep their data private. If you would like to edit the resulting pages and export them to plot.ly then click on the link in the bottom-right corner of the plotly output.
diff --git a/deepTools/source/docs/content/feature/read_extension.rst b/deepTools/source/docs/content/feature/read_extension.rst
new file mode 100644
index 0000000000000000000000000000000000000000..80c8328262ba399db5b7c03891f33133092bacfc
--- /dev/null
+++ b/deepTools/source/docs/content/feature/read_extension.rst
@@ -0,0 +1,30 @@
+Read extension
+==============
+
+In the majority of NGS experiment, DNA (or RNA) is fragmented into small stretches and only the ends of these fragments sequenced. For many applications, it's desirable to quantify coverage of the entire original fragments over the genome. Consequently, there is an `--extendReads` option present throughout deepTools. This works as follows:
+
+Paired-end reads
+----------------
+
+ 1. Regions of the genome are sampled to determine the median fragment/read length.
+ 2. The genome is subdivided into disjoint regions. Each of these regions comprises one or more bins of some desired size (specified by `-bs`).
+ 3. For each region, all alignments overlapping it are gathered. In addition, all alignments within 2000 bases are gathered, as 2000 bases is the maximum allowed fragment size.
+ 4. The resulting collection of alignments are all extended according to their fragment length, which for paired-end reads is indicated in BAM files.
+
+ - For singletons, the expected fragment length from step 1 is used.
+
+ 5. For each of the extended reads, the count in each bin that it overlaps is incremented.
+
+Single-end reads
+----------------
+
+ 1. An extension length, L, is specified.
+ 2. The genome is subdivided into disjoint regions. Each of these regions comprises one or more bins of some desired size (specified by `-bs`).
+ 3. For each region, all alignments overlapping it are gathered. In addition, all alignments within 2000 bases are gathered, as 2000 bases is the maximum allowed fragment size.
+ 4. The resulting collection of alignments are all extended to length L.
+ 5. For each of the extended reads, the count in each bin that it overlaps is incremented.
+
+Blacklisted regions
+-------------------
+
+The question likely arises as to how alignments originating inside of blacklisted regions are handled. In short, any alignment contained completely within a blacklisted region is ignored, regardless of whether it would extend into a non-blacklisted region or not. Alignments only partially overlapping blacklisted regions are treated as normal, as are pairs of reads that span over a blacklisted region. This is primarily for the sake of performance, as otherwise each extended read would need to be checked to see if it overlaps a blacklisted region.
diff --git a/deepTools/source/docs/content/feature/read_offsets.rst b/deepTools/source/docs/content/feature/read_offsets.rst
new file mode 100644
index 0000000000000000000000000000000000000000..295ba39280728a6bfa8a3bcb768c8fc43ae206aa
--- /dev/null
+++ b/deepTools/source/docs/content/feature/read_offsets.rst
@@ -0,0 +1,8 @@
+Offsetting signal to a given position
+=====================================
+
+A growing number of experiment types need to be analyzed by focusing the signal from each alignment at a single point. As an example, RiboSeq alignments tend to be offset such that the signal pause is centered around the translation start site, an offset of around 12. Alternatively, in GROseq experiments, the pause around the TSS becomes centered by using the 1st base of each read. This can be accomplished within `bamCoverage` using the `--Offset` option. A visual example is below:
+
+.. image:: ../../images/feature-offset0.png
+
+The alignments shown above overlap a transcript, denoted as a blue box, which in this case represents only the coding sequence. If the alignments are from a RiboSeq experiment then the signal from each alignment should be set at the ~12th base of each alignment. The section on the right denotes the resulting signal intensity, with the expected large peak at the translation start site.
diff --git a/deepTools/source/docs/content/feature/unscaled_regions.rst b/deepTools/source/docs/content/feature/unscaled_regions.rst
new file mode 100644
index 0000000000000000000000000000000000000000..5cbb0a6591f7efe309c2af42ca9e83eaa15b8630
--- /dev/null
+++ b/deepTools/source/docs/content/feature/unscaled_regions.rst
@@ -0,0 +1,7 @@
+Unscaled regions
+================
+
+Some experiments aim to quantify the distribution of pausing of factors, such as PolII, throughout gene or transcript bodies. PolII and many other factors, show pausing (i.e., accumulation of signal) near the start/end of transcripts. As scaling is normally performed to make all regions the same length, the breadth of the paused region could be scaled differently in each transcript. This would, in turn, cause biases during clustering or other analyses. In such cases, the `--unscaled5prime` and `--unscaled3prime` options in `computeMatrix` can be used. These will prevent regions at one or both end of transcripts (or other regions) to not be excluded from scaling, thereby allowing raw signal profiles to be compared across transcripts. An example of this from `Ferrari et al. 2013 `__ is shown below:
+
+.. image:: ../../images/feature-unscaled0.png
+
diff --git a/deepTools/source/docs/content/help_faq.rst b/deepTools/source/docs/content/help_faq.rst
new file mode 100644
index 0000000000000000000000000000000000000000..b6e397e6dccaf05817e1ecdea1064448954da4c6
--- /dev/null
+++ b/deepTools/source/docs/content/help_faq.rst
@@ -0,0 +1,309 @@
+General FAQ
+===========
+
+.. tip:: For support or questions please post to `Biostars `__. For bug reports and feature requests please open an issue ``__.
+
+.. Note:: We also have a :doc:`help_faq_galaxy` with questions that are more specific to Galaxy rather than deepTools usage.
+
+.. contents::
+ :local:
+
+
+--------------------------------------------------
+
+How does deepTools handle data from paired-end sequencing?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Generally, all the modules working on :ref:`BAM` files (``multiBamSummary``, ``bamCoverage``, ``bamCompare``, ``plotFingerprint``, ``computeGCBias``) automatically recognize paired-end sequencing data and will use the fragment size based on the distance between read pairs.
+You can by-pass the typical fragment handling on mate pairs with the option ``--doNotExtendPairedEnds`` (can be found under "advanced options" in Galaxy).
+
+--------------------------------------------------
+
+How can I test a tool with little computation time?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+When you're playing around with the tools to see what kinds of results they will produce, you can limit the operation to one chromosome or a specific region to save time. In Galaxy, you will find this under "advanced output options" --> "Region of the genome to limit the operation to". The command line option is called ``--region (CHR:START:END)``.
+
+The following tools currently have this option:
+
+* :doc:`tools/multiBamSummary`
+* :doc:`tools/plotFingerprint`
+* :doc:`tools/computeGCBias`, :doc:`tools/correctGCBias`
+* :doc:`tools/bamCoverage`, :doc:`tools/bamCompare`
+
+It works as follows: first, the *entire* genome represented in the :ref:`BAM ` file will be regarded and sampled, *then* all the regions or sampled bins that do not overlap the region indicated by the user will be discarded.
+
+.. note:: You can limit the operation to only **one** chromosome (or **one** specific locus on a chromosome) at a time. If you would like to limit the operation to more than one region, see the answer to the next question.
+
+-------------------------------------------------------
+
+Can I specify more than one chromosome in the ``--regions`` option?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The short answer is: no.
+
+Several programs allow specifying a specific regions.
+For these, the input must be in the format of ``chr:start:end``, for example "chr10" or "chr10:456700:891000".
+
+For these programs, it is **not possible** to indicate more than one region, e.g. chr10, chr11 - **this will not work**! Here are some ideas for workarounds if you none-the-less need to do this:
+
+General workaround
+~~~~~~~~~~~~~~~~~~
+
+Since all the tools that have the ``--region`` option work on :ref:`BAM` files, you could *filter your reads* prior to running the program, e.g. using ``intersectBed`` with ``--abam`` or ``samtools view``. Then use the resulting (smaller) BAM file with the deepTools program of your choice.
+
+.. code::
+
+ $ samtools view -b -L regionsOfInterest.bed Reads.bam > ReadsOverlappingWithRegionsOfInterest.bam
+
+or
+
+.. code::
+
+ $ intersectBed -abam Reads.bam -b regionsOfInterest.bed > ReadsOverlappingWithRegionsOfInterest.bam
+
+Build-in solutions
+~~~~~~~~~~~~~~~~~~~~
+
+``computeGCBias`` and ``multiBamSummary`` offer build-in solutions so that you do not need to resort to tools outside of deepTools.
+
+:doc:`tools/multiBamSummary` has two modes, ``bins`` and ``BED``.
+ If you make use of the ``BED`` mode, you can supply a :ref:`BED` file of regions that you would like to limit the operation to. This will do the same thing as in the general workaround mentioned above.
+
+:doc:`tools/computeGCBias` has a ``--filterOut`` option.
+ If you to create a BED file that contains all the regions you are **not** interested in, you can then supply this file to ``computeGCBias --filterOut Regions_to_be_ignored.bed`` and those regions will subsequently be ignored.
+
+------------------------------------------------
+
+When should I exclude regions from ``computeGCBias``?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+.. note:: In general, we recommend to only correct for GC bias (using :doc:`tools/computeGCBias` followed by :doc:`tools/correctGCBias`) if the majority of the genome (e.g., for mouse and human genomes the region between 30-60%) is GC-biased *and* you want to compare this sample with another sample that is not GC-biased.
+
+Sometimes, a certain GC bias is expected, for example for ChIP samples of H3K4Me3 in mammalian samples where GC-rich promoters are expected to be enriched. To not confound the GC bias caused by the library preparation with the inherent, expected GC-bias, we incorporated the possibility to supply a file of regions to ``computeGCBias`` that will be excluded from the GC bias calculation. This file should typically contain those regions that one expects to be significantly enriched. This allows the tool to focus on background regions.
+
+---------------------------------------------------
+
+When should I use ``bamCoverage`` or ``bamCompare``?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Both tools produce :ref:`bigWig` files, i.e. they translate the read-centered information from a :ref:`BAM` file into scores for genomic regions of a fixed size. The only difference is the *number of BAM files* that the tools use as input: while :doc:`tools/bamCoverage` will only take one BAM file and produce a coverage file that is mostly normalized for sequencing depth, :doc:`tools/bamCompare` will take *two* :ref:`BAM` files that can be compared with each other using several mathematical operations.
+
+``bamCompare`` will always normalize for sequencing depth like ``bamCoverage``, but then it will perform additional calculations depending on what the user chose, for example:
+
+* ChIP vs. :ref:`input `
+ obtain a :ref:`bigWig` file of log2ratios(ChIP/input)
+* treatment vs. control
+ obtain a :ref:`bigWig` file of *differences* (treatment - control)
+* replicate 1 and replicate 2
+ obtain a :ref:`bigWig` file where the values from two :ref:`BAM` files are summed up (replicate 1 + replicate 2)
+
+-----------------------------------------------------
+
+What should I pay attention to when dealing with RNA-seq data?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+By default, deepTools (**since version 2**) makes use of the information stored in the so-called CIGAR string of the alignment file (`SAM/BAM specification
+`_). The CIGAR tells precisely to which bases of the reference a read maps - and, accordingly, which bases are skipped in the case of reads that span introns. These so-called split reads are natively handled by all modules of deepTools 2.0.
+
+.. warning:: It is generally **not** recommended to activate the deepTools parameter ``--extendReads`` for RNA-seq data.
+
+ This is because there is no verified information on the fragment alignment outside the actual read sequence. A simple extension of a read over uncovered parts would probably be wrong for a lot of fragments! Activating the read extension also **deactivates** the utilization of the CIGAR.
+
+---------------------------------------------------------------------------
+
+How does computeMatrix handle overlapping genome regions?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+If the :ref:`bed` file supplied to :doc:`tools/computeMatrix` contains regions that overlap but they will just be taken as is. If you would like to prevent this, then clean the :ref:`BED` file before using ``computeMatrix``. There are several methods for modifying your BED file.
+
+Let's say your file looks like this::
+
+ $ cat testBed.bed
+ chr1 10 20 region1
+ chr1 7 15 region2
+ chr1 18 29 region3
+ chr1 35 40 region4
+ chr1 10 20 region1Duplicate
+
+
+Galaxy-based work around
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+To eliminate entries with *identical* genome coordinates, first use the tool "Count" and then filter out all entries that are present more than once.
+
+.. image:: ../images/Gal_FAQ_filteringDuplicates.png
+
+
+Command line-based work arounds
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+* if you just want to eliminate *identical* entries (here: region1 and region1Duplicate), use ``sort`` and ``uniq`` in the shell (note that the label of the identical regions is different - as ``uniq`` can only ignore fields at the beginning of a file, use ``rev`` to revert the sorted file, then ``uniq`` with ignoring the first field (which is now the name column) and then revert back::
+
+ $ sort -k1,1 -k2,2n testBed.bed | rev | uniq -f1 | rev
+ chr1 10 20 region1
+ chr1 7 15 region2
+ chr1 18 29 region3
+ chr1 35 40 region4
+
+* if you would like to *merge all overlapping regions* into one big one, use the ``mergeBed`` from the BEDtools suite:
+
+ * again, the BED file must be sorted first
+ * ``-n`` and ``-nms`` tell ``mergeBed`` to output the number of overlapping regions and the names of them
+ * in the resulting file, regions 1, 2 and 3 are merged
+ ::
+
+ $ sort -k1,1 -k2,2n testBed.bed | mergeBed -i stdin -n -nms
+ chr1 7 29 region2;region1;region1Duplicate;region3 4
+ chr1 35 40 region4 1
+
+* if you would like to *keep only regions that do not overlap* with any other region in the same BED file, use the same ``mergeBed`` routine but subsequently filter out those regions where several regions were merged.
+
+ * the ``awk`` command will check the last field of each line (``$NF``) and will print the original line (``$0``) only if the last field contained a number smaller than 2
+ ::
+
+ $ sort -k1,1 -k2,2n testBed.bed | mergeBed -i stdin -n -nms | awk '$NF < 2 {print $0}'
+ chr1 35 40 region4 1
+
+-----------------------------------------------------------------------------
+
+Why does the maximum value in the heatmap not equal the maximum value in the matrix?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Additional processing, such as outlier removal, is done on the matrix prior to plotting the heatmap. We've found this beneficial in most cases. You can override this by manually setting ``--zMax`` and/or ```--zMin``, respectively.
+
+-----------------------------------------------------------------------------
+
+The heatmap I generated looks very "coarse", I would like a much more fine-grained image.
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* decrease the *bin size* when generating the matrix using :doc:`computeMatrix `
+
+In Galaxy:
+ * go to "advanced options" --> "Length, in base pairs, of the non-overlapping :ref:`bin ` for averaging the score over the regions length" --> define a smaller value, e.g. 50 or 25 bp
+ * make sure that you used a sufficiently small :ref:`bin ` size when calculating the :ref:`bigWig` file, though (if generated with deepTools, you can check the option "bin size")
+
+-----------------------------------------------------------------------------
+
+How can I change the automatic labels of the clusters in a k-means clustered heatmap?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Each cluster is treated exactly the same way as different groups of regions. Therefore, you can use the same option to define the labels of the final heatmap:
+
+In Galaxy:
+ plotHeatmap --> "Advanced output options" --> "Labels for the regions plotted in the heatmap".
+
+If you indicated 2 clusters for k-means clustering, enter here: C1, C2, --> instead of the full default label ("cluster 1"), the heatmap will be labeled with the abbreviations.
+
+.. image:: ../images/Gal_FAQ_clusterLabeling.png
+
+In the command line, use the ``--regionsLabel`` option to define the customized names for the regions.
+
+------------------------------------------------------------------------------
+
+How can I manually specify several groups of regions (instead of clustering)?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Simply specify multiple BED files (e.g., genes.bed, exons.bed and introns.bed). This works both in Galaxy and on the command line.
+
+------------------------------------------------------------------------------
+
+What do I have to pay attention to when working with a draft version of a genome?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+If your genome isn't included in our standard dataset then you'll need the following:
+
+1. **Effective genome size** - this is mostly needed for :doc:`bamCoverage ` and :doc:`bamCompare `, see :ref:`below ` for details
+2. **Reference genome sequence in 2bit format** - this is needed for :doc:`computeGCBias `, see :ref:`2bit <2bit>` for details
+
+.. _effgenomesize:
+
+-------------------------------------------------------------------------------
+
+How can I use a temp directory other than /tmp?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+You can set your `$TMPDIR` environmental variable to a different directory.
+
+-------------------------------------------------------------------------------
+
+How do I calculate the effective genome size for an organism that's not in your list?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+At the moment we do not provide a tool for this purpose, so you'll have to find a solution outside of deepTools for the time being.
+
+The "real" effective genome size is the part of the genome that is *uniquely mappable*. This means that the value will depend on the genome properties (how many repetitive elements, quality of the assembly etc.) and the length of the sequenced reads as 100 million 36-bp-reads might cover less than 100 million 100-bp-reads.
+
+We currently have these options for you:
+
+1. Use an :ref:`GEM `
+2. Use :ref:`faCount ` (only if you let reads be aligned non-uniquely, too!)
+3. Use :ref:`bamCoverage `
+4. Use :ref:`genomeCoverageBed `
+
+.. _GEM:
+
+Use ``GEM``
+~~~~~~~~~~~~~~~~~~~~~~
+
+There is a tool that promises to calculate the mappability for any genome given the read length (k-mer length): `GEM-Mappability Calculator `_ . According to this reply `here `_, you can calculate the effective genome size after running this program by counting the numbers of "!" which stands for uniquely mappable regions.
+
+.. _faCount:
+
+Use ``faCount``
+~~~~~~~~~~~~~~~
+
+If you are using ``bowtie2``, which reports *multimappers* (i.e., *non-uniquely* mapped reads) as a default setting, you can use **faCount from UCSC tools** to report the total number of bases as well as the number of bases that are missing from the genome assembly indicated by 'N'. The effective genome size would then be the total number of base pairs minus the total number of 'N'.
+Here's an example output of ``faCount`` on *D. melanogaster* genome version dm3::
+
+ $ UCSCtools/faCount dm3.fa
+ #seq len A C G T N cpg
+ chr2L 23011544 6699731 4811687 4815192 6684734 200 926264
+ chr2LHet 368872 90881 58504 57899 90588 71000 10958
+ chr2R 21146708 6007371 4576037 4574750 5988450 100 917644
+ chr2RHet 3288761 828553 537840 529242 826306 566820 99227
+ chr3L 24543557 7113242 5153576 5141498 7135141 100 995078
+ chr3LHet 2555491 725986 473888 479000 737434 139183 89647
+ chr3R 27905053 7979156 5995211 5980227 7950459 0 1186894
+ chr3RHet 2517507 678829 447155 446597 691725 253201 84175
+ chr4 1351857 430227 238155 242039 441336 100 43274
+ chrU 10049037 2511952 1672330 1672987 2510979 1680789 335241
+ chrUextra 29004656 7732998 5109465 5084891 7614402 3462900 986216
+ chrX 22422827 6409325 4742952 4748415 6432035 90100 959534
+ chrXHet 204112 61961 40017 41813 60321 0 754
+ chrYHet 347038 74566 45769 47582 74889 104232 8441
+ chrM 19517 8152 2003 1479 7883 0 132
+ total 168736537 47352930 33904589 33863611 47246682 6368725 6650479
+
+In this example:
+Total no. bp = 168,736,537
+Total no. 'N' = 6,368,725
+
+.. warning:: This method only works if multimappers are randomly assigned to their possible locations (in such cases the effective genome size is simply the number of non-N bases).
+
+.. _mapp_bamCov:
+
+Use ``bamCoverage``
+~~~~~~~~~~~~~~~~~~~~
+
+If you have a sample where you expect the genome to be covered completely, e.g. from genome sequencing, a very trivial solution is to use :doc:`tools/bamCoverage` with a bin size of 1 bp and the ``--outFileFormat`` option set to 'bedgraph'. You can then count the number of non-Zero bins (bases) which will indicate the mappable genome size for this specific sample.
+
+.. _mapp_genomeCov:
+
+Use ``genomeCoverageBed``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+``genomeCoverageBed`` from the BEDtools suite can be used to calculate the number of bases in the genome for which 0 overlapping reads can be found.
+As described on the `BEDtools website `__ (go to genomeCov description), you need:
+
+* a file with the chromosome sizes of your sample's organism
+* a position-sorted BAM file
+
+.. code::
+
+ $ bedtools genomecov -ibam sortedBAMfile.bam -g genome.size
+
+---------------------------------------------------------------------------
+
+Where can I download the 2bit genome files required for ``computeGCBias``?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The 2bit files of most genomes can be found `here `__.
+Search for the .2bit ending. Otherwise, **fasta files can be converted to 2bit** using the UCSC program
+faToTwoBit (available for different platforms from `UCSC here `__).
+
+
diff --git a/deepTools/source/docs/content/help_faq_galaxy.rst b/deepTools/source/docs/content/help_faq_galaxy.rst
new file mode 100644
index 0000000000000000000000000000000000000000..9d3e2d058adfb33461bd4eb94123f7ab4c481f6c
--- /dev/null
+++ b/deepTools/source/docs/content/help_faq_galaxy.rst
@@ -0,0 +1,133 @@
+Galaxy-related FAQ
+===================
+
+.. contents::
+ :local:
+
+I've reached my quota - what can I do to save some space?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+1. make sure that all the data sets you deleted are **permanently** eliminated from our disks: go to the history option button and select "Purge deleted data sets", then hit the "refresh" button on top of your history panel
+2. download all data sets for which you've completed the analysis, then remove the data sets (click on the "x" and then **make sure they're purged** (see above)).
+
+----------------------------------------------------------------------
+
+Copying from one history to another doesn't work for me - the data set simply doesn't show up in the target history!
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Once you've copied a data set from one history to another, check two things:
+ * do you see the destination history in your history panel, i.e. does the title of the current history panel match the name of the destination history you selected in the main frame?
+ * hit the refresh button
+
+.. image:: ../images/Gal_historyReload.png
+
+----------------------------------------------------------------------
+
+How can I use a published workflow?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+You **must register** if you want to use the workflows within `deepTools Galaxy `__. ("User" --> "Register" - all you have to supply is an email address). Make sure to read the Terms of Use, though!
+
+You can find workflows that are public or specifically shared with you by another user via "Shared Data" --> "Published Workflows". Click on the triangle next to the workflow you're interested in and select "import".
+
+.. image:: ../images/GalHow_wf01.png
+
+
+A green box should appear, there you select "start using this workflow", which should lead you to your own workflow menu (that you can always access via the top menu "Workflow"). Here, you should now see a workflow labeled "imported: ....". If you want to use the workflow right away, click on the triangle and select "Run". The workflow should now be available within the Galaxy main data frame and should be waiting for your input.
+
+.. image:: ../images/GalHow_wf02.png
+
+----------------------------------------------------------------------
+
+I would like to use one of your workflows - not in the deepTools Galaxy, but in the local Galaxy instance provided by my institute. Is that possible?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Yes, it is possible. The only requirement is that your local Galaxy has a recent installation of deepTools.
+
+Go to the workflows, click on the ones you're interested in and go to "Download". This will save the workflows into .ga files on your computer. Now go to your local Galaxy installation and login. Go to the workflow menu and select "import workflow" (top right hand corner of the page). Click on "Browse" and select the saved workflow. If you have the same tool versions installed in your local Galaxy, these workflows should work right away.
+
+----------------------------------------------------------------------
+
+``plotProfile`` says that one option will only work if "computeMatrix was run with --missingDataAsZero". How can I find out whether I ran ``computeMatrix`` that way?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Galaxy keeps track of everything you do. To see which options you chose to generate a specific data set, simply click on the "info" button.
+
+.. image:: ../images/Gal_FAQ_info.png
+
+----------------------------------------------------------------------
+
+How can I have a look at the continuous read coverages from bigWig files? Which genome browser do you recommend?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+There are 2 popular genome browsers for visualizing continuous data: `UCSC `__ and `IGV `__.
+
+IGV (recommended)
+~~~~~~~~~~~~~~~~~~~
+
+We recommend downloading `IGV `__, which is free for academic use. IGV itself needs an up-to-date Java installation and a considerable amount of RAM. It's usage is rather intuitive and the display can be easily customized. In addition, you can download genome-wide annotation data that can be displayed together with your own data.
+
+To display data in IGV, do the following:
+
+1. Go to http://www.broadinstitute.org/igv/, register and download IGV
+2. Unpack the IGV archive and change to the extracted IGV folder
+3. Use the ``igv.bat`` (Windows), ``igv.sh`` (Linux) or ``igv.command`` (OSX) to start IGV (for more information please read the included ``readme.txt`` file or the IGV documentation).
+4. Choose the genome version of the file(s) you would like to visualize (e.g. dm3) **THIS IS THE MOST IMPORTANT STEP!** IGV will not detect the genome version automatically, i.e. if you select mm9 but your file is based on human data, it will still be displayed without an error message (but with the wrong positions, obviously!)
+5. Go to your deepTools Galaxy server (http://deeptools.ie-freiburg.mpg.de/) and navigate to your data set of choice
+6. Click on your data set so that you see its details like in the screenshot below (**Keep in mind that not all datasets can be visualized in IGV or UCSC.** We recommend to use :ref:`bigwig` or :ref:`bed` files for visualization.)
+
+.. image:: ../images/Gal_FAQ_IGV_dataset.png
+
+Now click on **"display with IGV local"** to visualize your data set in IGV that should already be running on your computer.
+
+.. Note:: "display with IGV Web current" can be used if you do not have an installed IGV. It will start an IGV web start version. **We do *not* recommend that option**.
+
+Here's a screenshot of a typical bigWig file display:
+
+.. image:: ../images/Gal_FAQ_IGV.png
+
+
+For more information, check out the `IGV documentation `__.
+
+
+UCSC
+~~~~~~~~
+
+There is a direct link from within deepTools Galaxy to stream a data set to UCSC. You can find it in the data set tiles: "display at UCSC", like here:
+
+.. image:: ../images/Gal_FAQ_UCSC_dataset.png
+
+
+Click on "main" and the UCSC browser should open within a new window, displaying the data set that you chose.
+The default setting for bigWig files is the "dense" display that looks like a heatmap.
+
+.. image:: ../images/Gal_FAQ_UCSC01.png
+
+
+If you would like to display the continuous profile in a "valley-mountain" fashion like the one shown in the IGV screenshot, go to the drop-down menu underneath your custom track and choose "full".
+
+UCSC has large amounts of public data that you can display which you can find by scrolling down the page, beyond your custom track entry. For more information on how to use the UCSC Genome Browser, go `here `__.
+
+**Known issues with UCSC**
+
+* **chromosome naming**: UCSC expects chromosome names to be indicated in the format "chr"Number, e.g. chr1. If you mapped your reads to a non-UCSC-standard genome, chances are that chromosomes are labeled just with their number. bigWig files generated from these BAM files will not be recognized by UCSC, i.e. you will see the data set name, but no signal.
+* **no upload of bigWig files from your hard drive**: to minimize the computational strains, UCSC relies on streaming bigWig files (i.e. there's no need to load the entire file at once, the browser will always just load the data for the specific region a user is looking at).
+
+----------------------------------------------------------------------
+
+What's the best way to integrate the deepTools results with other downstream analyses (outside of Galaxy)?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+You can **save all the data tables** underlying every image produced by deepTools, i.e. if you would like to plot the average profiles in a different way, you could download the corresponding data (after ticking the relevant option under "advanced output options") and import them into R, Excel, GraphPadPrism etc.
+
+The descriptions of the tools within Galaxy will also contain details on how to save the data and what sort of format to expect.
+
+----------------------------------------------------------------------
+
+How can I determine basic parameters of a BAM file, such as the number of reads, read length, duplication rate and average DNA fragment length?
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+If you downloaded the :ref:`BAM` file from a public repository, chances are that those characteristics are in fact noted there.
+
+If that's not the case, we recommend to have a look at the tool `FastQC `_, which will return all of the above points (except the fragment size).
+The fragment size distribution can be obtained using the deepTools' :doc:`tools/bamPEFragmentSize` (since deepTools 2.0).
+
diff --git a/deepTools/source/docs/content/help_galaxy_dataup.rst b/deepTools/source/docs/content/help_galaxy_dataup.rst
new file mode 100644
index 0000000000000000000000000000000000000000..e942d0d042abbb93eb3dac01edd5cc546312a586
--- /dev/null
+++ b/deepTools/source/docs/content/help_galaxy_dataup.rst
@@ -0,0 +1,108 @@
+Data import into Galaxy
+-------------------------
+
+There are three main ways to populate your Galaxy history with data
+files plus an additional one for sharing data within Galaxy.
+
+.. contents::
+ :local:
+
+
+Upload files from your computer
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The data upload of files **smaller than 2 GB** that lie on your computer is fairly straight-forward: click on the category "Get data" and choose the tool "Upload file".
+Then select the file via the "Browse" button.
+
+.. image:: ../images/Gal_DataUpload.png
+
+For files **greater than 2GB**, there's the option to upload via an FTP server. If your data is available via an URL that links to an FTP server, you can simply
+paste the URL in the empty text box.
+
+If you do not have access to an FTP server, you can directly upload to
+our Galaxy's FTP.
+
+1. Register with deeptools.ie-freiburg.mpg.de (via “User” ⟶ “register”; registration requires an email address and is free of charge)
+2. You will also need an FTP client, e.g. `filezilla `__.
+3. Then login to the **FTP client** using your **deepTools Galaxy user name and password** (host: deeptools.ie-freiburg.mpg.de). Down below you see a screenshot of what that looks like with filezilla.
+4. Copy the file you wish to upload to the remote site (in filezilla, you can simply drag the file to the window on the right hand side)
+5. Go back to `deepTools Galaxy `__.
+6. Click on the tool "Upload file" (⟶ "Files uploaded via FTP") - here, the files you just copied over via filezilla should appear. Select the files you want and hit “execute”. They will be moved from the FTP server to your history (i.e. they will be deleted from the FTP once the upload was successful).
+
+.. image:: ../images/Gal_filezilla.png
+
+Import data sets from the Galaxy data library
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+If you would like to play around with sample data, you can import files
+that we have saved within the general data storage of the deepTools
+Galaxy server. Everyone can import them into his or her own history,
+they will not contribute to the user's disk quota.
+
+You can reach the data library via "Shared Data" in the top menu, then
+select "Data Libraries".
+
+Within the Data Library you will find a folder called "Sample Data" that
+contains data that we downloaded from the `Roadmap
+project `__ and
+`UCSC `__
+More precisely, we donwloaded the [FASTQ][] files of various ChIP-seq samples and the corresponding input and mapped the reads to the human reference genome (version hg19) to obtain the [BAM][] files you see.
+In addition, you will find bigWig files created using ``bamCoverage`` and some annotation files in BED format as well as RNA-seq data.
+
+.. note:: To keep the file size smallish, all files contain data for chromosome 19 and chromosome X only!
+
+.. image:: ../images/Gal_DataLib.png
+
+Download annotation files from public data bases
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+In many cases you will want to query your sequencing data results for
+known genome annotation, such as genes, exons, transcription start sites
+etc. These information can be obtained via the two main sources of
+genome annotation, `UCSC `__ and `BioMart `__.
+
+.. warning:: UCSC and BioMart cater to different ways of genome annotation, i.e. genes defined in UCSC might not correspond to the same regions in a gene file downloaded from BioMart. (For a brief overview over the issues of genome annotation, you can check out `Wikipedia `_, if you always wanted to know much more about those issues, `this `_ might be a good start.)
+
+You can access the data stored at UCSC or BioMart conveniently through our Galaxy instance which will import the resulting files into your history. Just go to **"Get data"** ⟶ "UCSC" or "BioMart".
+
+The majority of annotation files will probably be in [BED][] format, however, you can also find other data sets.
+UCSC, for example, offers a wide range of data that you can browse via the "group" and "track" menus (for example, you could download the GC content of the genome as a signal file from UCSC via the "group" menu ("Mapping and Sequencing Tracks").
+
+.. warning:: The download through this interface is limited to 100,000 lines per file which might not be sufficient for some mammalian data sets.
+
+Here's a screenshot from downloading a BED-file of all RefSeq genes defined for the human genome (version hg19):
+
+.. image:: ../images/Gal_UCSC.png
+
+And here's how you would do it for the BioMart approach:
+
+.. image:: ../images/Gal_biomart.png
+
+.. tip:: Per default, **BioMart will not output a BED file** like UCSC does. It is therefore important that you make sure you get all the information you need (most likely: chromosome, gene start, gene end, ID, strand) via the "Attributes" section. You can click on the "Results" button at any time to check the format of the table that will be sent to Galaxy (Note that the strand information will be decoded as 1 for "forward" or "plus" strand and -1 for "reverse" or "minus" strand).
+
+.. warning:: Be aware, that BED files from UCSC will have chromosomes labelled with “chr” while ENSEMBL usually returns just the number – this might lead to incompatibilities, i.e. when working with annotations from UCSC and ENSEMBL, you need to make sure to use the same naming!
+
+Copy data sets between histories
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+If you have registered with deepTools Galaxy you can have more than one history.
+
+In order to minimize the disk space you're occupying we strongly suggest to **copy** data sets between histories when you're using the same data set in different histories.
+
+.. note:: Copying data sets is only possible for registered users.
+
+.. image:: ../images/Gal_copy.png
+
+Copying can easily be done via the History panel's ``option`` button ⟶ "Copy dataset". In the main frame, you should now be able to select the history you would like to copy from on the left hand side and the target history on the right hand side.
+
+**More help**
+
+.. hint:: If you encounter a failing data set (marked in red), please **send a bug report** via the Galaxy bug report button and we will get in touch if you indicate your email address.
+
++--------------------------------------------------------------------------------+-----------------------------------------------------------------+
+| `http://wiki.galaxyproject.org/Learn `__ | Help for Galaxy usage in general |
++--------------------------------------------------------------------------------+-----------------------------------------------------------------+
+| `deepTools Galaxy FAQs `__ | Frequently encountered issues with our specific Galaxy instance |
++--------------------------------------------------------------------------------+-----------------------------------------------------------------+
+| `Biostars `__ | For issues not addressed in the FAQs |
++--------------------------------------------------------------------------------+-----------------------------------------------------------------+
diff --git a/deepTools/source/docs/content/help_galaxy_deeptools.rst b/deepTools/source/docs/content/help_galaxy_deeptools.rst
new file mode 100644
index 0000000000000000000000000000000000000000..b80fcc9be6722137847a90552932b6f6d94c9fab
--- /dev/null
+++ b/deepTools/source/docs/content/help_galaxy_deeptools.rst
@@ -0,0 +1,132 @@
+Which tools can I find in the deepTools Galaxy?
+-----------------------------------------------
+
+As mentioned before, each Galaxy installation can be tuned to the
+individual interests.
+Our goal is to provide a Galaxy that enables you to **quality check, process and normalize and subsequently visualize your data obtained by high-throughput DNA sequencing**.
+
+.. tip:: If you do not know the difference between a BAM and a BED file, that's fine. You can read up on them in our :doc:`help_glossary`.
+
+.. tip:: For more specific help, check our :doc:`help_faq_galaxy` and the :doc:`example_step_by_step`.
+
+We provide the following kinds of tools:
+
+.. contents::
+ :local:
+
+deepTools
+^^^^^^^^^^
+
+The most important category is called **"deepTools"** that contains all the main tools we have developed.
+
+Tools for BAM and bigWig file processing
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
++------------------------+--------------------------------------------------------------------------------+
+| ``multiBamSummary`` | get read counts for the binned genome or user-specified regions |
++------------------------+--------------------------------------------------------------------------------+
+| ``multiBigwigSummary`` | calculate score summaries for the binned genome or user-specified regions |
++------------------------+--------------------------------------------------------------------------------+
+| ``correctGCBias`` | obtain a BAM file with reads distributed according to the genome's GC content |
++------------------------+--------------------------------------------------------------------------------+
+| ``bamCoverage`` | obtain the normalized read coverage of a single BAM file |
++------------------------+--------------------------------------------------------------------------------+
+| ``bamCompare`` | normalize 2 BAM files to each other (e.g. log2ratio, difference) |
++------------------------+--------------------------------------------------------------------------------+
+| ``bigwigCompare`` | normalize the scores of two bigWig files to each other (e.g., ratios) |
++------------------------+--------------------------------------------------------------------------------+
+| ``bigwigAverage`` | average the scores of multiple bigWig files |
++------------------------+--------------------------------------------------------------------------------+
+| ``computeMatrix`` | compute the values needed for heatmaps and summary plots |
++------------------------+--------------------------------------------------------------------------------+
+
+Tools for QC of NGS data
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
++-----------------------+-------------------------------------------------------------------------------------------------------+
+| ``plotCorrelation`` | calculate and visualize the pairwise Spearman or Pearson correlation of read counts (or other scores) |
++-----------------------+-------------------------------------------------------------------------------------------------------+
+| ``plotPCA`` | perform PCA and visualize the results |
++-----------------------+-------------------------------------------------------------------------------------------------------+
+| ``plotFingerprint`` | assess the ChIP enrichment strength |
++-----------------------+-------------------------------------------------------------------------------------------------------+
+| ``bamPEFragmentSize`` | obtain the average fragment length for paired-end samples |
++-----------------------+-------------------------------------------------------------------------------------------------------+
+| ``computeGCBias`` | assess the GC bias by calculating the expected and observed GC distribution of aligned reads |
++-----------------------+-------------------------------------------------------------------------------------------------------+
+| ``plotCoverage`` | obtain the normalized read coverage of a single BAM file |
++-----------------------+-------------------------------------------------------------------------------------------------------+
+
+Heatmaps and summary plots
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
++-------------------+-------------------------------------------------------------------------------------------+
+| ``plotHeatmap`` | visualize read counts or other scores in heatmaps with one row per genomic region |
++-------------------+-------------------------------------------------------------------------------------------+
+| ``plotProfile`` | visualize read counts or other scores using average profiles (e.g., meta-gene profiles) |
++-------------------+-------------------------------------------------------------------------------------------+
+
+For each tool, you can find example usages and tips within Galaxy once you select the tool.
+
+In addition, you may want to check our pages about :doc:`example_usage`, particularly :doc:`example_step_by_step`.
+
+Working with text files and tables
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+In addition to deepTools that were specifically developed for the handling of NGS data, we have incorporated several standard Galaxy tools that enable you to manipulate tab-separated files such as gene lists, peak lists, data matrices etc.
+
+There are 3 main categories;
+
+.. image:: ../images/Gal_textfiles.png
+
+Text manipulation
+~~~~~~~~~~~~~~~~~~
+
+Unlike Excel, where you can easily interact with your text and tables via the mouse, data manipulations within Galaxy are strictly based on commands.
+
+If you feel like you would like to do something to certain *columns* of a data set, go through the tools of this category!
+
+Example actions are:
+* adding columns
+* extracting columns
+* pasting two files side by side
+* selecting random lines
+* etc.
+
+A very useful tool of this category is called ``Trim``: if you need to **remove some characters from a column**, this tool's for you! (for example, sometimes you need to adjust the chromosome naming between two files from different source - using ``Trim``, you can remove the "chr" in front of the chromosome name)
+
+Filter and Sort
+~~~~~~~~~~~~~~~~
+
+In addition to the common sorting and filtering, there's the very useful tool to ``select lines that match an expression``.
+For example, using the expression ``c1=='chrM'`` will select all rows from a BED file with regions located on the mitochondrial chromosome.
+
+.. image:: ../images/Gal_filter.png
+
+Join, Subtract, Group
+~~~~~~~~~~~~~~~~~~~~~
+
+The tools of this category are very useful if you have several data sets that you would like to work with, e.g. by comparing them.
+
+.. image:: ../images/Gal_join.png
+
+Basic arithmetics for tables
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+We offer some very basic mathematical operations on values stored with tables.
+The ``Summary Statistics`` can be used to calculate the sum, mean, standard deviation and percentiles for a set of numbers, e.g. for values stored in a specific column.
+
+.. image:: ../images/Gal_statistics.png
+
+
+**More help**
+
+.. hint:: If you encounter a failing data set (marked in red), please **send a bug report** via the Galaxy bug report button and we will get in touch if you indicate your email address.
+
++-------------------------------------------------------------------------------+-----------------------------------------------------------------+
+| `http://wiki.galaxyproject.org/Learn `_ | Help for Galaxy usage in general |
++-------------------------------------------------------------------------------+-----------------------------------------------------------------+
+| `deepTools Galaxy FAQs `_ | Frequently encountered issues with our specific Galaxy instance |
++-------------------------------------------------------------------------------+-----------------------------------------------------------------+
+| `Biostars `__ | For issues not addressed in the FAQs |
++-------------------------------------------------------------------------------+-----------------------------------------------------------------+
diff --git a/deepTools/source/docs/content/help_galaxy_intro.rst b/deepTools/source/docs/content/help_galaxy_intro.rst
new file mode 100644
index 0000000000000000000000000000000000000000..7ba1b913d1c24d03435f30e32870a522fdb84a6e
--- /dev/null
+++ b/deepTools/source/docs/content/help_galaxy_intro.rst
@@ -0,0 +1,112 @@
+Using deepTools within Galaxy
+================================
+
+`Galaxy `_ is a tremendously useful platform developed by the Galaxy Team at Penn State and the Emory University. This platform is meant to offer access to a large variety of bioinformatics tools that can be used without computer programming experiences. That means, that the basic features of Galaxy will apply to every tool, i.e. every tool provided within a Galaxy framework will look very similar and will follow the concepts of Galaxy.
+
+Our publicly available deepTools Galaxy instance can be found here:
+`deeptools.ie-freiburg.mpg.de `_.
+This server also contains some additional tools that will enable users to analyse and visualize data from high-throughput sequencing experiments, starting from aligned reads.
+
+**Table of content**
+
+.. contents::
+ :local:
+
+.. toctree::
+ :maxdepth: 2
+
+ help_galaxy_dataup
+
+.. toctree::
+ :maxdepth: 2
+
+ help_galaxy_deeptools
+
+
+
+Basic features of Galaxy
+-------------------------
+
+Galaxy is a web-based platform for data intensive, bioinformatics-dependent research and it is being developed by Penn State and John Hopkins University. The original Galaxy can be found `here `_.
+
+Since it is impossible to meet all bioinformatics needs -- that can range from evolutionary analysis to data from mass spectrometry to high-throughput DNA sequencing (and way beyond) -- with one single web server, many institutes have installed their own versions of the Galaxy platform tuned to their specific needs.
+
+Our `deepTools Galaxy `__ is such a specialized server dedicated to the analysis of high-throughput DNA sequencing data. The overall makeup of this web server, however, is the same as for any other Galaxy installation, so if you've used Galaxy before, you will learn to use deepTools in no time!
+
+The start site
+^^^^^^^^^^^^^^^
+
+Here is a screenshot of what the start site will approximately look like:
+
+.. image:: ../images/Gal_startsite.png
+
+The start site contains 4 main features:
+
++-------------------+----------------------------------------------------------------------------------------------------------------------------+
+| **Top menu** | Your gateway away from the actual analysis part into other sections of Galaxy, e.g. **workflows** and **shared data**. |
++-------------------+----------------------------------------------------------------------------------------------------------------------------+
+| **Tool panel** | *What can be done?* Find all tools installed in this Galaxy instance |
++-------------------+----------------------------------------------------------------------------------------------------------------------------+
+| **Main frame** | *What am I doing?* This is your main **working space** where input will be required from you once you've selected a tool. |
++-------------------+----------------------------------------------------------------------------------------------------------------------------+
+| **History panel** | *What did I do?* The history is like a **log book**: everything you ever did is recorded here. |
++-------------------+----------------------------------------------------------------------------------------------------------------------------+
+
+For those visual learners, here's an annotated screenshot:
+
+.. image:: ../images/Gal_startsite_with_comments.png
+
+Details
+^^^^^^^^^^^^^^^
+
+In the default state of the tool panel you see the **tool categories**, e.g. "Get Data". If you click on them, you will see the **individual tools** belonging to each category, e.g. "Upload File from your computer", "UCSC Main table browser" and "Biomart central server" in case you clicked on "Get Data". To use a tool such as "Upload File from your computer", just click on it.
+
+The **tool *search* panel** is extremely useful as it allows you to enter a key word (e.g. "bam") that will lead to all the tools mentioning the key word in the tool name.
+
+Once you've uploaded any kind of data, you will find the history on the
+right hand side filling up with green tiles.
+Each tile corresponds to one data set that you either uploaded or created.
+The data sets can be images, raw sequencing files, text files, tables - virtually anything.
+The content of a data set *cannot* be modified - every time you want to change something *within* a data file (e.g. you would like to sort the values or add a line or cut a column), you will have to use a Galaxy tool that will lead to a *new* data set being produced.
+This behaviour is often confusing for Galaxy novices (as histories tend to accumulate data sets very quickly), but it is necessary to enforce the strict policy of documenting *every modification* to a given data set.
+Eventhough your history might be full of data sets with strange names, you will always be able to track back the source and evolution of each file.
+Also, every data set can be downloaded to your computer individually.
+Alternatively, you can *download* an entire history or *share* the history with another user.
+
+Have a look at the following screenshot to get a feeling for how many information Galaxy keeps for you (which makes it very feasible to reproduce any analysis):
+
+.. image:: ../images/Gal_screenshot_dataSet.png
+
+Each data set can have 4 different states that are intuitively color-coded:
+
+.. image:: ../images/Gal_screenshot_dataSetStates.png
+
+Handling failed files
+^^^^^^^^^^^^^^^^^^^^^^
+
+ If you encounter a failed file after you've run a tool, please do the following steps (**in this order**):
+
+ 1. click on the center button on the lower left corner of the failed data set ``(i)``: did you chose the **correct data files**?
+ 2. if you're sure that you chose the correct files, hit the ``re-run button`` (blue arrow in the lower left corner) - check again whether your files had the **correct file format**. If you suspect that the format might be incorrectly assigned (e.g. a file that should be a BED file is labelled as a tabular file), click the ``edit button`` (the pencil) of the input data file - there you can change the corresponding attributes
+ 3. if you've checked your input data and the error is persisting, click on the ``green bug`` (lower left corner of the failed data set) and send the **bug report** to us. You do not need to indicate a valid email-address unless you would like us to get in touch with you once the issue is solved.
+
+Workflows
+^^^^^^^^^^
+
+Workflows are Galaxy's equivalent of protocols.
+This is a very useful feature as it allows users to *share their protocols and bioinformatic analyses* in a very easy and transparent way.
+This is the graphical representation of a Galaxy workflow that can easily be modified via drag'n'drop within the workflows manual (you must be registered with deepTools Galaxy to be able to generate your own workflows or edit published ones).
+
+.. image:: ../images/Gal_workflow.png
+
+**More help**
+
+.. hint:: If you encounter a failing data set (marked in red), please **send a bug report** via the Galaxy bug report button and we will get in touch if you indicate your email address.
+
++-------------------------------------------------------------------------------+-----------------------------------------------------------------+
+| `http://wiki.galaxyproject.org/Learn `_ | Help for Galaxy usage in general |
++-------------------------------------------------------------------------------+-----------------------------------------------------------------+
+| `deepTools Galaxy FAQs `_ | Frequently encountered issues with our specific Galaxy instance |
++-------------------------------------------------------------------------------+-----------------------------------------------------------------+
+| `Biostars `__ | For issues not addressed in the FAQs |
++-------------------------------------------------------------------------------+-----------------------------------------------------------------+
diff --git a/deepTools/source/docs/content/help_glossary.rst b/deepTools/source/docs/content/help_glossary.rst
new file mode 100644
index 0000000000000000000000000000000000000000..c5de0cf48ecbfedb83a404f9a419f7fb4e171fc4
--- /dev/null
+++ b/deepTools/source/docs/content/help_glossary.rst
@@ -0,0 +1,252 @@
+Glossary of NGS terms
+=====================
+
+Like most specialized fields, next-generation sequencing has inspired many an acronyms.
+We are trying to keep track of those :ref:`abbreviations` that we heavily use.
+Do make us aware if something is unclear by opening an issue on `github `__
+
+
+.. contents::
+ :local:
+
+.. _abbreviations:
+
+Abbreviations
+---------------
+
+Reference genomes are usually referred to by their abbreviations, such as:
+
+* hg19 = human genome, version 19
+* mm9 = *Mus musculus* genome, version 9
+* dm3 = *Drosophila melanogaster*, version 3
+* ce10 = *Caenorhabditis elegans*, version 10
+
+For a more comprehensive list of available reference genomes and their abbreviations,
+see the `UCSC data base `__.
+
++---------------+------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
+| Acronym | full phrase | Synonyms/Explanation |
++===============+==========================================+===============================================================================================================================================+
+| -seq| -sequencing |indicates that an experiment was completed by DNA sequencing using NGS |
++---------------+------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
+| ChIP-seq | chromatin immunoprecipitation sequencing | NGS technique for detecting transcription factor binding sites and histone modifications (see entry *Input* for more information) |
++---------------+------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
+| DNase | deoxyribonuclease I | DNase I digestion is used to determine active ("open") chromatin regions |
++---------------+------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
+| HTS | high-throughput sequencing | next-generation sequencing, massive parallel short read sequencing, deep sequencing |
++---------------+------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
+| MNase | micrococcal nuclease | MNase digestion is used to determine sites with nucleosomes |
++---------------+------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
+| NGS | next-generation sequencing | high-throughput (DNA) sequencing, massive parallel short read sequencing, deep sequencing |
++---------------+------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
+| RPGC | reads per genomic content | normalize reads to 1x sequencing depth, sequencing depth is defined as: (mapped reads x fragment length) / effective genome size |
++---------------+------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
+| RPKM | reads per kilobase per million reads | normalize read numbers: RPKM (per bin) = reads per bin / ( mapped reads (in millions) x bin length (kb)) |
++---------------+------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------+
+
+For a review of popular \*-seq applications, see `Zentner and Henikoff `__.
+
+.. _terminology:
+
+NGS and generic terminology
+---------------------------
+The following are terms that may be new to some:
+
+.. _bin:
+
+bin
+^^^
+
+* synonyms: window, region
+* A 'bin' is a subset of a larger grouping. Many calculations calculation are performed by first dividing the genome into small regions (bins), on which the calculations are actually performed.
+
+.. _input:
+
+Input
+^^^^^^^^
+
+* Control experiment typically done for ChIP-seq experiments
+* While ChIP-seq relies on antibodies to enrich for DNA fragments bound to a certain protein, the input sample should be processed exactly the same way, excluding the antibody. This allows one to account for biases introduced by sample handling and the general chromatin structure of the cells
+
+.. _read:
+
+read
+^^^^^^^^
+
+* synonym: tag
+* This term refers to the piece of DNA that is sequenced ("read") by the sequencers. We try to differentiate between "read" and "DNA fragment" as the fragments that are put into the sequencer tend to be in the range of 200-1000 bases, of which only the first 50 to 300 bases are typically sequenced. Most of the deepTools will not only take these reads into account, but also extend them to match the original DNA fragment size. (The original size will either be given by you or, if you used paired-end sequencing, be calculated from the distance between the two read mates).
+
+.. _file formats:
+
+File Formats
+-------------------
+
+Data obtained from next-generation sequencing data must be processed several times.
+Most of the processing steps are aimed at extracting only that information
+needed for a specific down-stream analysis, with redundant entries often discarded.
+Therefore, **specific data formats are often associated with different steps of a data processing pipeline**.
+
+Here, we just want to give very brief key descriptions of the file, for elaborate information we will link to external websites.
+Be aware, that the file name sorting here is alphabetical, not according to their usage within an analysis pipeline that is depicted here:
+
+.. image:: ../images/flowChart_FileFormats.png
+
+Follow the links for more information on the different tool collections mentioned in the figure:
+
+`samtools `__ |
+`UCSCtools `__ |
+`BEDtools `__ |
+
+.. _2bit:
+
+2bit
+^^^^^
+
+* compressed, binary version of genome sequences that are often stored in :ref:`FASTA`
+* most genomes in 2bit format can be found `at UCSC `__
+* :ref:`FASTA` files can be converted to 2bit using the UCSC programm *faToTwoBit*, which is available for different platforms at `UCSC `__
+* more information can be found `here `__
+
+.. _BAM:
+
+BAM
+^^^^
+
+* typical file extension: ``.bam``
+* *binary* file format (complement to :ref:`SAM`)
+* contains information about sequenced reads (typically) *after alignment* to a reference genome
+* each line = 1 mapped read, with information about:
+ * its mapping quality (how likelihood that the reported alignment is correct)
+ * its sequencing quality (the probability that each base is correct)
+ * its sequence
+ * its location in the genome
+ * etc.
+* highly recommended format for storing data
+* to make a BAM file human-readable, one can, for example, use the program *samtools view*
+* for more information, see below for the definition of :ref:`SAM` files
+
+.. _bed:
+
+BED
+^^^
+
+* typical file extension: ``.bed``
+* text file
+* used for genomic intervals, e.g. genes, peak regions etc.
+* the format can be found at `UCSC `__
+* for deepTools, the first 3 columns are important: chromosome, start position of the region, end position of the genome
+* do not confuse it with the :ref:`bedgraph` format (although they are related)
+* example lines from a BED file of mouse genes (note that the start position is 0-based, the end-position 1-based, following UCSC conventions for BED files)::
+
+ chr1 3204562 3661579 NM_001011874 Xkr4 -
+ chr1 4481008 4486494 NM_011441 Sox17 -
+ chr1 4763278 4775807 NM_001177658 Mrpl15 -
+ chr1 4797973 4836816 NM_008866 Lypla1 +
+
+.. _bedGraph:
+
+bedGraph
+^^^^^^^^
+
+* typical file extension: ``.bg``, ``.bedGraph``
+* text file
+* similar to BED file (not the same!), it can *only* contain 4 columns and the 4th column *must* be a score
+* again, read the `UCSC description `__ for more details
+* 4 example lines from a bedGraph file (like BED files following the UCSC convention, the start position is 0-based, the end-position 1-based in bedGraph files):
+
+::
+
+ chr1 10 20 1.5
+ chr1 20 30 1.7
+ chr1 30 40 2.0
+ chr1 40 50 1.8
+
+.. _bigWig:
+
+bigWig
+^^^^^^
+
+* typical file extension: ``.bw``, ``.bigwig``
+* *binary* version of a :ref:`bedgraph` or ``wig`` file
+* contains coordinates for an interval and an associated score
+* the score can be anything, e.g. an average read coverage
+* `UCSC description `__ for more details
+
+.. _FASTA:
+
+FASTA
+^^^^^^
+
+* typical file extension: ``.fasta``
+* text file, often gzipped (``.fasta.gz``)
+* very simple format for **DNA/RNA** or **protein** sequences, this can be anything from small pieces of DNA or proteins to an entire genome (most likely, you will get the genome sequence of your organism of interest in fasta format)
+* see the :ref:`2bit` file format entry for a compressed alternative
+* example from `wikipedia `__ showing exactly one sequence:
+
+::
+
+ >gi|5524211|gb|AAD44166.1| cytochrome b [Elephas maximus maximus]
+ LCLYTHIGRNIYYGSYLYSETWNTGIMLLLITMATAFMGYVLPWGQMSFWGATVITNLFSAIPYIGTNLV
+ EWIWGGFSVDKATLNRFFAFHFILPFTMVALAGVHLTFLHETGSNNPLGLTSDSDKIPFHPYYTIKDFLG
+ LLILILLLLLLALLSPDMLGDPDNHMPADPLNTPLHIKPEWYFLFAYAILRSVPNKLGGVLALFLSIVIL
+ GLMPFLHTSKHRSMMLRPLSQALFWTLTMDLLTLTWIGSQPVEYPYTIIGQMASILYFSIILAFLPIAGX
+ IENY
+
+.. _FASTQ:
+
+FASTQ
+^^^^^
+
+* typical file extension: ``.fastq``, ``.fq``
+* text file, often gzipped (--> ``.fastq.gz``)
+* contains raw read information -- 4 lines per read:
+ * read ID
+ * base calls
+ * additional information or empty line
+ * sequencing quality measures - 1 per base call
+* note that there is no information about where in the genome the read originated from
+* example from the `wikipedia page `__, which contains further information::
+
+ @read001 # read ID
+ GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT # read sequence
+ + # usually empty line
+ !''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65 # ASCII-encoded quality scores
+
+* if you need to find out what type of ASCII-encoding your .fastq file contains, you can simply run `FastQC `__ -- its summery file will tell you
+
+.. _SAM:
+
+SAM
+^^^
+
+* typical file extension: ``.sam``
+* usually the result of an alignment of sequenced reads to a reference genome
+* contains a short header section (entries are marked by @ signs) and an alignment section where each line corresponds to a single read (thus, there can be millions of these lines)
+
+.. image:: ../images/glossary_sam.png
+
+SAM header section
+~~~~~~~~~~~~~~~~~~~
+
+ * tab-delimited lines, beginning with @, followed by tag\:value pairs
+ * *tag* = two-letter string that defines the content and the format of *value*
+
+SAM alignment section
+~~~~~~~~~~~~~~~~~~~~~~
+
+ * each line contains information about its mapping quality, its sequence, its location in the genome etc.
+ ::
+
+ r001 163 chr1 7 30 8M2I4M1D3M = 37 39 TTAGATAAAGGATACTG *
+ r002 0 chr1 9 30 3S6M1P1I4M * 0 0 AAAAGATAAGGATA *
+
+ * the **flag in the second field** contains the answer to several yes/no assessments that are encoded in a single number
+ * for more details on the flag, see `this thorough explanation `__ or `this more technical explanation `__
+ * the **CIGAR string in the 6th field** represents the types of operations that were needed in order to align the read to the specific genome location:
+
+ * insertion
+ * deletion (small deletions denoted with `D`, bigger deletions, e.g., for spliced reads, denoted with `N`)
+ * clipping (deletion at the ends of a read)
+
+.. warning:: Although the SAM/BAM format is rather meticulously defined and documented, whether an alignment program will produce a SAM/BAM file that adheres to these principles is completely up to the programmer. The mapping score, CIGAR string, and particularly, **all optional flags** (fields >11) are often **very differently defined depending on the program**. If you plan on filtering your data based on any of these criteria, make sure you know exactly how these entries were calculated and set!
+
diff --git a/deepTools/source/docs/content/installation.rst b/deepTools/source/docs/content/installation.rst
new file mode 100644
index 0000000000000000000000000000000000000000..fe925e11fa479a7859b9acd8d5d43abaae50a5d6
--- /dev/null
+++ b/deepTools/source/docs/content/installation.rst
@@ -0,0 +1,86 @@
+Installation
+=============
+
+Remember -- deepTools are available for **command line usage** as well as for
+**integration into Galaxy servers** !
+
+.. contents::
+ :local:
+
+Command line installation using ``conda``
+-----------------------------------------
+
+The recommended way to install deepTools (including its requirements) is via `miniconda `_ or `anaconda `_.
+
+.. code:: bash
+
+ $ conda install -c conda-forge -c bioconda deeptools
+
+Note that for ARM architecture (e.g. M1 on OSX) you could go via the pip installation (see below), or install via the osx-64 env:
+
+.. code:: bash
+
+ $ CONDA_SUBDIR=osx-64 conda create -c conda-forge -c bioconda -n deeptools deeptools
+
+
+Command line installation using ``pip``
+---------------------------------------
+
+deepTools can also be installed using `pip `_.
+You can either install the latest release from `pypi `_:
+
+.. code:: bash
+
+ $ pip install deeptools
+
+or a specific version with:
+
+.. code:: bash
+
+ $ pip install deeptools==3.5.3
+
+In case you would like to install an unreleased or development version, deepTools can also be installed from the repository:
+
+.. code:: bash
+
+ $ git clone https://github.com/deeptools/deepTools.git
+ $ cd deepTools
+ $ pip install .
+
+Galaxy installation
+--------------------
+
+deepTools can be easily integrated into a local `Galaxy `_.
+All wrappers and dependencies are available in the `Galaxy Tool
+Shed `_.
+
+Installation via Galaxy API (recommended)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+First generate an `API Key `_
+for your admin user and run the the installation script:
+::
+
+ $ python ./scripts/api/install_tool_shed_repositories.py \
+ --api YOUR_API_KEY -l http://localhost/ \
+ --url https://toolshed.g2.bx.psu.edu/ \
+ -o bgruening -r --name suite_deeptools \
+ --tool-deps --repository-deps --panel-section-name deepTools
+
+The ``-r`` argument specifies the version of deepTools. You can get the
+latest revision number from the test tool shed or with the following
+command:
+::
+
+ $ hg identify https://toolshed.g2.bx.psu.edu/repos/bgruening/suite_deeptools
+
+You can watch the installation status under: Top Panel --> Admin --> Manage
+installed tool shed repositories
+
+Installation via web browser
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+- go to the `admin page `_
+- select *Search and browse tool sheds*
+- Galaxy tool shed --> Sequence Analysis --> deeptools
+- install deeptools
diff --git a/deepTools/source/docs/content/list_of_tools.rst b/deepTools/source/docs/content/list_of_tools.rst
new file mode 100644
index 0000000000000000000000000000000000000000..2191f3c23572ea1cac63fa23ae03d376ce8acdb6
--- /dev/null
+++ b/deepTools/source/docs/content/list_of_tools.rst
@@ -0,0 +1,166 @@
+The tools
+=========
+
+.. contents::
+ :local:
+
++-------------------------------------+------------------+-------------------------------------+--------------------------------------------+-----------------------------------------------------------------------------------+
+| tool | type | input files | main output file(s) | application |
++=====================================+==================+=====================================+============================================+===================================================================================+
+|:doc:`tools/multiBamSummary` | data integration | 2 or more BAM | interval-based table of values | perform cross-sample analyses of read counts --> plotCorrelation, plotPCA |
++-------------------------------------+------------------+-------------------------------------+--------------------------------------------+-----------------------------------------------------------------------------------+
+|:doc:`tools/multiBigwigSummary` | data integration | 2 or more bigWig | interval-based table of values | perform cross-sample analyses of genome-wide scores --> plotCorrelation, plotPCA |
++-------------------------------------+------------------+-------------------------------------+--------------------------------------------+-----------------------------------------------------------------------------------+
+|:doc:`tools/plotCorrelation` | visualization | bam/multiBigwigSummary output | clustered heatmap | visualize the Pearson/Spearman correlation |
++-------------------------------------+------------------+-------------------------------------+--------------------------------------------+-----------------------------------------------------------------------------------+
+|:doc:`tools/plotPCA` | visualization | bam/multiBigwigSummary output | 2 PCA plots | visualize the principal component analysis |
++-------------------------------------+------------------+-------------------------------------+--------------------------------------------+-----------------------------------------------------------------------------------+
+|:doc:`tools/plotFingerprint` | QC | 2 BAM | 1 diagnostic plot | assess enrichment strength of a ChIP sample |
++-------------------------------------+------------------+-------------------------------------+--------------------------------------------+-----------------------------------------------------------------------------------+
+|:doc:`tools/computeGCBias` | QC | 1 BAM | 2 diagnostic plots | calculate the exp. and obs. GC distribution of reads |
++-------------------------------------+------------------+-------------------------------------+--------------------------------------------+-----------------------------------------------------------------------------------+
+|:doc:`tools/correctGCBias` | QC | 1 BAM, output from computeGCbias | 1 GC-corrected BAM | obtain a BAM file with reads distributed according to the genome’s GC content |
++-------------------------------------+------------------+-------------------------------------+--------------------------------------------+-----------------------------------------------------------------------------------+
+|:doc:`tools/bamCoverage` | normalization | BAM | bedGraph or bigWig | obtain the normalized read coverage of a single BAM file |
++-------------------------------------+------------------+-------------------------------------+--------------------------------------------+-----------------------------------------------------------------------------------+
+|:doc:`tools/bamCompare` | normalization | 2 BAM | bedGraph or bigWig | normalize 2 files to each other (e.g. log2ratio, difference) |
++-------------------------------------+------------------+-------------------------------------+--------------------------------------------+-----------------------------------------------------------------------------------+
+|:doc:`tools/computeMatrix` | data integration | 1 or more bigWig, 1 or more BED | zipped file for plotHeatmap or plotProfile | compute the values needed for heatmaps and summary plots |
++-------------------------------------+------------------+-------------------------------------+--------------------------------------------+-----------------------------------------------------------------------------------+
+|:doc:`tools/estimateReadFiltering` | information | 1 or more BAM files | table of values | estimate the number of reads filtered from a BAM file or files |
++-------------------------------------+------------------+-------------------------------------+--------------------------------------------+-----------------------------------------------------------------------------------+
+|:doc:`tools/alignmentSieve` | QC | 1 BAM file | 1 filtered BAM or BEDPE file | filters a BAM file based on one or more criteria |
++-------------------------------------+------------------+-------------------------------------+--------------------------------------------+-----------------------------------------------------------------------------------+
+|:doc:`tools/plotHeatmap` | visualization | computeMatrix output | heatmap of read coverages | visualize the read coverages for genomic regions |
++-------------------------------------+------------------+-------------------------------------+--------------------------------------------+-----------------------------------------------------------------------------------+
+|:doc:`tools/plotProfile` | visualization | computeMatrix output | summary plot (“meta-profile”) | visualize the average read coverages over a group of genomic regions |
++-------------------------------------+------------------+-------------------------------------+--------------------------------------------+-----------------------------------------------------------------------------------+
+|:doc:`tools/plotCoverage` | visualization | 1 or more BAM | 2 diagnostic plots | visualize the average read coverages over sampled genomic positions |
++-------------------------------------+------------------+-------------------------------------+--------------------------------------------+-----------------------------------------------------------------------------------+
+|:doc:`tools/bamPEFragmentSize` | information | 1 BAM | text with paired-end fragment length | obtain the average fragment length from paired ends |
++-------------------------------------+------------------+-------------------------------------+--------------------------------------------+-----------------------------------------------------------------------------------+
+|:doc:`tools/plotEnrichment` | visualization | 1 or more BAM and 1 or more BED/GTF | A diagnostic plot | plots the fraction of alignments overlapping the given features |
++-------------------------------------+------------------+-------------------------------------+--------------------------------------------+-----------------------------------------------------------------------------------+
+|:doc:`tools/computeMatrixOperations` | miscellaneous | 1 or more BAM and 1 or more BED/GTF | A diagnostic plot | plots the fraction of alignments overlapping the given features |
++-------------------------------------+------------------+-------------------------------------+--------------------------------------------+-----------------------------------------------------------------------------------+
+
+General principles
+^^^^^^^^^^^^^^^^^^
+
+A typical deepTools command could look like this:
+
+.. code:: bash
+
+ $ bamCoverage --bam myAlignedReads.bam \
+ --outFileName myCoverageFile.bigWig \
+ --outFileFormat bigwig \
+ --fragmentLength 200 \
+ --ignoreDuplicates \
+ --scaleFactor 0.5
+
+You can always see all available command-line options via --help or -h:
+
+.. code:: bash
+
+ $ bamCoverage --help
+ $ bamCoverage -h
+
+And a minimal usage example can be shown by running a command without any arguments:
+
+.. code:: bash
+
+ $ bamCoverage
+
+- Output format of plots should be indicated by the file ending, e.g. ``MyPlot.pdf`` will return a pdf file, ``MyPlot.png`` a png-file
+- All tools that produce plots can also output the underlying data - this can be useful in cases where you don't like the deepTools visualization, as you can then use the data matrices produced by deepTools with your favorite plotting tool, such as R
+- The vast majority of command line options are also available in Galaxy (in a few cases with minor changes to their naming).
+
+Parameters to decrease the run time
+"""""""""""""""""""""""""""""""""""
+
+- ``numberOfProcessors`` - Number of processors to be used
+
+For example, setting ``--numberOfProcessors 10`` will split up the workload internally into 10 chunks, which will be processed in parallel.
+Note that for highly fragmented assemblies (> 1000 contigs) the runtime increases drastically. Consider to include only canonical chromosomes in cases like this.
+
+- ``region`` - Process only a single genomic region.
+
+This is particularly useful when you're still trying to figure out the best parameter setting. You can focus on a certain genomic region by setting, e.g., ``--region chr2`` or ``--region chr2:100000-200000``
+
+Both parameters are optional and available throughout almost all deepTools.
+
+Filtering BAMs while processing
+"""""""""""""""""""""""""""""""
+
+Several deepTools modules allow for efficient processing of BAM files, e.g. ``bamCoverage`` and ``bamCompare``.
+We offer several ways to filter those BAM files on the fly so that you don't need to pre-process them using other tools such as `samtools `_
+
+- ``ignoreDuplicates``
+ Reads with the same orientation and start position will be considered only once. If reads are paired, the mate is also evaluated
+- ``minMappingQuality``
+ Only reads with a mapping quality score of at least this are considered
+- ``samFlagInclude``
+ Include reads based on the SAM flag, e.g. ``--samFlagInclude 64`` gets reads that are first in a pair. For translating SAM flags into English, go to: `https://broadinstitute.github.io/picard/explain-flags.html `_
+- ` `samFlagExclude``
+ Exclude reads based on the SAM flags - see previous explanation.
+
+These parameters are optional and available throughout deepTools.
+
+.. note:: In version 2.3 we introduced a sampling method to correct the effect of filtering when normalizing using ``bamCoverage`` or ``bamCompare``. For previous versions, if you know that your files will be strongly affected by the filtering of duplicates or reads of low quality then consider removing those reads *before* using ``bamCoverage`` or ``bamCompare``, as the filtering by deepTools is done *after* the scaling factors are calculated!
+
+
+Tools for BAM and bigWig file processing
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+:doc:`tools/multiBamSummary`
+""""""""""""""""""""""""""""
+:doc:`tools/multiBigwigSummary`
+"""""""""""""""""""""""""""""""
+:doc:`tools/correctGCBias`
+""""""""""""""""""""""""""
+:doc:`tools/bamCoverage`
+""""""""""""""""""""""""
+:doc:`tools/bamCompare`
+"""""""""""""""""""""""
+:doc:`tools/bigwigCompare`
+""""""""""""""""""""""""""
+:doc:`tools/bigwigAverage`
+""""""""""""""""""""""""""
+:doc:`tools/computeMatrix`
+""""""""""""""""""""""""""
+:doc:`tools/alignmentSieve`
+"""""""""""""""""""""""""""
+
+Tools for QC
+^^^^^^^^^^^^
+
+:doc:`tools/plotCorrelation`
+""""""""""""""""""""""""""""
+:doc:`tools/plotPCA`
+""""""""""""""""""""
+:doc:`tools/plotFingerprint`
+""""""""""""""""""""""""""""
+:doc:`tools/bamPEFragmentSize`
+""""""""""""""""""""""""""""""
+:doc:`tools/computeGCBias`
+""""""""""""""""""""""""""
+:doc:`tools/plotCoverage`
+"""""""""""""""""""""""""
+
+Heatmaps and summary plots
+^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+:doc:`tools/plotHeatmap`
+""""""""""""""""""""""""
+:doc:`tools/plotProfile`
+""""""""""""""""""""""""
+:doc:`tools/plotEnrichment`
+"""""""""""""""""""""""""""
+
+Miscellaneous
+^^^^^^^^^^^^^
+
+:doc:`tools/computeMatrixOperations`
+""""""""""""""""""""""""""""""""""""
+:doc:`tools/estimateReadFiltering`
+""""""""""""""""""""""""""""""""""
diff --git a/deepTools/source/docs/content/tools/alignmentSieve.rst b/deepTools/source/docs/content/tools/alignmentSieve.rst
new file mode 100644
index 0000000000000000000000000000000000000000..d9f6dd19d6a3df906327379a82d57a3181eb1599
--- /dev/null
+++ b/deepTools/source/docs/content/tools/alignmentSieve.rst
@@ -0,0 +1,82 @@
+alignmentSieve
+==============
+
+.. contents::
+ :local:
+
+.. argparse::
+ :ref: deeptools.alignmentSieve.parseArguments
+ :prog: alignmentSieve
+ :nodefault:
+
+
+Background
+^^^^^^^^^^
+
+This tool filters alignments in a BAM/CRAM file according the the specified parameters. It can optionally output to BEDPE format, possibly with the fragment ends shifted in a custom manner.
+
+Usage example
+^^^^^^^^^^^^^
+
+``alignmentSieve`` needs a sorted and indexed BAM file and the desired filtering criteria.
+
+.. code:: bash
+
+ $ alignmentSieve -b paired_chr2L.bam \
+ --minMappingQuality 5 --samFlagInclude 16 \
+ --samFlagExclude 256 --ignoreDuplicates \
+ -o filtered.bam --filterMetrics metrics.txt
+
+The alignments passing the filtering criteria are then written to the file specified by ``-o``. You can additionally save alignments **NOT** passing the filtering criteria with the ``-filteredOutReads`` If you would like to store metrics about the number of reads seen and the number remaining after filtering, then specify the file for that with ``--filterMetrics``. An example metrics file is below:
+
+ #bamFilterReads --filterMetrics
+ #File Reads Remaining Total Initial Reads
+ paired_chr2L.bam 8440 12644
+
+Instead of a BAM file, a BEDPE file (suitable for input into MACS2) can be produced. Like the BAM/CRAM output, BEDPE also allows shifting of fragment ends, as is often desirable in ATAC-seq and related protocols:
+
+.. code:: bash
+
+ $ alignmentSieve -b paired_chr2L.bam \
+ --minFragmentLength 140 --BED \
+ --shift -5 3 -o fragments.bedpe
+
+The ``--shift`` option can take either 2 or 4 integers. If two integers are given, then the first value shifts the left-most end of a fragment and the second the right-most end. Positive values shift to the right and negative values to the left. See below for how the above settings would shift a single fragment::
+
+ ----> read 1
+ read 2 <----
+
+ ------------------------ fragment
+
+ -------------------------------- shifted fragment
+
+The same results will be produced if read 1 and read 2 are swapped. If, instead, the protocol is strand-specific, then the first set of integers in a pair would be applied to fragments where read 1 precedes read 2, and the second set to cases where read 2 precedes read 1. In this case, the first value in each pair is applied to the end of read 1 and the second to the end of read 2. Take the following command as an example:
+
+.. code:: bash
+
+ $ alignmentSieve -b paired_chr2L.bam \
+ --minFragmentLength 140 --BED \
+ --shift -5 3 -1 4 -o fragments.bedpe
+
+Given that, the ``-5 3`` set would produce the following::
+
+ ----> read 1
+ read 2 <----
+
+ ------------------------ fragment
+
+ -------------------------------- shifted fragment
+
+and the ``-1 4`` set would produce the following::
+
+ ----> read 2
+ read 1 <----
+
+ ------------------------ fragment
+
+ --------------------- shifted fragment
+
+As can be seen, such fragments are considered to be on the ``-`` strand, so negative values then shift to the left on its frame of reference (thus, to the right relative to the ``+`` strand).
+
+.. note::
+ If the ``--shift`` or ``--ATACshift`` options are used, then only properly-paired reads will be used.
diff --git a/deepTools/source/docs/content/tools/bamCompare.rst b/deepTools/source/docs/content/tools/bamCompare.rst
new file mode 100644
index 0000000000000000000000000000000000000000..a0522a88c42b5699af1f461fe4f0f136cb2d429f
--- /dev/null
+++ b/deepTools/source/docs/content/tools/bamCompare.rst
@@ -0,0 +1,27 @@
+bamCompare
+===========
+
+``bamCompare`` can be used to generate a :ref:`bigWig` or :ref:`bedGraph` file based on **two BAM** files that are compared to each other while being simultaneously normalized for sequencing depth.
+
+.. image:: ../../images/norm_IGVsnapshot_indFiles.png
+
+If you are not familiar with BAM, bedGraph and bigWig formats, you can read up on that in our :doc:`../help_glossary`
+
+The basic algorithm works proceeds in two steps:
+
+1. Per-sample scaling / depth Normalization:
+
+ - If scaling is used (using the SES or read counts method), appropriate scaling
+ factors are determined to account for sequencing depth differences.
+ - Optionally scaling can be turned off and individual samples normalized using the
+ RPKM, BPM or CPM methods (or no normalization at all)
+
+2. A per-bin calculation is performed after accounting for scaling:
+
+ - The genome is transversed and the log2 ratio/ratio/difference/etc. for each bin of fixed width is computed.
+
+
+.. argparse::
+ :ref: deeptools.bamCompare.parseArguments
+ :prog: bamCompare
+ :nodefault:
diff --git a/deepTools/source/docs/content/tools/bamCoverage.rst b/deepTools/source/docs/content/tools/bamCoverage.rst
new file mode 100644
index 0000000000000000000000000000000000000000..e5420ea16ff4913adf7bd919293fbe13604b7cf3
--- /dev/null
+++ b/deepTools/source/docs/content/tools/bamCoverage.rst
@@ -0,0 +1,159 @@
+bamCoverage
+===========
+
+.. contents::
+ :local:
+
+.. image:: ../../images/norm_IGVsnapshot_indFiles.png
+
+If you are not familiar with BAM, bedGraph and bigWig formats, you can read up on that in our :doc:`../help_glossary`
+
+.. argparse::
+ :ref: deeptools.bamCoverage.parseArguments
+ :prog: bamCoverage
+ :nodefault:
+
+
+Usage hints
+^^^^^^^^^^^^
+
+* A smaller bin size value will result in a higher resolution of the coverage track but also in a larger file size.
+* The 1x normalization (RPGC) requires the input of a value for the **effective genome size**, which is the mappable part of the reference genome. Of course, this value is species-specific. The command line help of this tool offers suggestions for a number of model species.
+* It might be useful for some studies to exclude certain chromosomes in order to avoid biases, e.g. chromosome X, as male mice contain a pair of each autosome, but usually only a single X chromosome.
+* By default, the read length is **NOT** extended! This is the preferred setting for **spliced-read** data like RNA-seq, where one usually wants to rely on the detected read locations only. A read extension would neglect potential splice sites in the unmapped part of the fragment.
+ Other data, e.g. Chip-seq, where fragments are known to map contiguously, should be processed with read extension (``--extendReads [INTEGER]``).
+* For paired-end data, the fragment length is generally defined by the two read mates. The user provided fragment length is only used as a fallback for singletons or mate reads that map too far apart (with a distance greater than four times the fragment length or are located on different chromosomes).
+
+.. warning:: If you already normalized for GC bias using ``correctGCbias``, you should absolutely **NOT** set the parameter ``--ignoreDuplicates``!
+
+.. note:: Like BAM files, bigWig files are compressed, binary files. If you would like to see the coverage values, choose the bedGraph output via ``--outFileFormat``.
+
+Usage example for ChIP-seq
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+This is an example for ChIP-seq data using additional options (smaller bin size for higher resolution, normalizing coverage to 1x mouse genome size, excluding chromosome X during the normalization step, and extending reads):
+
+.. code:: bash
+
+ bamCoverage --bam a.bam -o a.SeqDepthNorm.bw \
+ --binSize 10
+ --normalizeUsing RPGC
+ --effectiveGenomeSize 2150570000
+ --ignoreForNormalization chrX
+ --extendReads
+
+If you had run the command with ``--outFileFormat bedgraph``, you could easily peak into the resulting file.
+
+.. code:: bash
+
+ $ head SeqDepthNorm_chr19.bedgraph
+ 19 60150 60250 9.32
+ 19 60250 60450 18.65
+ 19 60450 60650 27.97
+ 19 60650 60950 37.29
+ 19 60950 61000 27.97
+ 19 61000 61050 18.65
+ 19 61050 61150 27.97
+ 19 61150 61200 18.65
+ 19 61200 61300 9.32
+ 19 61300 61350 18.65
+
+As you can see, each row corresponds to one region. If consecutive bins have the same number of reads overlapping, they will be merged.
+
+Usage examples for RNA-seq
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Note that some BAM files are filtered based on SAM flags (`Explain SAM flags `_).
+
+Regular bigWig track
+~~~~~~~~~~~~~~~~~~~~~
+
+.. code:: bash
+
+ bamCoverage -b a.bam -o a.bw
+
+
+Separate tracks for each strand
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Sometimes it makes sense to generate two independent :ref:`bigWig` files for all reads on the forward and reverse strand, respectively.
+As of deepTools version 2.2, one can simply use the ``--filterRNAstrand`` option, such as ``--filterRNAstrand forward`` or ``--filterRNAstrand reverse``.
+This handles paired-end and single-end datasets. For older versions of deepTools, please see the instructions below.
+
+.. note:: The ``--filterRNAstrand`` option assumes the sequencing library generated from ILLUMINA dUTP/NSR/NNSR methods, which are the most commonly used method for
+ library preparation, where Read 2 (R2) is in the direction of RNA strand (**reverse-stranded** library). However other methods exist, which generate read
+ R1 in the direction of RNA strand (`see this review `_). For these libraries,
+ ``--filterRNAstrand`` will have an opposite behavior, i.e. ``--filterRNAstrand forward`` will give you reverse strand signal and vice-versa.
+
+Versions before 2.2
+*******************
+
+To follow the examples, you need to know that ``-f`` will tell ``samtools view`` to **include** reads with the indicated flag, while ``-F`` will lead to the **exclusion** of reads with the respective flag.
+
+**For a stranded `single-end` library**
+
+.. code:: bash
+
+ # Forward strand
+ bamCoverage -b a.bam -o a.fwd.bw --samFlagExclude 16
+
+ # Reverse strand
+ bamCoverage -b a.bam -o a.rev.bw --samFlagInclude 16
+
+
+
+**For a stranded `paired-end` library**
+
+Now, this gets a bit cumbersome, but future releases of deepTools will make this more straight-forward.
+For now, bear with us and perhaps read up on SAM flags, e.g. `here `_.
+
+For paired-end samples, we assume that a proper pair should have the mates on opposing strands where the Illumina strand-specific protocol produces reads in a ``R2-R1`` orientation. We basically follow the recipe given `in this biostars tutorial `_.
+
+To get the file for transcripts that originated from the **forward strand**:
+
+.. code:: bash
+
+
+ # include reads that are 2nd in a pair (128);
+ # exclude reads that are mapped to the reverse strand (16)
+ $ samtools view -b -f 128 -F 16 a.bam > a.fwd1.bam
+
+ # exclude reads that are mapped to the reverse strand (16) and
+ # first in a pair (64): 64 + 16 = 80
+ $ samtools view -b -f 80 a.bam > a.fwd2.bam
+
+ # combine the temporary files
+ $ samtools merge -f fwd.bam a.fwd1.bam a.fwd2.bam
+
+ # index the filtered BAM file
+ $ samtools index fwd.bam
+
+ # run bamCoverage
+ $ bamCoverage -b fwd.bam -o a.fwd.bigWig
+
+ # remove the temporary files
+ $ rm a.fwd*.bam
+
+To get the file for transcripts that originated from the **reverse strand**:
+
+.. code:: bash
+
+ # include reads that map to the reverse strand (128)
+ # and are second in a pair (16): 128 + 16 = 144
+ $ samtools view -b -f 144 a.bam > a.rev1.bam
+
+ # include reads that are first in a pair (64), but
+ # exclude those ones that map to the reverse strand (16)
+ $ samtools view -b -f 64 -F 16 a.bam > a.rev2.bam
+
+ # merge the temporary files
+ $ samtools merge -f rev.bam a.rev1.bam a.rev2.bam
+
+ # index the merged, filtered BAM file
+ $ samtools index rev.bam
+
+ # run bamCoverage
+ $ bamCoverage -b rev.bam -o a.rev.bw
+
+ # remove temporary files
+ $ rm a.rev*.bam
diff --git a/deepTools/source/docs/content/tools/bamPEFragmentSize.rst b/deepTools/source/docs/content/tools/bamPEFragmentSize.rst
new file mode 100644
index 0000000000000000000000000000000000000000..85f16e07d082e28d461da65e529b610a487ae093
--- /dev/null
+++ b/deepTools/source/docs/content/tools/bamPEFragmentSize.rst
@@ -0,0 +1,84 @@
+bamPEFragmentSize
+=================
+
+.. argparse::
+ :ref: deeptools.bamPEFragmentSize.parse_arguments
+ :prog: bamPEFragmentSize
+ :nodefault:
+
+Example usage
+^^^^^^^^^^^^^^
+
+.. code:: bash
+
+ $ deepTools2.0/bin/bamPEFragmentSize \
+ -hist fragmentSize.png \
+ -T "Fragment size of PE RNA-seq data" \
+ --maxFragmentLength 1000 \
+ -b testFiles/RNAseq_sample1.bam testFiles/RNAseq_sample2.bam \
+ testFiles/RNAseq_sample3.bam testFiles/RNAseq_sample4.bam \
+ -samplesLabel sample1 sample2 sample3 sample4
+
+
+## Output
+
+.. code:: bash
+
+ BAM file : testFiles/RNAseq_sample1.bam
+
+ Sample size: 10815
+
+
+ Fragment lengths:
+ Min.: 0.0
+ 1st Qu.: 311.0
+ Mean: 8960.68987517
+ Median: 331.0
+ 3rd Qu.: 362.0
+ Max.: 53574842.0
+ Std: 572421.46625
+
+ Read lengths:
+ Min.: 20.0
+ 1st Qu.: 101.0
+ Mean: 99.1621821544
+ Median: 101.0
+ 3rd Qu.: 101.0
+ Max.: 101.0
+ Std: 9.16567362755
+
+ BAM file : testFiles/RNAseq_sample2.bam
+
+ Sample size: 6771
+
+
+ Fragment lengths:
+ Min.: 43.0
+ 1st Qu.: 148.0
+ Mean: 176.465071629
+ Median: 164.0
+ 3rd Qu.: 185.0
+ Max.: 500.0
+ Std: 53.733877263
+
+ ......(output truncated)
+
+
+.. image:: ../../images/test_plots/ExampleFragmentSize.png
+
+If the ``--table`` option is specified, the summary statistics are additionally printed in a tabular format::
+
+ Frag. Len. Min. Frag. Len. 1st. Qu. Frag. Len. Mean Frag. Len. Median Frag. Len. 3rd Qu. Frag. Len. Max Frag. Len. Std. Read Len. Min. Read Len. 1st. Qu. Read Len. Mean Read Len. Median Read Len. 3rd Qu. Read Len. Max Read Len. Std.
+ bowtie2 test1.bam 241.0 241.5 244.666666667 242.0 246.5 251.0 4.49691252108 251.0 251.0 251.0 251.0 251.0 251.0 0.0
+
+If the ``--outRawFragmentLengths`` option is provided, another history item will be produced, containing the raw data underlying the histogram. It has the following format::
+
+ #bamPEFragmentSize
+ Size Occurrences Sample
+ 241 1 bowtie2 test1.bam
+ 242 1 bowtie2 test1.bam
+ 251 1 bowtie2 test1.bam
+
+The "Size" is the fragment (or read, for single-end datasets) size and "Occurrences" are the number of times reads/fragments with that length were observed. For easing downstream processing, the sample name is a
+lso included on each row.
+
diff --git a/deepTools/source/docs/content/tools/bigwigAverage.rst b/deepTools/source/docs/content/tools/bigwigAverage.rst
new file mode 100644
index 0000000000000000000000000000000000000000..7772219240b42f6120556c185d2ea20ff13043ec
--- /dev/null
+++ b/deepTools/source/docs/content/tools/bigwigAverage.rst
@@ -0,0 +1,7 @@
+bigwigAverage
+=============
+
+.. argparse::
+ :ref: deeptools.bigwigAverage.parse_arguments
+ :prog: bigwigAverage
+ :nodefault:
diff --git a/deepTools/source/docs/content/tools/bigwigCompare.rst b/deepTools/source/docs/content/tools/bigwigCompare.rst
new file mode 100644
index 0000000000000000000000000000000000000000..e0a861035416c85ffe948dda788b10164bc658e0
--- /dev/null
+++ b/deepTools/source/docs/content/tools/bigwigCompare.rst
@@ -0,0 +1,7 @@
+bigwigCompare
+=============
+
+.. argparse::
+ :ref: deeptools.bigwigCompare.parse_arguments
+ :prog: bigwigCompare
+ :nodefault:
diff --git a/deepTools/source/docs/content/tools/computeGCBias.rst b/deepTools/source/docs/content/tools/computeGCBias.rst
new file mode 100644
index 0000000000000000000000000000000000000000..c3ba679f58ea60a63445fba2f67c39b37b9905b9
--- /dev/null
+++ b/deepTools/source/docs/content/tools/computeGCBias.rst
@@ -0,0 +1,67 @@
+computeGCBias
+=============
+
+.. contents::
+ :local:
+
+.. argparse::
+ :ref: deeptools.computeGCBias.parse_arguments
+ :prog: computeGCBias
+ :nodefault:
+
+
+Background
+^^^^^^^^^^^
+
+``computeGCBias`` is based on a paper by `Benjamini and Speed `_.
+The basic assumption of the GC bias diagnosis is that an ideal sample should show a uniform distribution of sequenced reads across the genome, i.e. all regions of the genome should have similar numbers of reads, regardless of their base-pair composition.
+In reality, the DNA polymerases used for PCR-based amplifications during the library preparation of the sequencing protocols prefer GC-rich regions. This will influence the outcome of the sequencing as there will be more reads for GC-rich regions just because of the DNA polymerase's preference.
+
+``computeGCbias`` will first calculate the **expected GC profile** by counting the number of DNA fragments of a fixed size per GC fraction where GC fraction is defined as the number of G's or C's in a genome region of a given length.
+The result is basically a histogram depicting the frequency of DNA fragments for each type of genome region with a GC fraction between 0 to 100 percent. This will be different for each reference genome, but is independent of the actual sequencing experiment.
+
+The profile of the expected DNA fragment distribution is then compared to the **observed GC profile**, which is generated by counting the number of sequenced reads per GC fraction.
+
+In an ideal experiment, the observed GC profile would, of course, look like the expected profile.
+This is indeed the case when applying ``computeGCBias`` to simulated reads.
+
+.. _computeGCBias_example_image:
+
+.. image:: ../../images/GC_bias_simulated_reads_2L.png
+
+As you can see, both plots based on **simulated reads** do not show enrichments or depletions for specific GC content bins, there is an almost flat line around the log2ratio of 0 (= ratio(observed/expected) of 1). The fluctuations on the ends of the x axis are due to the fact that only very, very few regions in the *Drosophila* genome have such extreme GC fractions so that the number of fragments that are picked up in the random sampling can vary.
+
+Now, let's have a look at **real-life data** from genomic DNA sequencing. Panels A and B can be clearly distinguished and the major change that took place between the experiments underlying the plots was that the samples in panel A were prepared with too many PCR cycles and a standard polymerase whereas the samples of panel B were subjected to very few rounds of amplification using a high fidelity DNA polymerase.
+
+.. image:: ../../images/QC_GCplots_input.png
+
+.. note:: The expected GC profile depends on the reference genome as different organisms have very different GC contents. For example, one would expect more fragments with GC fractions between 30% to 60% in mouse samples (average GC content of the mouse genome: 45 %) than for genome fragments from, for example, *Plasmodium falciparum* (average genome GC content *P. falciparum*: 20%).
+
+Excluding regions from the read distribution calculation
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+In some cases, it will make sense to exclude certain regions from the calculation of the read distributions to increase the accuracy of the computation.
+There are several kinds of regions that are either not expected to show a read distribution matching the background or where the uncertainty of the reference genome might be too big. Please consider the following points:
+
+* **repetitive regions**: if multi-reads (reads that map to more than one genomic position) were excluded from the [BAM][] file, it will help to exclude known repetitive regions. You can get BED files of known repetitive regions from `UCSC Table Browser `_ (see the screenshot below for an example of human repetitive elements).
+
+.. image:: ../../images/QC_GCregionexclusion_UCSCscreenshot.png
+
+* **regions of low mappability**: these are regions where the mapping of the reads notoriously fails and we recommend to exclude known regions with mappability issues from the GC computation. You can download the mappability tracks for different read lengths from UCSC, e.g. for `mouse `_ and `human `_. In the github deepTools folder "scripts", you can find a shell script called ``mappabilityBigWig_to_unmappableBed.sh`` which will turn the [bigWig][] mappability file from UCSC into a BED file.
+
+* **ChIP-seq peaks**: in ChIP-seq samples it is *expected* that certain regions *should* show more reads than expected based on the background distribution, therefore it makes absolute sense to exclude those regions from the GC bias calculation. We recommend to run a simple, non-conservative peak calling on the uncorrected [BAM][] file first to obtain a BED file of peak regions that should then subsequently be supplied to ``computeGCbias``.
+
+
+Usage example
+^^^^^^^^^^^^^^^
+
+::
+
+ $ computeGCBias -b H3K27Me3.bam --effectiveGenomeSize 2695000000
+ --genome genome.2bit -l 200 -freq freq_test.txt
+ --region X --biasPlot test_gc.png
+
+
+.. image:: ../../images/test_plots/ExampleComputeGCBias.png
+
+
diff --git a/deepTools/source/docs/content/tools/computeMatrix.rst b/deepTools/source/docs/content/tools/computeMatrix.rst
new file mode 100644
index 0000000000000000000000000000000000000000..60aaf8cb55fb5731922c10c0bfb9ae6fff823022
--- /dev/null
+++ b/deepTools/source/docs/content/tools/computeMatrix.rst
@@ -0,0 +1,132 @@
+computeMatrix
+=============
+
+.. contents::
+ :local:
+
+.. argparse::
+ :ref: deeptools.computeMatrix.parse_arguments
+ :prog: computeMatrix
+ :nodefault:
+
+Details
+^^^^^^^
+
+``computeMatrix`` has two main modes of use:
+
+* for computing the signal distribution **relative to a point** (``reference-point``), e.g., the beginning or end of each genomic region
+* for computing the signal **over a set of regions** (``scale-regions``) where all regions are scaled to the same size
+
+.. image:: ../../images/computeMatrix_modes.png
+
+``computeMatrix`` is tightly connected to ``plotHeatmap`` and ``plotProfile``: it takes the values of all the signal files and all genomic regions that you would like to plot and computes the corresponding data matrix.
+
+See :doc:`plotHeatmap` and :doc:`plotProfile` for example plots.
+
+.. image:: ../../images/computeMatrix_overview.png
+
+In addition to generating the intermediate, gzipped file for ``plotHeatmap`` and ``plotProfile``, ``computeMatrix`` can also be used to simply output the values underlying the heatmap or to **filter and sort BED files** using, for example, the ``--skipZeros`` and the ``--sortUsing`` parameters.
+
+The following tables summarizes the kinds of optional outputs that are available with the three tools.
+
++-----------------------------------+--------------------------------+-------------------+-----------------+-----------------+
+| **optional output type** | **command** | **computeMatrix** | **plotHeatmap** | **plotProfile** |
++-----------------------------------+--------------------------------+-------------------+-----------------+-----------------+
+| values underlying the heatmap | ``--outFileNameMatrix`` | yes | yes | no |
++-----------------------------------+--------------------------------+-------------------+-----------------+-----------------+
+| values underlying the profile | ``--outFileNameData`` | no | yes | yes |
++-----------------------------------+--------------------------------+-------------------+-----------------+-----------------+
+| sorted and/or filtered regions | ``--outFileSortedRegions`` | yes | yes | yes |
++-----------------------------------+--------------------------------+-------------------+-----------------+-----------------+
+
+.. tip:: ``computeMatrix`` can use multiple threads (``-p`` option), which significantly decreases the time for calculating the values.
+
+.. attention::
+ As of version 3.0, computeMatrix produces output with labels present for each sample. Matrices produced with that or later versions can not be used with older versions of ``plotHeatmap`` or any other deepTools program.
+
+.. note::
+ ``computeMatrix`` will properly handle strand information if your BED file includes that column (GTF files always include strand). For the ``--metagene`` option to work, you will need either a BED12 (including columns 11 and 12) or a GTF file as input. GFF is NOT the same as GTF format!
+
+Examples
+^^^^^^^^
+
+The following examples should give you an idea of some of the most often used settings for ``computeMatrix``. As you can see, ``computeMatrix`` offers myriad tweaks and may turn out to be more useful to you than "just" to calculate heatmap matrices.
+
+Example 1: single input files (reference-point mode)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Here, we start with a single :ref:`bigWig` and a single :ref:`BED` file, i.e., ``computeMatrix`` will:
+
+1. take the beginning of the regions specified in the BED file
+2. add the values indicated with ``--beforeRegionStartLength`` (``-b``) and ``--afterRegionStartLength`` (``-a``)
+3. split the resulting region up into 50 bp bins (can be changed via (``--binSize``)
+4. calculate the mean score based on the scores given in the bigWig file (the kind of score can be changed via ``--averageTypeBins``)
+5. write out the values where each row corresponds to one region in the BED file (note that you can, for example, skip regions with zero coverage; sorting is also possible)
+
+.. code:: bash
+
+ $ computeMatrix reference-point \ # choose the mode
+ --referencePoint TSS \ # alternatives: TES, center
+ -b 3000 -a 10000 \ # define the region you are interested in
+ -R testFiles/genes.bed \
+ -S testFiles/log2ratio_H3K4Me3_chr19.bw \
+ --skipZeros \
+ -o matrix1_H3K4me3_l2r_TSS.gz \ # to be used with plotHeatmap and plotProfile
+ --outFileSortedRegions regions1_H3K4me3_l2r_genes.bed
+
+Let's have a closer look at the regions' output:
+
+.. code:: bash
+
+ $ wc -l testFiles/genes.bed # original file
+ 18257 testFiles/genes.bed
+ $ wc -l regions1_H3K4me3_l2r_genes.bed # file generated by computeMatrix
+ 12423 regions1_H3K4me3_l2r_genes.bed
+
+As you can see, the number of regions is drastically reduced. The remaining genes happen to be the ones on chromosome 19 for which there was at least one overlapping read. This makes sense since the bigWig file used above only contained reads for chromosome 19.
+
+.. code:: bash
+
+ # the original file contained genes for chr.19 and chr.X
+ $ cut -f 1 testFiles/genes.bed | sort | uniq -c
+ 12439 19
+ 5818 X
+
+ # the regions used for the computation of the matrix for the heatmap are all located on chr.19 due to the --skipZeros setting (see above)
+ $ cut -f 1 regions1_H3K4me3_l2r_genes.bed | sort | uniq -c
+ 1 #genes
+ 12422 19
+
+
+Example 2: multiple input files (scale-regions mode)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code:: bash
+
+ $ deepTools2.0/bin/computeMatrix scale-regions \
+ -R genes_chr19_firstHalf.bed genes_chr19_secondHalf.bed \ # separate multiple files with spaces
+ -S testFiles/log2ratio_*.bw \ or use the wild card approach
+ -b 3000 -a 3000 \
+ --regionBodyLength 5000 \
+ --skipZeros -o matrix2_multipleBW_l2r_twoGroups_scaled.gz \
+ --outFileNameMatrix matrix2_multipleBW_l2r_twoGroups_scaled.tab \
+ --outFileSortedRegions regions2_multipleBW_l2r_twoGroups_genes.bed
+
+
+Note that the reported regions will have the same coordinates as the ones in the originally supplied file, not the region that was used for the heatmap matrix.
+
+The groups of regions supplied by two individual files will be merged into one:
+
+.. code:: bash
+
+ $ head -n 2 regions2_multipleBW_l2r_twoGroups_genes.bed
+ 19 60104 70951 ENST00000592209 0.0 - genes_chr19_firstHalf
+ 19 60950 70966 ENST00000606728 0.0 - genes_chr19_firstHalf
+
+ $ tail -n 3 regions2_multipleBW_l2r_twoGroups_genes.bed
+ 19 59108549 59110722 ENST00000596427 0.0 - genes_chr19_secondHalf
+ 19 59110333 59110802 ENST00000464061 0.0 + genes_chr19_secondHalf
+ #genes_chr19_secondHalf
+
+
+.. tip:: **More examples** can be found in our `Gallery `_.
diff --git a/deepTools/source/docs/content/tools/computeMatrixOperations.rst b/deepTools/source/docs/content/tools/computeMatrixOperations.rst
new file mode 100644
index 0000000000000000000000000000000000000000..e06db04cf00f7d95593ddf7c06e66d0849aa0bf7
--- /dev/null
+++ b/deepTools/source/docs/content/tools/computeMatrixOperations.rst
@@ -0,0 +1,89 @@
+computeMatrixOperations
+=======================
+
+.. contents::
+ :local:
+
+.. argparse::
+ :ref: deeptools.computeMatrixOperations.parse_arguments
+ :prog: computeMatrixOperations
+ :nodefault:
+
+Details
+^^^^^^^
+
+``computeMatrixOperations`` can perform a variety of operations on one or more files produced by ``computeMatrix`` (N.B., the output is always written to a new file):
+
++----------------+--------------------------------------------------------------------------------------------------------------------------+
++ **Subcommand** | **What it does** |
++----------------+--------------------------------------------------------------------------------------------------------------------------+
++ info | Prints out the sample and region group names in the order in which they appear. |
++----------------+--------------------------------------------------------------------------------------------------------------------------+
++ subset | Subsets a file by the desired samples/region group names. This can also change the order of these samples/region groups. |
++----------------+--------------------------------------------------------------------------------------------------------------------------+
++ filterStrand | Filters the file to only include regions annotated as being on a particular strand. |
++----------------+--------------------------------------------------------------------------------------------------------------------------+
++ rbind | Concatenates multiple matrices together, top to bottom. |
++----------------+--------------------------------------------------------------------------------------------------------------------------+
++ cbind | Merges multiple matrices, left to right. |
++----------------+--------------------------------------------------------------------------------------------------------------------------+
++ sort | Sorts the given file so regions are in the order of occurence in the input BED/GTF file(s). |
++----------------+--------------------------------------------------------------------------------------------------------------------------+
+
+
+These operations are useful when you want to run computeMatrix on multiple files (thereby keeping all of the values together) and later exclude regions/samples or add new ones. Another common use would be if you require the output of computeMatrix to be sorted to match the order of regions in the input file.
+
+.. attention::
+ As of version 3.0, computeMatrix (and therefore also computeMatrixOperations) produces output with labels present for each sample. If you run any operations on matrices output by older versions then they will be modified to be comformant with the new output, which is not backward compatible!
+
+Examples
+^^^^^^^^
+
+Suppose that we have a strand-specific RNAseq dataset and would like to plot only the strand-specific signal across spliced transcripts. The general steps would be as follows:
+
+1. Run `bamCoverage` on each sample twice, once with `--filterRNAstrand forward` and again with `--filterRNAstrand reverse`. This will result in twice the number of bigWig files as samples.
+2. Run `computeMatrix scale-regions` with all of these bigWig files, including the `--metagene` option and a BED12 and/or a GTF file. This produces a file containing the signal separated by strand for each transcript.
+3. Get the list of sample names stored in the matrix file:
+
+.. code:: bash
+
+ $ computeMatrixOperations info -m foo.mat.gz
+ Groups:
+ genes
+ Samples:
+ SRR648667.forward
+ SRR648668.forward
+ SRR648669.forward
+ SRR648670.forward
+ SRR648667.reverse
+ SRR648668.reverse
+ SRR648669.reverse
+ SRR648670.reverse
+
+4. Create two new files, each containing only the samples containing signal from a given strand.
+
+.. code:: bash
+
+ $ computeMatrixOperations subset -m foo.mat.gz -o forward.mat.gz --samples SRR648667.forward SRR648668.forward SRR648669.forward SRR648670.forward
+ $ computeMatrixOperations subset -m foo.mat.gz -o reverse.mat.gz --samples SRR648667.reverse SRR648668.reverse SRR648669.reverse SRR648670.reverse
+
+5. These files can then be subset to contain only transcripts on a particular strand. Note that it's best to double check that the ``--strand -`` setting produces the intended results. There are many peculiar variants of RNAseq library preparation and the settings for one type may not be appropriate for another (to check this, use the different ``--strand`` options on the same matrix and then run ``plotHeatmap``, one of them will be obviously correct and the other largely blank).
+
+.. code:: bash
+
+ $ computeMatrixOperations filterStrand -m forward.mat.gz -o forward.subset.mat.gz --strand -
+ $ computeMatrixOperations filterStrand -m reverse.mat.gz -o reverse.subset.mat.gz --strand +
+
+6. Finally, the files can be merged back together, head to tail. The samples are already in the correct order, as indicated by step 3.
+
+.. code:: bash
+
+ $ computeMatrixOperations rbind -m forward.subset.mat.gz reverse.subset.mat.gz -o merged.mat.gz
+
+7. If desired, the transcripts can then be resorted to match the order of the input GTF file.
+
+.. code:: bash
+
+ $ computeMatrixOperations sort -m merged.mat.gz -o sorted.mat.gz -R genes.gtf
+
+The resulting file can then be used with ``plotHeatmap`` or ``plotProfile``. Note that we could have skipped the subset step and run ``computeMatrix`` independently on the forward and reverse bigWig files.
diff --git a/deepTools/source/docs/content/tools/correctGCBias.rst b/deepTools/source/docs/content/tools/correctGCBias.rst
new file mode 100644
index 0000000000000000000000000000000000000000..7432fb7562680238cbb6594d3d291951cec6e972
--- /dev/null
+++ b/deepTools/source/docs/content/tools/correctGCBias.rst
@@ -0,0 +1,25 @@
+correctGCBias
+=============
+
+.. hint:: For background information about the GC bias assessment and correction, see :doc:`computeGCBias`.
+
+.. argparse::
+ :ref: deeptools.correctGCBias.parse_arguments
+ :prog: correctGCBias
+ :nodefault:
+
+
+Usage example
+^^^^^^^^^^^^^^
+
+.. note:: ``correctGCBias`` requires the output of ``computeGCBias`` and a genome file in 2bit format. Most genomes can be found here: http://hgdownload.cse.ucsc.edu/gbdb/. Search for the ``.2bit`` ending. Otherwise, FASTA files can be converted to 2bit using ``faToTwoBit``, which is available here: http://hgdownload.cse.ucsc.edu/admin/exe/
+
+.. code:: bash
+
+ $ correctGCBias -b H3K27Me3.bam
+ --effectiveGenomeSize 2695000000
+ --genome genome.2bit
+ --GCbiasFrequenciesFile freq_test.txt # output of computeGCBias
+ -o gc_corrected.bam
+
+.. warning:: The GC-corrected BAM file will most likely contain several duplicated reads in regions where the coverage had to increased in order to match the expected read density. This means that you should absolutely avoid using any filtering of duplicate reads during your downstream analyses!
diff --git a/deepTools/source/docs/content/tools/estimateReadFiltering.rst b/deepTools/source/docs/content/tools/estimateReadFiltering.rst
new file mode 100644
index 0000000000000000000000000000000000000000..f57fd386a477bc1c1b2f6e6aa19e1761c693433b
--- /dev/null
+++ b/deepTools/source/docs/content/tools/estimateReadFiltering.rst
@@ -0,0 +1,51 @@
+estimateReadFiltering
+=====================
+
+.. contents::
+ :local:
+
+.. argparse::
+ :ref: deeptools.estimateReadFiltering.parseArguments
+ :prog: estimateReadFiltering
+ :nodefault:
+
+
+Background
+^^^^^^^^^^
+
+Many tools within deepTools allow one to filter BAM files according to alignment mapping qualities or other criteria. It's difficult to know ahead of time how these various settings will affect the number of filtered reads. Consequently, ``estimateReadFiltering`` can be used to approximate the number of reads in a BAM file or files that will be filtered according to one or more criteria. This can also be used the quickly estimate the duplication level in a BAM file.
+
+Usage example
+^^^^^^^^^^^^^
+
+``estimateReadFiltering`` needs one or more sorted and indexed BAM files and the desired filtering criteria.
+
+.. code:: bash
+
+ $ estimateReadFiltering -b paired_chr2L.bam \
+ --minMappingQuality 5 --samFlagInclude 16 \
+ --samFlagExclude 256 --ignoreDuplicates
+
+By default, the output is printed to the screen. You can change this with the ``-o`` option. The output is a tab-separated file:
+
+ Sample Total Reads Mapped Reads Alignments in blacklisted regions Estimated mapped reads filtered Below MAPQ Missing Flags Excluded Flags Internally-determined Duplicates Marked Duplicates Singletons Wrong strand
+ paired_chr2L.bam 12644 12589 0 6313.2 4114.0 6340.0 0.0 1163.0 0.0 55.0 0.0
+
+The columns are as follows:
+
+ * Total reads (including unmapped)
+ * Unmapped reads
+ * Reads in blacklisted regions (--blackListFileName)
+
+The following metrics are estimated according to the --binSize and --distanceBetweenBins parameters
+ * Estimated mapped reads filtered (the total number of mapped reads filtered for any reason)
+ * Alignments with a below threshold MAPQ (--minMappingQuality)
+ * Alignments with at least one missing flag (--samFlagInclude)
+ * Alignments with undesirable flags (--samFlagExclude)
+ * Duplicates determined by deepTools (--ignoreDuplicates)
+ * Duplicates marked externally (e.g., by picard)
+ * Singletons (paired-end reads with only one mate aligning)
+ * Wrong strand (due to --filterRNAstrand)
+
+The sum of these may be more than the total number of reads. Note that alignments are sampled from bins of size --binSize spaced --distanceBetweenBins apart.
+
diff --git a/deepTools/source/docs/content/tools/multiBamSummary.rst b/deepTools/source/docs/content/tools/multiBamSummary.rst
new file mode 100644
index 0000000000000000000000000000000000000000..e39ed3ab0b890fb2fe28552d18c0e7aeb595e3bf
--- /dev/null
+++ b/deepTools/source/docs/content/tools/multiBamSummary.rst
@@ -0,0 +1,39 @@
+multiBamSummary
+================
+
+.. contents::
+ :local:
+
+.. argparse::
+ :ref: deeptools.multiBamSummary.parse_arguments
+ :prog: multiBamSummary
+ :nodefault:
+
+
+Example
+^^^^^^^
+
+The default output of ``multiBamSummary`` (a compressed ``numpy`` array: `*.npz`) can be visualized using :doc:`plotCorrelation` or :doc:`plotPCA`.
+
+The optional output (``--outRawCounts``) is a simple tab-delimited file that can be used with any other program. The first three columns define the region of the genome for which the reads were summarized.
+
+.. code:: bash
+
+ $ deepTools2.0/bin/multiBamSummary bins \
+ --bamfiles testFiles/*bam \ # using all BAM files in the folder
+ --minMappingQuality 30 \
+ --region 19 \ # limiting the binning of the genome to chromosome 19
+ --labels H3K27me3 H3K4me1 H3K4me3 HeK9me3 input \
+ -out readCounts.npz --outRawCounts readCounts.tab
+
+ $ head readCounts.tab
+ #'chr' 'start' 'end' 'H3K27me3' 'H3K4me1' 'H3K4me3' 'HeK9me3' 'input'
+ 19 10000 20000 0.0 0.0 0.0 0.0 0.0
+ 19 20000 30000 0.0 0.0 0.0 0.0 0.0
+ 19 30000 40000 0.0 0.0 0.0 0.0 0.0
+ 19 40000 50000 0.0 0.0 0.0 0.0 0.0
+ 19 50000 60000 0.0 0.0 0.0 0.0 0.0
+ 19 60000 70000 1.0 1.0 0.0 0.0 1.0
+ 19 70000 80000 0.0 1.0 7.0 0.0 1.0
+ 19 80000 90000 15.0 0.0 0.0 6.0 4.0
+ 19 90000 100000 73.0 7.0 4.0 16.0 5.0
diff --git a/deepTools/source/docs/content/tools/multiBigwigSummary.rst b/deepTools/source/docs/content/tools/multiBigwigSummary.rst
new file mode 100644
index 0000000000000000000000000000000000000000..1c9af5520c5afa91e1adf22f42243e3c0b4b97ad
--- /dev/null
+++ b/deepTools/source/docs/content/tools/multiBigwigSummary.rst
@@ -0,0 +1,72 @@
+multiBigwigSummary
+==================
+
+.. contents::
+ :local:
+
+.. argparse::
+ :ref: deeptools.multiBigwigSummary.parse_arguments
+ :prog: multiBigwigSummary
+ :nodefault:
+
+
+Example
+~~~~~~~
+
+In the following example, the average values for our test ENCODE
+ChIP-Seq datasets are computed for consecutive genome bins (default size: 10kb) by using the `bins` mode.
+
+.. code:: bash
+
+ $ deepTools2.0/bin/multiBigwigSummary bins \
+ -b testFiles/H3K4Me1.bigWig testFiles/H3K4Me3.bigWig testFiles/H3K27Me3.bigWig testFiles/Input.bigWig \
+ --labels H3K4me1 H3K4me3 H3K27me3 input \
+ -out scores_per_bin.npz --outRawCounts scores_per_bin.tab
+
+ $ head scores_per_bin.tab
+ #'chr' 'start' 'end' 'H3K4me1' 'H3K4me3' 'H3K27me3' 'input'
+ 19 0 10000 0.0 0.0 0.0 0.0
+ 19 10000 20000 0.0 0.0 0.0 0.0
+ 19 20000 30000 0.0 0.0 0.0 0.0
+ 19 30000 40000 0.0 0.0 0.0 0.0
+ 19 40000 50000 0.0 0.0 0.0 0.0
+ 19 50000 60000 0.0221538461538 0.0 0.00482142857143 0.0522717391304
+ 19 60000 70000 4.27391282051 1.625 0.634116071429 1.29124347826
+ 19 70000 80000 13.0891675214 24.65 1.8180625 2.80073695652
+ 19 80000 90000 1.74591965812 0.29 4.35576785714 0.92987826087
+
+To compute the average values for a set of genes, use the `BED-file` mode.
+
+.. code:: bash
+
+ $ deepTools2.0/bin/multiBigwigSummary BED-file \
+ --bwfiles testFiles/*bigWig \
+ --BED testFiles/genes.bed \
+ --labels H3K27me3 H3K4me1 H3K4me3 HeK9me3 input \
+ -out scores_per_transcript.npz --outRawCounts scores_per_transcript.tab
+
+ $ head scores_per_transcript.tab
+ #'chr' 'start' 'end' 'H3K27me3' 'H3K4me1' 'H3K4me3' 'HeK9me3' 'input'
+ 19 60104 70951 0.663422099099 4.37103606574 14.9609108509 0.596631607217 1.34274297191
+ 19 60950 70966 0.714223982699 4.54650763906 16.2336261981 0.62173674295 1.41719308888
+ 19 62114 70944 0.747578769617 4.84009060023 18.2951302378 0.648723472352 1.51324474371
+ 19 63820 70951 0.781816722009 5.30500631048 22.5579862572 0.682862029229 1.55490104062
+ 19 65057 66382 0.528301886792 5.45886792453 0.523018867925 0.555471698113 1.97056603774
+ 19 65821 66416 0.411764705882 3.0 0.636974789916 0.168067226891 1.67226890756
+ 19 65821 70945 0.844600775761 4.79176424668 31.1346604215 0.693073728066 1.47911787666
+ 19 66319 66492 0.774566473988 1.59537572254 0.0 0.0 0.578034682081
+ 19 66345 71535 0.877430197151 5.49036608863 43.978805395 0.746026011561 1.43545279383
+
+
+The default output of ``multiBamSummary`` (a compressed ``numpy`` array: `*.npz`) can be visualized using :doc:`plotCorrelation` or :doc:`plotPCA`.
+
+The optional output (``--outRawCounts``) is a simple tab-delimited file that can be used with any other program. The first three columns define the region of the genome for which the reads were summarized.
+
+
+multiBigwigSummary in Galaxy
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Below is the screenshot showing how to use multiBigwigSummary on the deeptools galaxy.
+
+
+.. image:: ../../images/bigwiCorr_galaxy.png
diff --git a/deepTools/source/docs/content/tools/plotCorrelation.rst b/deepTools/source/docs/content/tools/plotCorrelation.rst
new file mode 100644
index 0000000000000000000000000000000000000000..d42244687c86c34ba53e18226bf6017da4b7db1f
--- /dev/null
+++ b/deepTools/source/docs/content/tools/plotCorrelation.rst
@@ -0,0 +1,95 @@
+plotCorrelation
+===============
+
+.. contents::
+ :local:
+
+.. argparse::
+ :ref: deeptools.plotCorrelation.parse_arguments
+ :prog: plotCorrelation
+ :nodefault:
+
+Background
+^^^^^^^^^^
+
+``plotCorrelation`` computes the overall similarity between two or more files based on read coverage (or other scores) within genomic regions, which must be calculated using either :doc:`multiBamSummary` or :doc:`multiBigwigSummary`.
+
+Correlation calculation
+~~~~~~~~~~~~~~~~~~~~~~~
+
+The result of the correlation computation is a **table of correlation coefficients** that indicates how "strong" the relationship between two samples is and it will consist of numbers between -1 and 1. (-1 indicates perfect anti-correlation, 1 perfect correlation.)
+
+.. image:: ../../images/QC_bamCorrelate_intro.png
+
+We offer two different functions for the correlation computation: *Pearson* or *Spearman*.
+
+The *Pearson method* measures the **metric differences** between samples and is therefore influenced by outliers. More precisely, it is defined as the covariance of two variables divided by the product of their standard deviation.
+
+The *Spearman method* is based on **rankings**.
+If you imagine a race with 3 participants where the winner and runner-up are very close together while the third person broke her leg and comes in way, way after the first two, then Pearson would be strongly influenced by the fact that the third person had a great distance to the first ones while Spearman would only care about the fact that person 1 came in first, person 2 came in second and person 3 got the third rank, the distances between them are ignored.
+
+.. tip:: Pearson is an appropriate measure for data that follows a normal distribution, while Spearman does not make this assumption and is generally less driven by outliers, but with the caveat of also being less sensitive.
+
+Hierarchical clustering
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+If you use the heatmap output of ``plotCorrelation``, this will automatically lead to a clustering of the samples based on the correlation coefficients. This helps to determine whether the different sample types can be separated, i.e., samples of different conditions are expected to be more dissimilar to each other than replicates within the same condition.
+
+The *distances* of the sample pairs are based on the correlation coefficients, *r*, where distance = 1 - *r*. The similarity of the samples is assessed using the nearest point algorithm, i.e., the shortest distance between any 2 members of the tree is considered to decide whether to join a cluster or not. For more details of the algorithm, go `here `_.
+
+Examples
+^^^^^^^^
+
+Here's an example of RNA-seq data from different human cell lines that we had downloaded from https://genome.ucsc.edu/ENCODE/dataMatrix/encodeDataMatrixHuman.html.
+
+.. image:: ../../images/QC_bamCorrelate_RNAseq.png
+
+As you can see, both correlation calculations more or less agree on which samples are nearly identical (the replicates, indicated by 1 or 2 at the end of the label). The Spearman correlation, however, seems to be more robust and meets our expectations more closely as the two different cell types (HUVEC and IMR90) are clearly separated.
+
+In the following example, a correlation analysis is performed based on the coverage file computed by :doc:`multiBamSummary` or :doc:`multiBigwigSummary` for our test ENCODE ChIP-Seq datasets.
+
+**Scatterplot**
+
+Here we make pairwose scatterplots of the average scores per transcript that we calculated using :doc:`multiBigwigSummary` and include the Pearson correlation coefficients for each comparison.
+
+.. code:: bash
+
+ $ deepTools2.0/bin/plotCorrelation \
+ -in scores_per_transcript.npz \
+ --corMethod pearson --skipZeros \
+ --plotTitle "Pearson Correlation of Average Scores Per Transcript" \
+ --whatToPlot scatterplot \
+ -o scatterplot_PearsonCorr_bigwigScores.png \
+ --outFileCorMatrix PearsonCorr_bigwigScores.tab
+
+.. image:: ../../images/test_plots/scatterplot_PearsonCorr_bigwigScores.png
+
+.. code:: bash
+
+ $ cat PearsonCorr_bigwigScores.tab
+ 'H3K27me3' 'H3K4me1' 'H3K4me3' 'HeK9me3' 'input'
+ 'H3K27me3' 1.0000 -0.1032 -0.1269 -0.0339 -0.0395
+ 'H3K4me1' -0.1032 1.0000 0.3985 -0.1863 0.3328
+ 'H3K4me3' -0.1269 0.3985 1.0000 -0.0480 0.2822
+ 'HeK9me3' -0.0339 -0.1863 -0.0480 1.0000 -0.0353
+ 'input' -0.0395 0.3328 0.2822 -0.0353 1.0000
+
+
+**Heatmap**
+
+In addition to scatterplots, heatmaps can be generated where the pairwise correlation coefficients are depicted by varying color intensities and are clustered using hierarchical clustering.
+
+The example here calculates the Spearman correlation coefficients of read counts.
+The dendrogram indicates which samples' read counts are most similar to each other.
+
+.. code:: bash
+
+ $ deepTools2.0/bin/plotCorrelation \
+ -in readCounts.npz \
+ --corMethod spearman --skipZeros \
+ --plotTitle "Spearman Correlation of Read Counts" \
+ --whatToPlot heatmap --colorMap RdYlBu --plotNumbers \
+ -o heatmap_SpearmanCorr_readCounts.png \
+ --outFileCorMatrix SpearmanCorr_readCounts.tab
+
+.. image:: ../../images/test_plots/heatmap_SpearmanCorr_readCounts.png
diff --git a/deepTools/source/docs/content/tools/plotCoverage.rst b/deepTools/source/docs/content/tools/plotCoverage.rst
new file mode 100644
index 0000000000000000000000000000000000000000..82e8558ddacee341fff19e1eda581b59e1946620
--- /dev/null
+++ b/deepTools/source/docs/content/tools/plotCoverage.rst
@@ -0,0 +1,82 @@
+plotCoverage
+============
+
+.. contents::
+ :local:
+
+.. argparse::
+ :ref: deeptools.plotCoverage.parse_arguments
+ :prog: plotCoverage
+ :nodefault:
+
+What the plots tell you
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+The tool generates a panel of two plots.
+The first one simply represents the frequencies of the found read coverages, which helps you judge how relevant the mean coverage value (printed next to the sample name) is. If the distribution of read coverages is more or less homoskedatic and, ideally, normally distributed (most likely it won't be), then the mean is a very appropriate proxy for sequencing depth.
+
+The second plot helps you answer the question *what is the fraction of the genome that has a depth of sequencing of 2?*
+
+.. image:: ../../images/plotCoverage_annotated.png
+
+Usage example
+^^^^^^^^^^^^^^
+
+.. code:: bash
+
+ $ plotCoverage -b H3K4Me1.bam H3K4Me3.bam H3K27Me3.bam H3K9Me3.bam
+ --plotFile example_coverage
+ -n 1000000
+ --plotTitle "example_coverage" \
+ --outRawCounts coverage.tab \
+ --ignoreDuplicates \
+ --minMappingQuality 10 \
+ --region 19
+
+ # have a look at the optional tabular output: each row represents the number of reads overlapping with a sampled bp
+ $ head coverage.tab
+ 'H3K27me3' 'H3K4me1' 'H3K4me3' 'H3K9me3'
+ 0 0 0 0
+ 0 0 0 0
+ 0 0 0 0
+ 0 0 0 0
+ 0 0 0 0
+ 0 0 0 0
+ 0 0 0 0
+ 0 0 0 0
+ 0 0 0 0
+
+ $ cut -f1 coverage.tab | sort -n | uniq -c
+ 1 'H3K27me3'
+ 548190 0 # the vast majority of sampled bp had 0 overlapping reads
+ 127914 1
+ 35703 2
+ 12271 3
+ 4584 4
+ 1717 5
+ 659 6
+ 251 7
+ 106 8
+ 49 9
+ 16 10
+ 6 11
+ 3 12
+ 2 13
+ 3 14
+ 1 15
+ 1 16
+ 2 17
+ 1 19
+ 1 21
+ 2 22
+ 1 23
+ 1 24
+ 2 28
+ 1 35
+ 1 40 # there was one bp with 40 overlapping reads!
+ 1 44
+
+
+.. image:: ../../images/test_plots/ExamplePlotCoverage.png
+
+As you can see, the coverage of our test data sets is very poor -- on average, there is fewer than 1 read per bp!
diff --git a/deepTools/source/docs/content/tools/plotEnrichment.rst b/deepTools/source/docs/content/tools/plotEnrichment.rst
new file mode 100644
index 0000000000000000000000000000000000000000..c9815edfe1fff3f94473b3bcaf23143780e50c0f
--- /dev/null
+++ b/deepTools/source/docs/content/tools/plotEnrichment.rst
@@ -0,0 +1,33 @@
+plotEnrichment
+==============
+
+.. contents::
+ :local:
+
+.. argparse::
+ :ref: deeptools.plotEnrichment.parse_arguments
+ :prog: plotEnrichment
+ :nodefault:
+
+
+Background
+^^^^^^^^^^
+
+It's often useful to know what percentage of alignments or fragments overlap one or more groups of regions. For example, "fragment of reads in peaks" or FRiP scores are a common QC metric for ChIPseq data that asks such a question. Another example would be seeing what fraction of RNAseq reads are in exons, or genes, since both of these should be high. ``plotEnrichment`` allows efficiently answering these sorts of questions.
+
+Usage example
+^^^^^^^^^^^^^
+
+``plotEnrichment`` needs one or more sorted and indexed BAM files and one or more BED and/or GTF files. For GTF files, feature labels are given according to the 3rd column ('feature'). For BED files, labels are given by the file name, though this can be overriden with the ``--regionLabels`` option.
+
+.. code:: bash
+
+ $ plotEnrichment -b Input.bam H3K4Me1.bam H3K4Me3.bam \
+ --BED up.bed down.bed \
+ --regionLabels "up regulated" "down regulated" \
+ -o enrichment.png
+
+The values underlying the plot can also be output with the ``--outRawCounts`` option and the y-axis can be auto-adjusted with the ``--variableScales`` option.
+
+.. image:: ../../images/test_plots/plotEnrichment.png
+
diff --git a/deepTools/source/docs/content/tools/plotFingerprint.rst b/deepTools/source/docs/content/tools/plotFingerprint.rst
new file mode 100644
index 0000000000000000000000000000000000000000..1be0d17cfc14ff4d713c78e88558ccaab0573814
--- /dev/null
+++ b/deepTools/source/docs/content/tools/plotFingerprint.rst
@@ -0,0 +1,76 @@
+plotFingerprint
+===============
+
+This quality control will most likely be of interest for you if you are dealing with ChIP-seq samples as a pressing question in ChIP-seq experiments is "Did my ChIP work?", i.e. did the antibody-treatment enrich sufficiently so that the ChIP signal can be separated from the background signal? (After all, around 90% of all DNA fragments in a ChIP experiment will represent the genomic background).
+
+.. note:: We've termed the plots described here "fingerprints" because we feel that they help us judging individual ChIP-seq files, but the original idea came from `Diaz et al. `__
+
+.. contents::
+ :local:
+
+.. argparse::
+ :ref: deeptools.plotFingerprint.parse_arguments
+ :prog: plotFingerprint
+ :nodefault:
+
+Background
+^^^^^^^^^^^
+
+This tool is based on a method developed by `Diaz et al. `__.
+It determines how well the signal in the ChIP-seq sample can be differentiated from the background distribution of reads in the control sample.
+For factors that will enrich well-defined, rather narrow regions (e.g. transcription factors such as p300), the resulting plot can be used to assess the strength of a ChIP, but the broader the enrichments are to be expected, the less clear the plot will be.
+Vice versa, if you do not know what kind of signal to expect, the fingerprint plot will give you a straight-forward indication of how careful you will have to be during your downstream analyses to separate biological noise from meaningful signal.
+
+Similar to ``multiBamSummary``, ``plotFingerprint`` randomly samples genome regions (bins) of a specified length and sums the per-base coverage in indexed BAM files that overlap with those regions.
+These values are then sorted according to their rank and the cumulative sum of read counts is plotted.
+
+
+What the plots tell you
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+An ideal [input][] with perfect uniform distribution of reads along the genome (i.e. without enrichments in open chromatin etc.) and infinite sequencing coverage should generate a straight diagonal line. A very specific and strong ChIP enrichment will be indicated by a prominent and steep rise of the cumulative sum towards the highest rank. This means that a big chunk of reads from the ChIP sample is located in few bins which corresponds to high, narrow enrichments typically seen for transcription factors.
+
+Here you see 3 different fingerprint plots.
+We chose these examples to show you how the nature of the ChIP signal (narrow and high vs. wide and not extremely high) is reflected in the "fingerprint" plots.
+
+.. image:: ../../images/QC_fingerprint.png
+
+Quality control metrics
+~~~~~~~~~~~~~~~~~~~~~~~
+
+For a detailed explanation of the QC metrics, please see: :doc:`../feature/plotFingerprint_QC_metrics`.
+
+Usage example
+^^^^^^^^^^^^^^^^
+
+The following example generates the fingerprints for the invididual ENCODE histone mark ChIP-seq data sets and their corresponding input (focusing on chromosome 19 and thus adjusting the number of 500 bp bins that are being sampled using ``--numberOfSamples`` to avoid overlapping bins).
+
+.. code:: bash
+
+ $ deepTools2.0/bin/plotFingerprint \
+ -b testFiles/*bam \
+ --labels H3K27me3 H3K4me1 H3K4me3 H3K9me3 input \
+ --minMappingQuality 30 --skipZeros \
+ --region 19 --numberOfSamples 50000 \
+ -T "Fingerprints of different samples" \
+ --plotFile fingerprints.png \
+ --outRawCounts fingerprints.tab
+
+.. image:: ../../images/test_plots/fingerprints.png
+
+The table that you can obtain via ``--outRawCounts`` simply contains the sum of the per-base coverage inside each sampled genome bin. For the plot above, each column is sorted in increasing order and then the cumulative sum is plotted.
+
+.. code:: bash
+
+ $ head fingerprints.tab
+ #plotFingerprint --outRawCounts
+ 'H3K27me3' 'H3K4me1' 'H3K4me3' 'H3K9me3' 'input'
+ 1 0 0 0 0
+ 0 0 0 0 1
+ 0 1 0 0 0
+ 12 0 0 3 3
+ 3 0 1 1 0
+ 6 4 0 1 0
+ 1 0 0 0 0
+ 4 1 1 1 0
+ 1 0 0 0 0
diff --git a/deepTools/source/docs/content/tools/plotHeatmap.rst b/deepTools/source/docs/content/tools/plotHeatmap.rst
new file mode 100644
index 0000000000000000000000000000000000000000..3920ef0ec95d0db598cc834020b532a0bedd4b49
--- /dev/null
+++ b/deepTools/source/docs/content/tools/plotHeatmap.rst
@@ -0,0 +1,122 @@
+plotHeatmap
+===========
+
+.. contents::
+ :local:
+
+.. argparse::
+ :ref: deeptools.plotHeatmap.parse_arguments
+ :prog: plotHeatmap
+ :nodefault:
+
+
+Details
+^^^^^^^
+
+.. note:: With the release of deepTools 2.3 is is now possible to set the color and scale of each heatmap
+ individually. Also, we added the option to remove the boxes around the heatmaps.
+
+
+``plotHeatmap`` does not change the values that ``computeMatrix`` calculated, it simply translates them into heatmaps and summary plots.
+It offers a large variety of parameters to explore various visualizations and customize the resulting image (see the commands above).
+
+In addition, you can retrieve all the data tables underlying the various plots including the regions that were used to generate the final plot.
+The following tables summarizes the kinds of optional outputs that are available with the three tools.
+
+
++-----------------------------------+--------------------------------+-------------------+-----------------+-----------------+
+| **optional output type** | **command** | **computeMatrix** | **plotHeatmap** | **plotProfile** |
++-----------------------------------+--------------------------------+-------------------+-----------------+-----------------+
+| values underlying the heatmap | ``--outFileNameMatrix`` | yes | yes | no |
++-----------------------------------+--------------------------------+-------------------+-----------------+-----------------+
+| values underlying the profile | ``--outFileNameData`` | no | yes | yes |
++-----------------------------------+--------------------------------+-------------------+-----------------+-----------------+
+| sorted and/or filtered regions | ``--outFileSortedRegions`` | yes | yes | yes |
++-----------------------------------+--------------------------------+-------------------+-----------------+-----------------+
+
+.. tip:: For more details on the optional output, see the examples for :doc:`computeMatrix`.
+
+
+Usage examples
+^^^^^^^^^^^^^^
+
+The following example creates a heatmap over hg19 transcripts for our test ENCODE datasets. Note that the matrix contains multiple groups of regions (in this case, one for each chromosome used).
+
+.. code:: bash
+
+ # run compute matrix to collect the data needed for plotting
+ $ computeMatrix scale-regions -S H3K27Me3-input.bigWig \
+ H3K4Me1-Input.bigWig \
+ H3K4Me3-Input.bigWig \
+ -R genes19.bed genesX.bed \
+ --beforeRegionStartLength 3000 \
+ --regionBodyLength 5000 \
+ --afterRegionStartLength 3000
+ --skipZeros -o matrix.mat.gz
+
+ $ plotHeatmap -m matrix.mat.gz \
+ -out ExampleHeatmap1.png \
+
+.. image:: ../../images/test_plots/ExampleHeatmap1.png
+
+As mentioned above, ``plotHeatmap`` has many options, including the ability to do k-means clustering and change the color map.
+
+.. code:: bash
+
+ $ plotHeatmap -m matrix_two_groups.gz \
+ -out ExampleHeatmap2.png \
+ --colorMap RdBu \
+ --whatToShow 'heatmap and colorbar' \
+ --zMin -3 --zMax 3 \
+ --kmeans 4
+
+.. image:: ../../images/test_plots/ExampleHeatmap2.png
+
+.. tip:: **More examples** can be found in our `Gallery `_.
+
+
+Multiple colors for heatmaps
+++++++++++++++++++++++++++++
+
+Since deepTools version 2.3 it is now possible to adjust the color and scale of each heatmap. There are two ways
+to adjust the colors, one by specifying each of the colormaps (e.g. `--colorMap RdBlGr winter terrain`) and the
+other is by giving each of the colors in the heatmap (e.g. `--colorList 'red,blue' 'white,green', 'white, blue, red'`).
+For the second example, the number of transitions between the colors is given by the `--colorNumber` which by default
+is 256.
+
+The following is an example using the `--colorList` method is used. Also the scale of each heatmap is modified
+using `--zMin` and `--zMax`.
+
+.. code:: bash
+
+ $ plotHeatmap -m matrix_two_groups.gz \
+ -out ExampleHeatmap3.png \
+ --colorList 'black, yellow' 'white,blue' '#ffffff,orange,#000000'
+ --whatToShow 'heatmap and colorbar' \
+ --zMin -2 -2 0 --zMax 2 2 3
+ --kmeans 4
+ --dpi 100
+
+.. image:: ../../images/test_plots/ExampleHeatmap3.png
+
+
+No box around heatmaps
+++++++++++++++++++++++
+
+In version 2.3 we also added the option to remove the box around heatmaps. In the following example
+we combine different colormap colors, different scales and the new `--boxAroundHeatmaps` option.
+
+.. code:: bash
+
+ $ plotHeatmap -m matrix_two_groups.gz \
+ -out ExampleHeatmap4.png \
+ --colorMap Oranges_r Blues_r Greens
+ --whatToShow 'heatmap and colorbar' \
+ --zMin -4 -4 0 --zMax 0 0 5
+ --kmeans 4
+ --dpi 100
+ --boxAroundHeatmaps no
+
+.. image:: ../../images/test_plots/ExampleHeatmap4.png
+
+.. tip:: **More examples** can be found in our `Gallery `_.
diff --git a/deepTools/source/docs/content/tools/plotPCA.rst b/deepTools/source/docs/content/tools/plotPCA.rst
new file mode 100644
index 0000000000000000000000000000000000000000..abef9194857bd93388433b24321801dcf65588dc
--- /dev/null
+++ b/deepTools/source/docs/content/tools/plotPCA.rst
@@ -0,0 +1,37 @@
+plotPCA
+=======
+
+.. contents::
+ :local:
+
+.. argparse::
+ :ref: deeptools.plotPCA.parse_arguments
+ :prog: plotPCA
+ :nodefault:
+
+
+Background
+^^^^^^^^^^^
+
+Principal component analysis (PCA) can be used, for example, to determine whether **samples display greater variability** between experimental conditions than between replicates of the same treatment. PCA is also useful to identify unexpected patterns, such as those caused by batch effects or outliers.
+Principal components represent the directions along which the variation in the data is maximal, so that the information (e.g., read coverage values) from thousands of regions can be represented by just a few dimensions.
+
+.. note:: PCA is not designed to identify unknown groupings or clustering and given an unexpected result, it is up to the researcher to determine the experimental or technical reason underlying the principal components.
+
+Usage example
+^^^^^^^^^^^^^^^
+
+``plotPCA`` needs the compressed ``numpy array`` output from either :doc:`multiBamSummary` or :doc:`multiBigwigSummary`
+
+.. code:: bash
+
+ $ deepTools2.0/bin/plotPCA -in readCounts.npz \
+ -o PCA_readCounts.png \
+ -T "PCA of read counts"
+
+After perfoming the PCA on the values supplied as the input, ``plotPCA`` will sort the principal components according to the amount of variability of the data that they explain. Based on this, you will obtain two plots:
+
+* the eigenvalues of the **top two principal components**
+* the **Scree plot** for the top five principal components where the bars represent the amount of variability explained by the individual factors and the red line traces the amount of variability is explained by the individual components in a cumulative manner
+
+.. image:: ../../images/test_plots/PCA_readCounts.png
diff --git a/deepTools/source/docs/content/tools/plotProfile.rst b/deepTools/source/docs/content/tools/plotProfile.rst
new file mode 100644
index 0000000000000000000000000000000000000000..fb0a1a41cbfa9bd8e9e2a13ba4d2f4a2e4f39326
--- /dev/null
+++ b/deepTools/source/docs/content/tools/plotProfile.rst
@@ -0,0 +1,90 @@
+plotProfile
+===========
+
+.. argparse::
+ :ref: deeptools.plotProfile.parse_arguments
+ :prog: plotProfile
+ :nodefault:
+
+Details
+^^^^^^^^
+
+Like :doc:`plotHeatmap`, ``plotProfile`` simply takes the compressed matrix produced by ``computeMatrix`` and turns it into summary plots.
+
+In addition to a large range of parameters for optimizing the visualization, you can also export the values underlying the profiles as tables.
+
++-----------------------------------+--------------------------------+-------------------+-----------------+-----------------+
+| **optional output type** | **command** | **computeMatrix** | **plotHeatmap** | **plotProfile** |
++-----------------------------------+--------------------------------+-------------------+-----------------+-----------------+
+| values underlying the heatmap | ``--outFileNameMatrix`` | yes | yes | no |
++-----------------------------------+--------------------------------+-------------------+-----------------+-----------------+
+| values underlying the profile | ``--outFileNameData`` | no | yes | yes |
++-----------------------------------+--------------------------------+-------------------+-----------------+-----------------+
+| sorted and/or filtered regions | ``--outFileSortedRegions`` | yes | yes | yes |
++-----------------------------------+--------------------------------+-------------------+-----------------+-----------------+
+
+.. tip:: For more details on the optional output, see the examples for :doc:`computeMatrix`.
+
+Usage example
+^^^^^^^^^^^^^^
+
+The following example plots the signal profile over hg19 transcripts for our test ENCODE datasets. Note that
+the matrix contains multiple groups of regions (in this case, one for each present chromosome).
+
+.. code:: bash
+
+ # run compute matrix to collect the data needed for plotting
+ $ computeMatrix scale-regions -S H3K27Me3-input.bigWig \
+ H3K4Me1-Input.bigWig \
+ H3K4Me3-Input.bigWig \
+ -R genes19.bed genesX.bed \
+ --beforeRegionStartLength 3000 \
+ --regionBodyLength 5000 \
+ --afterRegionStartLength 3000
+ --skipZeros -o matrix.mat.gz
+
+ $ plotProfile -m matrix.mat.gz \
+ -out ExampleProfile1.png \
+ --numPlotsPerRow 2 \
+ --plotTitle "Test data profile"
+
+.. image:: ../../images/test_plots/ExampleProfile1.png
+
+``plotProfile`` has many options, including the ability to change the type of lines plotted and to plot by group rather than sample.
+
+Here's the same data set, but plotted with a different set of parameters.
+
+.. code:: bash
+
+ $ plotProfile -m matrix.mat.gz \
+ -out ExampleProfile2.png \
+ --plotType=fill \ # add color between the x axis and the lines
+ --perGroup \ # make one image per BED file instead of per bigWig file
+ --colors red yellow blue \
+ --plotTitle "Test data profile"
+
+.. image:: ../../images/test_plots/ExampleProfile2.png
+
+
+In this other example the data is clustered using k-means into two groups.
+
+.. code:: bash
+
+ $ plotProfile -m matrix.mat.gz \
+ --perGroup \
+ --kmeans 2 \
+ -out ExampleProfile3.png
+
+.. image:: ../../images/test_plots/ExampleProfile3.png
+
+This is the same data but visualized using `--plotType heatmap`
+
+.. code:: bash
+
+ $ plotProfile -m matrix.mat.gz \
+ --perGroup \
+ --kmeans 2 \
+ -plotType heatmap \
+ -out ExampleProfile3.png
+
+.. image:: ../../images/test_plots/ExampleProfile4.png
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diff --git a/deepTools/source/docs/index.rst b/deepTools/source/docs/index.rst
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@@ -0,0 +1,72 @@
+=====================================================
+deepTools: *tools for exploring deep sequencing data*
+=====================================================
+
+.. image:: images/start_collage.png
+
+deepTools is a **suite of python tools** particularly developed for the
+efficient analysis of high-throughput sequencing data, such as ChIP-seq, RNA-seq or MNase-seq.
+
+There are 3 ways for using deepTools:
+
+* **Galaxy usage** -- our public `deepTools Galaxy server `_ let's you use the deepTools within the familiar Galaxy framework without the need to master the command line
+* **command line usage** -- simply download and install the tools (see :doc:`content/installation` and :doc:`content/list_of_tools`)
+* **API** -- make use of your favorite deepTools modules in your own python programs (see :doc:`content/api`)
+
+The flow chart below depicts the different tool modules that are
+currently available.
+
+.. image:: images/start_workflow.png
+
+If the file names in the figure mean nothing to you,
+please make sure to check our :doc:`content/help_glossary`.
+
+
+Contents:
+---------
+.. toctree::
+ :maxdepth: 1
+
+ content/installation
+ content/list_of_tools
+ content/advanced_features
+ content/example_usage
+ content/changelog
+ content/help_galaxy_intro
+ content/help_faq
+ content/help_faq_galaxy
+ content/help_glossary
+ content/api
+ content/about
+
+
+While developing deepTools, we continuously strive to create software
+that fulfills the following criteria:
+
+- **efficiently extract reads from BAM files** and perform various
+ computations on them
+- **turn BAM files of aligned reads into bigWig files** using different
+ normalization strategies
+- make use of **multiple processors** (speed!)
+- generation of **highly customizable images** (change colours, size,
+ labels, file format, etc.)
+- enable **customized down-stream analyses**, meaning that every
+ data set created can be stored by the user
+- **modular approach** - compatibility, flexibility, scalability (i.e.
+ we can add more and more modules and make use of established methods)
+
+
+.. tip:: For support or questions please post to `Biostars `__. For bug reports and feature requests please open an issue `on github `__.
+
+
+Please cite deepTools2 as follows:
+
+Ramírez, Fidel, Devon P. Ryan, Björn Grüning, Vivek Bhardwaj, Fabian Kilpert, Andreas S. Richter,
+Steffen Heyne, Friederike Dündar, and Thomas Manke.
+**"deepTools2: a next generation web server for deep-sequencing data analysis." Nucleic Acids Research (2016): gkw257.**
+
+.. image:: images/logo_mpi-ie.jpg
+
+This tool suite is developed by the `Bioinformatics Facility `_ at the
+`Max Planck Institute for Immunobiology and Epigenetics,
+Freiburg `_.
diff --git a/deepTools/source/docs/requirements.txt b/deepTools/source/docs/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f330fe4e6a784435bc624eabac91d40c3664664f
--- /dev/null
+++ b/deepTools/source/docs/requirements.txt
@@ -0,0 +1,4 @@
+sphinx==7.2.6
+mock==5.1.0
+sphinx_rtd_theme==2.0.0
+sphinx-argparse==0.4.0
\ No newline at end of file
diff --git a/deepTools/source/docs/source/_templates/layout.html b/deepTools/source/docs/source/_templates/layout.html
new file mode 100644
index 0000000000000000000000000000000000000000..32e7305372560157e80ff4cc5e577ae70f0ad9d7
--- /dev/null
+++ b/deepTools/source/docs/source/_templates/layout.html
@@ -0,0 +1,3 @@
+{% extends "!layout.html" %}
+{% set script_files = script_files + ["_static/welcome_owl.carousel.min.js"] %}
+{% set css_files = css_files + ["_static/welcome_owl.carousel.css", "_static/welcome_owl.carousel.theme.css"] %}
diff --git a/deepTools/source/docs/source/deeptools.rst b/deepTools/source/docs/source/deeptools.rst
new file mode 100644
index 0000000000000000000000000000000000000000..97d9689941a6b4868a16b3d314c7989f531505f1
--- /dev/null
+++ b/deepTools/source/docs/source/deeptools.rst
@@ -0,0 +1,134 @@
+deeptools package modules
+=========================
+
+
+deeptools.SES_scaleFactor module
+--------------------------------
+
+.. automodule:: deeptools.SES_scaleFactor
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+
+deeptools.bamHandler module
+---------------------------
+
+.. automodule:: deeptools.bamHandler
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+
+deeptools.correlation module
+----------------------------
+
+.. automodule:: deeptools.correlation
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+deeptools.correlation_heatmap module
+------------------------------------
+
+.. automodule:: deeptools.correlation_heatmap
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+deeptools.countReadsPerBin module
+---------------------------------
+
+.. automodule:: deeptools.countReadsPerBin
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+deeptools.getFragmentAndReadSize module
+---------------------------------------
+
+.. automodule:: deeptools.getFragmentAndReadSize
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+deeptools.getRatio module
+-------------------------
+
+.. automodule:: deeptools.getRatio
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+deeptools.getScorePerBigWigBin module
+-------------------------------------
+
+.. automodule:: deeptools.getScorePerBigWigBin
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+deeptools.heatmapper module
+---------------------------
+
+.. automodule:: deeptools.heatmapper
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+deeptools.heatmapper_utilities module
+-------------------------------------
+
+.. automodule:: deeptools.heatmapper_utilities
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+deeptools.mapReduce module
+--------------------------
+
+.. automodule:: deeptools.mapReduce
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+deeptools.sumCoveragePerBin
+---------------------------
+
+.. automodule:: deeptools.sumCoveragePerBin
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+deeptools.utilities module
+--------------------------
+
+.. automodule:: deeptools.utilities
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+deeptools.writeBedGraph module
+------------------------------
+
+.. automodule:: deeptools.writeBedGraph
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+deeptools.writeBedGraph_bam_and_bw module
+-----------------------------------------
+
+.. automodule:: deeptools.writeBedGraph_bam_and_bw
+ :members:
+ :undoc-members:
+ :show-inheritance:
+
+
+Module contents
+---------------
+
+.. automodule:: deeptools
+ :members:
+ :undoc-members:
+ :show-inheritance:
diff --git a/deepTools/source/docs/source/modules.rst b/deepTools/source/docs/source/modules.rst
new file mode 100644
index 0000000000000000000000000000000000000000..06391abc61e1b2f135284851ecae669f9b722967
--- /dev/null
+++ b/deepTools/source/docs/source/modules.rst
@@ -0,0 +1,7 @@
+deeptools
+=========
+
+.. toctree::
+ :maxdepth: 4
+
+ deeptools
diff --git a/deepTools/source/galaxy/workflows/1_BAM_file_TO_Heatmap_of_read_coverages.ga b/deepTools/source/galaxy/workflows/1_BAM_file_TO_Heatmap_of_read_coverages.ga
new file mode 100644
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@@ -0,0 +1,262 @@
+{
+ "a_galaxy_workflow": "true",
+ "annotation": "",
+ "format-version": "0.1",
+ "name": "1 BAM file -->(clustered) Heatmap of read coverages",
+ "steps": {
+ "0": {
+ "annotation": "",
+ "id": 0,
+ "input_connections": {},
+ "inputs": [
+ {
+ "description": "",
+ "name": "file of mapped reads"
+ }
+ ],
+ "name": "Input dataset",
+ "outputs": [],
+ "position": {
+ "left": 257.5,
+ "top": 480
+ },
+ "tool_errors": null,
+ "tool_id": null,
+ "tool_state": "{\"name\": \"file of mapped reads\"}",
+ "tool_version": null,
+ "type": "data_input",
+ "user_outputs": []
+ },
+ "1": {
+ "annotation": "",
+ "id": 1,
+ "input_connections": {},
+ "inputs": [
+ {
+ "description": "",
+ "name": "regions"
+ }
+ ],
+ "name": "Input dataset",
+ "outputs": [],
+ "position": {
+ "left": 583.5,
+ "top": 234
+ },
+ "tool_errors": null,
+ "tool_id": null,
+ "tool_state": "{\"name\": \"regions\"}",
+ "tool_version": null,
+ "type": "data_input",
+ "user_outputs": []
+ },
+ "2": {
+ "annotation": "",
+ "id": 2,
+ "input_connections": {
+ "bamInput": {
+ "id": 0,
+ "output_name": "output"
+ }
+ },
+ "inputs": [
+ {
+ "description": "runtime parameter for tool bamCoverage",
+ "name": "region"
+ },
+ {
+ "description": "runtime parameter for tool bamCoverage",
+ "name": "fragmentLength"
+ }
+ ],
+ "name": "bamCoverage",
+ "outputs": [
+ {
+ "name": "outFileName",
+ "type": "bigwig"
+ }
+ ],
+ "position": {
+ "left": 491.6166687011719,
+ "top": 474.11669921875
+ },
+ "post_job_actions": {
+ "HideDatasetActionoutFileName": {
+ "action_arguments": {},
+ "action_type": "HideDatasetAction",
+ "output_name": "outFileName"
+ },
+ "RenameDatasetActionoutFileName": {
+ "action_arguments": {
+ "newname": "Drosophila coverage file"
+ },
+ "action_type": "RenameDatasetAction",
+ "output_name": "outFileName"
+ }
+ },
+ "tool_errors": null,
+ "tool_id": "toolshed.g2.bx.psu.edu/repos/bgruening/deeptools/deeptools_bamCoverage/1.1.0",
+ "tool_state": "{\"__page__\": 0, \"outFileFormat\": \"\\\"bigwig\\\"\", \"region\": \"{\\\"__class__\\\": \\\"RuntimeValue\\\"}\", \"bamInput\": \"null\", \"binSize\": \"\\\"50\\\"\", \"scaling\": \"{\\\"effectiveGenomeSize\\\": {\\\"effectiveGenomeSize_opt\\\": \\\"121400000\\\", \\\"__current_case__\\\": 3}, \\\"type\\\": \\\"1x\\\", \\\"__current_case__\\\": 2}\", \"__rerun_remap_job_id__\": null, \"advancedOpt\": \"{\\\"ignoreDuplicates\\\": \\\"True\\\", \\\"doNotExtendPairedEnds\\\": \\\"False\\\", \\\"smoothLength\\\": \\\"150\\\", \\\"showAdvancedOpt\\\": \\\"yes\\\", \\\"__current_case__\\\": 1, \\\"minMappingQuality\\\": \\\"10\\\"}\", \"fragmentLength\": \"{\\\"__class__\\\": \\\"RuntimeValue\\\"}\"}",
+ "tool_version": "1.1.0",
+ "type": "tool",
+ "user_outputs": []
+ },
+ "3": {
+ "annotation": "",
+ "id": 3,
+ "input_connections": {
+ "regionsFiles_0|regionsFile": {
+ "id": 1,
+ "output_name": "output"
+ },
+ "scoreFile": {
+ "id": 2,
+ "output_name": "outFileName"
+ }
+ },
+ "inputs": [],
+ "name": "computeMatrix",
+ "outputs": [
+ {
+ "name": "outFileName",
+ "type": "bgzip"
+ },
+ {
+ "name": "outFileNameData",
+ "type": "tabular"
+ },
+ {
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index 0000000000000000000000000000000000000000..51435ad9e018e0e91128958f5fba3c193a5a466a
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diff --git a/deepTools/source/galaxy/workflows/Add_chr_to_first_column_of_a_6_columns_BED_file.ga b/deepTools/source/galaxy/workflows/Add_chr_to_first_column_of_a_6_columns_BED_file.ga
new file mode 100644
index 0000000000000000000000000000000000000000..319e2ab60de52e6727bfaaf4d56bea530ef9b24b
--- /dev/null
+++ b/deepTools/source/galaxy/workflows/Add_chr_to_first_column_of_a_6_columns_BED_file.ga
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diff --git a/deepTools/source/galaxy/workflows/Clustered_heatmap_of_signals_around_the_TSSs__1_bigWig_TO_heatmap.ga b/deepTools/source/galaxy/workflows/Clustered_heatmap_of_signals_around_the_TSSs__1_bigWig_TO_heatmap.ga
new file mode 100644
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+ "format-version": "0.1",
+ "name": "Clustered heatmap of signals around the TSSs (1 bigWig --> heatmap)",
+ "steps": {
+ "0": {
+ "annotation": "",
+ "id": 0,
+ "input_connections": {},
+ "inputs": [
+ {
+ "description": "",
+ "name": "Genes"
+ }
+ ],
+ "name": "Input dataset",
+ "outputs": [],
+ "position": {
+ "left": 175,
+ "top": 186.5
+ },
+ "tool_errors": null,
+ "tool_id": null,
+ "tool_state": "{\"name\": \"Genes\"}",
+ "tool_version": null,
+ "type": "data_input",
+ "user_outputs": []
+ },
+ "1": {
+ "annotation": "",
+ "id": 1,
+ "input_connections": {},
+ "inputs": [
+ {
+ "description": "",
+ "name": "score file"
+ }
+ ],
+ "name": "Input dataset",
+ "outputs": [],
+ "position": {
+ "left": 168,
+ "top": 304.5
+ },
+ "tool_errors": null,
+ "tool_id": null,
+ "tool_state": "{\"name\": \"score file\"}",
+ "tool_version": null,
+ "type": "data_input",
+ "user_outputs": []
+ },
+ "2": {
+ "annotation": "",
+ "id": 2,
+ "input_connections": {
+ "regionsFiles_0|regionsFile": {
+ "id": 0,
+ "output_name": "output"
+ },
+ "scoreFile": {
+ "id": 1,
+ "output_name": "output"
+ }
+ },
+ "inputs": [
+ {
+ "description": "runtime parameter for tool computeMatrix",
+ "name": "mode"
+ },
+ {
+ "description": "runtime parameter for tool computeMatrix",
+ "name": "mode"
+ }
+ ],
+ "name": "computeMatrix",
+ "outputs": [
+ {
+ "name": "outFileName",
+ "type": "bgzip"
+ },
+ {
+ "name": "outFileNameData",
+ "type": "tabular"
+ },
+ {
+ "name": "outFileSortedRegions",
+ "type": "bed"
+ },
+ {
+ "name": "outFileNameMatrix",
+ "type": "tabular"
+ }
+ ],
+ "position": {
+ "left": 490.6166687011719,
+ "top": 485.11669921875
+ },
+ "post_job_actions": {
+ "HideDatasetActionoutFileNameData": {
+ "action_arguments": {},
+ "action_type": "HideDatasetAction",
+ "output_name": "outFileNameData"
+ },
+ "HideDatasetActionoutFileNameMatrix": {
+ "action_arguments": {},
+ "action_type": "HideDatasetAction",
+ "output_name": "outFileNameMatrix"
+ },
+ "HideDatasetActionoutFileSortedRegions": {
+ "action_arguments": {},
+ "action_type": "HideDatasetAction",
+ "output_name": "outFileSortedRegions"
+ }
+ },
+ "tool_errors": null,
+ "tool_id": "toolshed.g2.bx.psu.edu/repos/bgruening/deeptools/deeptools_computeMatrix/1.1.0",
+ "tool_state": "{\"__page__\": 0, \"scoreFile\": \"null\", \"__rerun_remap_job_id__\": null, \"mode\": \"{\\\"afterRegionStartLength\\\": {\\\"__class__\\\": \\\"RuntimeValue\\\"}, \\\"beforeRegionStartLength\\\": {\\\"__class__\\\": \\\"RuntimeValue\\\"}, \\\"nanAfterEnd\\\": \\\"False\\\", \\\"__current_case__\\\": 1, \\\"referencePoint\\\": \\\"TSS\\\", \\\"mode_select\\\": \\\"reference-point\\\"}\", \"output\": \"{\\\"save_matrix\\\": \\\"False\\\", \\\"saveSortedRegions\\\": \\\"True\\\", \\\"showOutputSettings\\\": \\\"yes\\\", \\\"__current_case__\\\": 1, \\\"saveData\\\": \\\"False\\\"}\", \"advancedOpt\": \"{\\\"averageTypeBins\\\": \\\"mean\\\", \\\"maxThreshold\\\": \\\"\\\", \\\"sortUsing\\\": \\\"mean\\\", \\\"skipZeros\\\": \\\"True\\\", \\\"binSize\\\": \\\"50\\\", \\\"showAdvancedOpt\\\": \\\"yes\\\", \\\"scale\\\": \\\"\\\", \\\"__current_case__\\\": 1, \\\"minThreshold\\\": \\\"\\\", \\\"sortRegions\\\": \\\"no\\\", \\\"missingDataAsZero\\\": \\\"True\\\"}\", \"regionsFiles\": \"[{\\\"__index__\\\": 0, \\\"regionsFile\\\": null, \\\"label\\\": \\\"Genes\\\"}]\"}",
+ "tool_version": "1.1.0",
+ "type": "tool",
+ "user_outputs": []
+ },
+ "3": {
+ "annotation": "",
+ "id": 3,
+ "input_connections": {
+ "matrixFile": {
+ "id": 2,
+ "output_name": "outFileName"
+ }
+ },
+ "inputs": [
+ {
+ "description": "runtime parameter for tool heatmapper",
+ "name": "output"
+ },
+ {
+ "description": "runtime parameter for tool heatmapper",
+ "name": "advancedOpt"
+ },
+ {
+ "description": "runtime parameter for tool heatmapper",
+ "name": "advancedOpt"
+ },
+ {
+ "description": "runtime parameter for tool heatmapper",
+ "name": "advancedOpt"
+ }
+ ],
+ "name": "heatmapper",
+ "outputs": [
+ {
+ "name": "outFileName",
+ "type": "png"
+ },
+ {
+ "name": "outFileNameData",
+ "type": "tabular"
+ },
+ {
+ "name": "outFileSortedRegions",
+ "type": "bed"
+ },
+ {
+ "name": "outFileNameMatrix",
+ "type": "tabular"
+ }
+ ],
+ "position": {
+ "left": 834,
+ "top": 487.5
+ },
+ "post_job_actions": {
+ "HideDatasetActionoutFileNameData": {
+ "action_arguments": {},
+ "action_type": "HideDatasetAction",
+ "output_name": "outFileNameData"
+ },
+ "HideDatasetActionoutFileNameMatrix": {
+ "action_arguments": {},
+ "action_type": "HideDatasetAction",
+ "output_name": "outFileNameMatrix"
+ },
+ "HideDatasetActionoutFileSortedRegions": {
+ "action_arguments": {},
+ "action_type": "HideDatasetAction",
+ "output_name": "outFileSortedRegions"
+ },
+ "RenameDatasetActionoutFileName": {
+ "action_arguments": {
+ "newname": "Heatmap of scores around the TSS"
+ },
+ "action_type": "RenameDatasetAction",
+ "output_name": "outFileName"
+ }
+ },
+ "tool_errors": null,
+ "tool_id": "toolshed.g2.bx.psu.edu/repos/bgruening/deeptools/deeptools_heatmapper/1.1.0",
+ "tool_state": "{\"__page__\": 0, \"output\": \"{\\\"outFileFormat\\\": {\\\"__class__\\\": \\\"RuntimeValue\\\"}, \\\"save_matrix\\\": \\\"False\\\", \\\"showOutputSettings\\\": \\\"yes\\\", \\\"saveSortedRegions\\\": \\\"False\\\", \\\"__current_case__\\\": 1, \\\"saveData\\\": \\\"False\\\"}\", \"advancedOpt\": \"{\\\"used_multiple_regions\\\": {\\\"clustering\\\": {\\\"k_kmeans\\\": {\\\"__class__\\\": \\\"RuntimeValue\\\"}, \\\"__current_case__\\\": 0, \\\"clustering_options\\\": \\\"kmeans\\\"}, \\\"used_multiple_regions_options\\\": \\\"no\\\", \\\"__current_case__\\\": 0}, \\\"yAxisLabel\\\": \\\"genes\\\", \\\"xAxisLabel\\\": \\\"distance from TSS (bp)\\\", \\\"zMax\\\": \\\"\\\", \\\"heatmapWidth\\\": \\\"7.5\\\", \\\"sortUsing\\\": \\\"mean\\\", \\\"zMin\\\": \\\"\\\", \\\"plotTitle\\\": {\\\"__class__\\\": \\\"RuntimeValue\\\"}, \\\"averageTypeSummaryPlot\\\": \\\"mean\\\", \\\"whatToShow\\\": \\\"plot, heatmap and colorbar\\\", \\\"missingDataColor\\\": \\\"black\\\", \\\"referencePointLabel\\\": \\\"TSS\\\", \\\"yMin\\\": \\\"\\\", \\\"onePlotPerGroup\\\": \\\"False\\\", \\\"regionsLabel\\\": \\\"genes\\\", \\\"yMax\\\": \\\"\\\", \\\"endLabel\\\": \\\"TES\\\", \\\"sortRegions\\\": {\\\"__class__\\\": \\\"RuntimeValue\\\"}, \\\"startLabel\\\": \\\"TSS\\\", \\\"colorMap\\\": {\\\"__class__\\\": \\\"RuntimeValue\\\"}, \\\"showAdvancedOpt\\\": \\\"yes\\\", \\\"__current_case__\\\": 1, \\\"heatmapHeight\\\": \\\"15.0\\\"}\", \"__rerun_remap_job_id__\": null, \"matrixFile\": \"null\"}",
+ "tool_version": "1.1.0",
+ "type": "tool",
+ "user_outputs": []
+ }
+ }
+}
\ No newline at end of file
diff --git a/deepTools/source/galaxy/workflows/Compute_and_correct_GC_bias.ga b/deepTools/source/galaxy/workflows/Compute_and_correct_GC_bias.ga
new file mode 100644
index 0000000000000000000000000000000000000000..1e1bd0e361b2a944c7bad7839ddb0f8906deb828
--- /dev/null
+++ b/deepTools/source/galaxy/workflows/Compute_and_correct_GC_bias.ga
@@ -0,0 +1,145 @@
+{
+ "a_galaxy_workflow": "true",
+ "annotation": "",
+ "format-version": "0.1",
+ "name": "Compute and correct GC bias",
+ "steps": {
+ "0": {
+ "annotation": "",
+ "id": 0,
+ "input_connections": {},
+ "inputs": [
+ {
+ "description": "",
+ "name": "BAM file to be checked for GC bias"
+ }
+ ],
+ "name": "Input dataset",
+ "outputs": [],
+ "position": {
+ "left": 148,
+ "top": 505.88330078125
+ },
+ "tool_errors": null,
+ "tool_id": null,
+ "tool_state": "{\"name\": \"BAM file to be checked for GC bias\"}",
+ "tool_version": null,
+ "type": "data_input",
+ "user_outputs": []
+ },
+ "1": {
+ "annotation": "",
+ "id": 1,
+ "input_connections": {
+ "bamInput": {
+ "id": 0,
+ "output_name": "output"
+ }
+ },
+ "inputs": [
+ {
+ "description": "runtime parameter for tool computeGCBias",
+ "name": "region"
+ },
+ {
+ "description": "runtime parameter for tool computeGCBias",
+ "name": "source"
+ },
+ {
+ "description": "runtime parameter for tool computeGCBias",
+ "name": "effectiveGenomeSize"
+ },
+ {
+ "description": "runtime parameter for tool computeGCBias",
+ "name": "image_format"
+ },
+ {
+ "description": "runtime parameter for tool computeGCBias",
+ "name": "fragmentLength"
+ }
+ ],
+ "name": "computeGCBias",
+ "outputs": [
+ {
+ "name": "outFileName",
+ "type": "tabular"
+ },
+ {
+ "name": "outImageName",
+ "type": "png"
+ }
+ ],
+ "position": {
+ "left": 404.61669921875,
+ "top": 200
+ },
+ "post_job_actions": {
+ "HideDatasetActionoutFileName": {
+ "action_arguments": {},
+ "action_type": "HideDatasetAction",
+ "output_name": "outFileName"
+ }
+ },
+ "tool_errors": null,
+ "tool_id": "toolshed.g2.bx.psu.edu/repos/bgruening/deeptools/deeptools_computeGCBias/1.1.0",
+ "tool_state": "{\"__page__\": 0, \"region\": \"{\\\"__class__\\\": \\\"RuntimeValue\\\"}\", \"bamInput\": \"null\", \"source\": \"{\\\"input1_2bit\\\": {\\\"__class__\\\": \\\"RuntimeValue\\\"}, \\\"ref_source\\\": \\\"cached\\\", \\\"__current_case__\\\": 0}\", \"__rerun_remap_job_id__\": null, \"effectiveGenomeSize\": \"{\\\"effectiveGenomeSize_opt\\\": \\\"specific\\\", \\\"effectiveGenomeSize\\\": {\\\"__class__\\\": \\\"RuntimeValue\\\"}, \\\"__current_case__\\\": 0}\", \"advancedOpt\": \"{\\\"filterOut\\\": null, \\\"extraSampling\\\": null, \\\"showAdvancedOpt\\\": \\\"yes\\\", \\\"sampleSize\\\": \\\"50000000\\\", \\\"__current_case__\\\": 1, \\\"regionSize\\\": \\\"300\\\"}\", \"image_format\": \"{\\\"__class__\\\": \\\"RuntimeValue\\\"}\", \"fragmentLength\": \"{\\\"__class__\\\": \\\"RuntimeValue\\\"}\"}",
+ "tool_version": "1.1.0",
+ "type": "tool",
+ "user_outputs": []
+ },
+ "2": {
+ "annotation": "",
+ "id": 2,
+ "input_connections": {
+ "GCbiasFrequenciesFile": {
+ "id": 1,
+ "output_name": "outFileName"
+ },
+ "bamInput": {
+ "id": 0,
+ "output_name": "output"
+ }
+ },
+ "inputs": [
+ {
+ "description": "runtime parameter for tool correctGCBias",
+ "name": "region"
+ },
+ {
+ "description": "runtime parameter for tool correctGCBias",
+ "name": "source"
+ },
+ {
+ "description": "runtime parameter for tool correctGCBias",
+ "name": "effectiveGenomeSize"
+ }
+ ],
+ "name": "correctGCBias",
+ "outputs": [
+ {
+ "name": "outFileName",
+ "type": "bam"
+ }
+ ],
+ "position": {
+ "left": 734.61669921875,
+ "top": 462
+ },
+ "post_job_actions": {
+ "RenameDatasetActionoutFileName": {
+ "action_arguments": {
+ "newname": "GCcorrected BAM file"
+ },
+ "action_type": "RenameDatasetAction",
+ "output_name": "outFileName"
+ }
+ },
+ "tool_errors": null,
+ "tool_id": "toolshed.g2.bx.psu.edu/repos/bgruening/deeptools/deeptools_correctGCBias/1.1.0",
+ "tool_state": "{\"__page__\": 0, \"region\": \"{\\\"__class__\\\": \\\"RuntimeValue\\\"}\", \"bamInput\": \"null\", \"source\": \"{\\\"input1_2bit\\\": {\\\"__class__\\\": \\\"RuntimeValue\\\"}, \\\"ref_source\\\": \\\"cached\\\", \\\"__current_case__\\\": 0}\", \"effectiveGenomeSize\": \"{\\\"effectiveGenomeSize_opt\\\": \\\"specific\\\", \\\"effectiveGenomeSize\\\": {\\\"__class__\\\": \\\"RuntimeValue\\\"}, \\\"__current_case__\\\": 0}\", \"GCbiasFrequenciesFile\": \"null\", \"advancedOpt\": \"{\\\"showAdvancedOpt\\\": \\\"yes\\\", \\\"__current_case__\\\": 1, \\\"binSize\\\": \\\"50\\\"}\", \"__rerun_remap_job_id__\": null}",
+ "tool_version": "1.1.0",
+ "type": "tool",
+ "user_outputs": []
+ }
+ }
+}
\ No newline at end of file
diff --git a/deepTools/source/galaxy/workflows/Remove_chr_from_the_beginning_of_genomic_interval_files.ga b/deepTools/source/galaxy/workflows/Remove_chr_from_the_beginning_of_genomic_interval_files.ga
new file mode 100644
index 0000000000000000000000000000000000000000..be1da25717ba15ddfca33292b35c72fe2f1c1334
--- /dev/null
+++ b/deepTools/source/galaxy/workflows/Remove_chr_from_the_beginning_of_genomic_interval_files.ga
@@ -0,0 +1,68 @@
+{
+ "a_galaxy_workflow": "true",
+ "annotation": "",
+ "format-version": "0.1",
+ "name": "Remove 'chr' from the beginning of genomic interval files",
+ "steps": {
+ "0": {
+ "annotation": "",
+ "id": 0,
+ "input_connections": {},
+ "inputs": [
+ {
+ "description": "",
+ "name": "any column-based file of genomic intervals"
+ }
+ ],
+ "name": "Input dataset",
+ "outputs": [],
+ "position": {
+ "left": 268.5,
+ "top": 182.5
+ },
+ "tool_errors": null,
+ "tool_id": null,
+ "tool_state": "{\"name\": \"any column-based file of genomic intervals\"}",
+ "tool_version": null,
+ "type": "data_input",
+ "user_outputs": []
+ },
+ "1": {
+ "annotation": "",
+ "id": 1,
+ "input_connections": {
+ "input1": {
+ "id": 0,
+ "output_name": "output"
+ }
+ },
+ "inputs": [],
+ "name": "Trim",
+ "outputs": [
+ {
+ "name": "out_file1",
+ "type": "input"
+ }
+ ],
+ "position": {
+ "left": 552,
+ "top": 168
+ },
+ "post_job_actions": {
+ "RenameDatasetActionout_file1": {
+ "action_arguments": {
+ "newname": "removed \"chr\""
+ },
+ "action_type": "RenameDatasetAction",
+ "output_name": "out_file1"
+ }
+ },
+ "tool_errors": null,
+ "tool_id": "trimmer",
+ "tool_state": "{\"__page__\": 0, \"input1\": \"null\", \"end\": \"\\\"0\\\"\", \"fastq\": \"\\\"\\\"\", \"ignore\": \"\\\"35\\\"\", \"start\": \"\\\"4\\\"\", \"__rerun_remap_job_id__\": null, \"chromInfo\": \"\\\"/data2/galaxy/galaxy-dist/tool-data/shared/ucsc/chrom/mm9.len\\\"\", \"col\": \"\\\"1\\\"\"}",
+ "tool_version": "0.0.1",
+ "type": "tool",
+ "user_outputs": []
+ }
+ }
+}
\ No newline at end of file
diff --git a/deepTools/source/galaxy/workflows/readme.rst b/deepTools/source/galaxy/workflows/readme.rst
new file mode 100644
index 0000000000000000000000000000000000000000..36d9a37d3b85816ccba461cb47fbb8e2d9ea91b4
--- /dev/null
+++ b/deepTools/source/galaxy/workflows/readme.rst
@@ -0,0 +1,41 @@
+This package contains a collection of Galaxy workflows utilising deepTools.
+
+See http://www.galaxyproject.org for information about the Galaxy Project.
+
+
+Sample Data
+===========
+
+Sample data can be obtained from http://deeptools.ie-freiburg.mpg.de/library or you
+can use aligned reads in BAM or SAM format.
+
+
+
+Citation
+========
+
+If you use this workflow directly, or a derivative of it, or the associated
+deepTools wrappers for Galaxy, in work leading to a scientific publication,
+please cite:
+
+{publication under review}
+
+
+Availability
+============
+
+This workflow is available on the main Galaxy Tool Shed:
+
+http://toolshed.g2.bx.psu.edu/view/bgruening/deeptools_workflows
+
+Development is being done on github:
+
+https://github.com/deeptools/deepTools
+
+
+Dependencies
+============
+
+These dependencies should be resolved automatically via the Galaxy Tool Shed:
+
+* http://toolshed.g2.bx.psu.edu/view/bgruening/deeptools
diff --git a/deepTools/source/galaxy/workflows/repository_dependencies.xml b/deepTools/source/galaxy/workflows/repository_dependencies.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ad24bb9bb69b8002ecda0dc328133a7a9641a275
--- /dev/null
+++ b/deepTools/source/galaxy/workflows/repository_dependencies.xml
@@ -0,0 +1,4 @@
+
+
+
+
diff --git a/deepTools/source/galaxy/wrapper/.shed.yml b/deepTools/source/galaxy/wrapper/.shed.yml
new file mode 100644
index 0000000000000000000000000000000000000000..54795afc6bd7a9bce213162f5dfeba1e467750b5
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/.shed.yml
@@ -0,0 +1,55 @@
+categories:
+- ChIP-seq
+- Convert Formats
+- Sequence Analysis
+- Visualization
+description: "deepTools: User-friendly tools for the normalization, quality control and visualization of deep-sequencing data"
+long_description: |
+ deepTools address the challenge of visualizing the large amounts of data that
+ are now routinely generated from sequencing centers in a meaningful way. To do so,
+ deepTools contain useful routines to process the mapped reads data through removal of
+ duplicates and different filtering options to create coverage files in standard
+ bedGraph and bigWig file formats. deepTools allow the creation of normalized
+ coverage files or the comparison between two files (for example, treatment and control).
+
+ Finally, using such normalized and standardized files, multiple visualizations can be
+ created to identify enrichments with functional annotations of the genome.
+ For a gallery of images that can be produced and a description of the tools see http://f1000.com/posters/browse/summary/1094053
+
+ https://github.com/deeptools/deepTools
+ doi: 10.1093/nar/gku365
+ Wikipage: https://github.com/deeptools/deepTools/wiki
+
+ Repository-Maintainer: Björn Grüning
+
+ https://github.com/deeptools/deepTools
+name: deeptools
+owner: bgruening
+remote_repository_url: https://github.com/deeptools/deepTools/tree/master/galaxy/wrapper/
+type: unrestricted
+auto_tool_repositories:
+ name_template: "{{ tool_id }}"
+ description_template: "Wrapper for the deepTools: {{ tool_name }}"
+suite:
+ name: "suite_deeptools"
+ description: "deepTools: User-friendly tools for the normalization, quality control and visualization of deep-sequencing data"
+ long_description: |
+ deepTools address the challenge of visualizing the large amounts of data that
+ are now routinely generated from sequencing centers in a meaningful way. To do so,
+ deepTools contain useful routines to process the mapped reads data through removal of
+ duplicates and different filtering options to create coverage files in standard
+ bedGraph and bigWig file formats. deepTools allow the creation of normalized
+ coverage files or the comparison between two files (for example, treatment and control).
+
+ Finally, using such normalized and standardized files, multiple visualizations can be
+ created to identify enrichments with functional annotations of the genome.
+ For a gallery of images that can be produced and a description of the tools see http://f1000.com/posters/browse/summary/1094053
+
+ https://github.com/deeptools/deepTools
+ doi: 10.1093/nar/gku365
+ Wikipage: https://github.com/deeptools/deepTools/wiki
+
+ Repository-Maintainer: Björn Grüning
+
+ https://github.com/deeptools/deepTools
+
diff --git a/deepTools/source/galaxy/wrapper/alignmentSieve.xml b/deepTools/source/galaxy/wrapper/alignmentSieve.xml
new file mode 100644
index 0000000000000000000000000000000000000000..f893d24c3fc6599b0cd8b3731194ade499439666
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/alignmentSieve.xml
@@ -0,0 +1,216 @@
+
+ Filter BAM/CRAM files according to specified parameters
+
+ alignmentSieve
+ deepTools_macros.xml
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ filterMetrics is True
+
+
+ filteredOutReads is True
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ read 1
+ read 2 <----
+
+ ------------------------ fragment
+
+ -------------------------------- shifted fragment
+
+The same results will be produced if read 1 and read 2 are swapped. If, instead, the protocol is strand-specific, then the first set of integers in a pair would be applied to fragments where read 1 precedes read 2, and the second set to cases where read 2 precedes read 1. In this case, the first value in each pair is applied to the end of read 1 and the second to the end of read 2. For example, suppose "-5 3 -1 4" were given as the option to ``--shift``. The ``-5 3`` set would produce the following::
+
+ ----> read 1
+ read 2 <----
+
+ ------------------------ fragment
+
+ -------------------------------- shifted fragment
+
+and the ``-1 4`` set would produce the following::
+
+ ----> read 2
+ read 1 <----
+
+ ------------------------ fragment
+
+ --------------------- shifted fragment
+
+As can be seen, such fragments are considered to be on the ``-`` strand, so negative values then shift to the left on its frame of reference (thus, to the right relative to the ``+`` strand).
+
+-----
+
+@REFERENCES@
+]]>
+
+
+
diff --git a/deepTools/source/galaxy/wrapper/bamCompare.xml b/deepTools/source/galaxy/wrapper/bamCompare.xml
new file mode 100644
index 0000000000000000000000000000000000000000..604774d6c15a0bcce560fb7c6c8fe24eff5811b6
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/bamCompare.xml
@@ -0,0 +1,252 @@
+
+ normalizes and compares two BAM or CRAM files to obtain the ratio, log2ratio or difference between them
+
+ bamCompare
+ deepTools_macros.xml
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+`_
+
+.. image:: $PATH_TO_IMAGES/bamCompare_output.png
+ :width: 600
+ :height: 436
+
+-----
+
+@REFERENCES@
+]]>
+
+
+
diff --git a/deepTools/source/galaxy/wrapper/bamCoverage.xml b/deepTools/source/galaxy/wrapper/bamCoverage.xml
new file mode 100644
index 0000000000000000000000000000000000000000..8150bfdc617783b1ad46472e32973fa8874e5873
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/bamCoverage.xml
@@ -0,0 +1,291 @@
+
+ generates a coverage bigWig file from a given BAM or CRAM file
+
+ bamCoverage
+ deepTools_macros.xml
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+`_
+
+.. image:: $PATH_TO_IMAGES/bamCoverage_output.png
+ :width: 600
+ :height: 450
+
+Usage hints
+------------
+
+* A smaller ``bin size`` value will result in a higher resolution of the coverage track but also in a larger file size.
+* The ``1x normalization`` (RPGC) requires the input of a value for the **effective genome size**, which is the mappable part of the reference genome. Of course, this value is species-specific.
+* It might be useful for some studies to exclude certain chromosomes in order to avoid biases, e.g. chromosome X for many mammals where the males contain a pair of each autosome, but often only a single X chromosome.
+* By default, the read length is **NOT** extended! This is the preferred setting for **spliced-read** data like RNA-seq, where one usually wants to rely on the detected read locations only. A read extension would neglect potential splice sites in the unmapped part of the fragment.
+ Other data, e.g. ChIP-seq, where fragments are known to map contiuously, should be processed with read extension (``--extendReads [INT]``).
+* For paired-end data, the fragment length is generally defined by the two read mates. The user-provided fragment length is only used as a fallback for singletons or mate reads that map too far apart (with a distance greater than four times the fragment length or if the mates are located on different chromosomes).
+
+WARNING: If you already normalized for GC bias using ``correctGCbias``, you should absolutely **NOT** set the parameter ``--ignoreDuplicates``!
+
+
+-----
+
+@REFERENCES@
+]]>
+
+
+
diff --git a/deepTools/source/galaxy/wrapper/bamPEFragmentSize.xml b/deepTools/source/galaxy/wrapper/bamPEFragmentSize.xml
new file mode 100644
index 0000000000000000000000000000000000000000..1566654091c357353460d7adf9f61cfe54576eb3
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/bamPEFragmentSize.xml
@@ -0,0 +1,164 @@
+
+ Estimate the predominant cDNA fragment length from paired-end sequenced BAM/CRAM files
+
+ bamPEFragmentSize
+ deepTools_macros.xml
+
+
+
+ '$outfile'
+]]>
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ histogram is True
+
+
+
+
+
+
+
+
+ table is True
+
+
+ outRawFragmentLengths is True
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/deepTools/source/galaxy/wrapper/bigwigAverage.xml b/deepTools/source/galaxy/wrapper/bigwigAverage.xml
new file mode 100644
index 0000000000000000000000000000000000000000..ffb5536a4e16b26e75243b6eb26ea35f82b24334
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/bigwigAverage.xml
@@ -0,0 +1,122 @@
+
+ normalizes and average multiple bigWig files
+
+ bigwigAverage
+ deepTools_macros.xml
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/deepTools/source/galaxy/wrapper/bigwigCompare.xml b/deepTools/source/galaxy/wrapper/bigwigCompare.xml
new file mode 100644
index 0000000000000000000000000000000000000000..914b05c602f97938ef0adce47e1ae08427e978f1
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/bigwigCompare.xml
@@ -0,0 +1,157 @@
+
+ normalizes and compares two bigWig files to obtain the ratio, log2ratio or difference between them
+
+ bigwigCompare
+ deepTools_macros.xml
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/deepTools/source/galaxy/wrapper/computeGCBias.xml b/deepTools/source/galaxy/wrapper/computeGCBias.xml
new file mode 100644
index 0000000000000000000000000000000000000000..fc99e02de649bf7ee35599ef762be518c0f67ee5
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/computeGCBias.xml
@@ -0,0 +1,192 @@
+
+ Determine the GC bias of your sequenced reads
+
+ computeGCBias
+ deepTools_macros.xml
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ ((
+ image_format != 'none'
+ ))
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+`_.
+The basic assumption of the GC bias diagnosis is that an ideal sample should show a uniform distribution of sequenced reads across the genome, i.e. all regions of the genome should have similar numbers of reads, regardless of their base-pair composition.
+In reality, the DNA polymerases used for PCR-based amplifications during the library preparation of the sequencing protocols prefer GC-rich regions. This will influence the outcome of the sequencing as there will be more reads for GC-rich regions just because of the DNA polymerase's preference.
+
+``computeGCbias`` will first calculate the **expected GC profile** by counting the number of DNA fragments of a fixed size per GC fraction where GC fraction is defined as the number of G's or C's in a genome region of a given length.
+The result is basically a histogram depicting the frequency of DNA fragments for each type of genome region with a GC fraction between 0 to 100 percent. This will be different for each reference genome, but is independent of the actual sequencing experiment.
+
+The profile of the expected DNA fragment distribution is then compared to the **observed GC profile**, which is generated by counting the number of sequenced reads per GC fraction.
+
+In an ideal experiment, the observed GC profile would, of course, look like the expected profile.
+This is indeed the case when applying ``computeGCBias`` to simulated reads.
+
+.. _computeGCBias_example_image:
+
+.. image:: $PATH_TO_IMAGES/GC_bias_simulated_reads_2L.png
+
+As you can see, both plots based on **simulated reads** do not show enrichments or depletions for specific GC content bins, there is an almost flat line around the log2ratio of 0 (= ratio(observed/expected) of 1). The fluctuations on the ends of the x axis are due to the fact that only very, very few regions in the *Drosophila* genome have such extreme GC fractions so that the number of fragments that are picked up in the random sampling can vary.
+
+Now, let's have a look at **real-life data** from genomic DNA sequencing. Panels A and B can be clearly distinguished and the major change that took place between the experiments underlying the plots was that the samples in panel A were prepared with too many PCR cycles and a standard polymerase whereas the samples of panel B were subjected to very few rounds of amplification using a high fidelity DNA polymerase.
+
+.. image:: $PATH_TO_IMAGES/QC_GCplots_input.png
+ :width: 600
+ :height: 452
+
+**Note:** The expected GC profile depends on the reference genome as different organisms have very different GC contents. For example, one would expect more fragments with GC fractions between 30% to 60% in mouse samples (average GC content of the mouse genome: 45 %) than for genome fragments from, for example, *Plasmodium falciparum* (average genome GC content *P. falciparum*: 20%).
+
+For more details, for example about when to exclude regions from the read distribution calculation, go `here `_
+
+
+-----
+
+@REFERENCES@
+]]>
+
+
+
diff --git a/deepTools/source/galaxy/wrapper/computeMatrix.xml b/deepTools/source/galaxy/wrapper/computeMatrix.xml
new file mode 100644
index 0000000000000000000000000000000000000000..d6a82f79083528287d0a46d6e7ef60c7b7d13859
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/computeMatrix.xml
@@ -0,0 +1,337 @@
+
+ prepares data for plotting a heatmap or a profile of given regions
+
+ computeMatrix
+ deepTools_macros.xml
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+`_.
+
+The intermediate file produced by ``computeMatrix`` is meant to be used with ``plotHeatmap`` and ``plotProfile``.
+See the descriptions of ``plotHeatmap`` and ``plotProfile`` for example plots.
+
+.. image:: $PATH_TO_IMAGES/computeMatrix_overview.png
+ :alt: Relationship between computeMatrix, heatmapper and profiler
+ :width: 600
+ :height: 418
+
+=======
+
+Usage hints
+-------------
+
+The supplied genomic regions can really be anything - genes, parts of genes, ChIP-seq peaks, favorite genome regions... as long as you provide a proper file
+in BED or INTERVAL format. If you would like to compare different groups of regions (e.g., genes from chromosome 2 and 3), you can supply more than 1 regions file, one for each group by selecting "Insert Select regions".
+
+.. image:: $PATH_TO_IMAGES/computeMatrix_selectRegions.png
+ :width: 600
+ :height: 150
+
+You can select as many score (bigWig) files as you like. Simply use the Shift and/or Command key while clicking on the files of interest.
+
+.. image:: $PATH_TO_IMAGES/computeMatrix_selectScores.png
+ :width: 600
+ :height: 136
+
+The multitude of parameters can seem daunting at first - here are the options that we tend to tune most often:
+
+* ``bin Size`` -- The default value works well most of the time, but if you want to have a more finely grained image, decrease the default value (but not smaller than your bigWig file(s)' bin size). If you want to reduce the computation time, increase it.
+* ``Skip zeros`` -- useful to avoid completely blank lines in the heatmap.
+* ``Convert missing values to 0?`` -- If you want to identify clusters of similar regions in an unsupervised fashion using ``plotHeatmap`` and/or ``plotProfile``, you should definitely set this to 'yes'.
+
+
+Output files
+---------------
+
+The default output is a **gzipped table of values** that is used by both ``plotHeatmap`` and ``plotProfile``.
+
+The optional output files include a) the **regions after sorting and filtering (if selected)** as they were used to calculate the values for the plotting, and b) the uncompressed table that **underlies the heatmap**.
+
+**TIP:** ``computeMatrix`` can also be used to filter and sort regions according to their score by making use of the "advanced output settings".
+
+.. image:: $PATH_TO_IMAGES/computeMatrix_advancedOutput.png
+ :width: 600
+ :height: 189
+
+.. image:: $PATH_TO_IMAGES/computeMatrix_output.png
+ :width: 600
+ :height: 297
+
+Note that these advanced output options are available for ``plotHeatmap`` and ``plotProfile``, too.
+
+See the following table for the optional output options:
+
++-----------------------------------+--------------------+-----------------+-----------------+
+| **optional output type** | **computeMatrix** | **plotHeatmap** | **plotProfile** |
++-----------------------------------+--------------------+-----------------+-----------------+
+| values underlying the heatmap | yes | yes | no |
++-----------------------------------+--------------------+-----------------+-----------------+
+| values underlying the profile | no | no | yes |
++-----------------------------------+--------------------+-----------------+-----------------+
+| sorted and/or filtered regions | yes | yes | yes |
++-----------------------------------+--------------------+-----------------+-----------------+
+
+**More examples** can be found in our `Gallery `_.
+
+-----
+
+@REFERENCES@
+]]>
+
+
+
diff --git a/deepTools/source/galaxy/wrapper/computeMatrixOperations.xml b/deepTools/source/galaxy/wrapper/computeMatrixOperations.xml
new file mode 100644
index 0000000000000000000000000000000000000000..9366508610a57606491a9f9928911fd0032fe543
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/computeMatrixOperations.xml
@@ -0,0 +1,229 @@
+
+ Modify or combine the output of computeMatrix in a variety of ways.
+
+ computeMatrixOperations
+ deepTools_macros.xml
+
+
+
+ $outFileTxt
+ #else if $submodule.command == "relabel":
+ relabel
+ -m $submodule.matrixFile
+ #if $submodule.groupLabels is not None and str($submodule.groupLabels) != '':
+ --groupLabels $submodule.groupLabels
+ #end if
+ #if $submodule.sampleLabels is not None and str($submodule.sampleLabels) != '':
+ --sampleLabels $submodule.sampleLabels
+ #end if
+ #else if $submodule.command == "subset":
+ subset
+ -m $submodule.matrixFile
+ #if $submodule.groups is not None and str($submodule.groups) != '':
+ --groups $submodule.groups
+ #end if
+ #if $submodule.samples is not None and str($submodule.samples) != '':
+ --samples $submodule.samples
+ #end if
+ -o $outFileName
+ #else if $submodule.command == "filterStrand":
+ filterStrand
+ -m $submodule.matrixFile
+ --strand $submodule.strand
+ -o $outFileName
+ #else if $submodule.command == "filterValues":
+ filterValues
+ -m $submodule.matrixFile
+ #if $submodule.minValue is not None and str($submodule.minValue) != '':
+ --min $submodule.minValue
+ #end if
+ #if $submodule.maxValue is not None and str($submodule.maxValue) != '':
+ --max $submodule.maxValue
+ #end if
+ -o $outFileName
+ #else if $submodule.command == "rbind":
+ #set $files=[]
+ #for $f in $submodule.matrixFiles:
+ #silent $files.append(str($f.matrixFile))
+ #end for
+ rbind
+ -m '#echo "' '".join($files)#'
+ -o $outFileName
+ #else if $submodule.command == "cbind":
+ cbind
+ #set $files=[]
+ #for $f in $submodule.matrixFiles:
+ #silent $files.append(str($f.matrixFile))
+ #end for
+ -m '#echo "' '".join($files)#'
+ -o $outFileName
+ #else if $submodule.command == "sort":
+ sort
+ #set $files=[]
+ #for $f in $submodule.regionsFiles:
+ #silent $files.append(str($f.regionsFile))
+ #end for
+ -m $submodule.matrixFile
+ -R '#echo "' '".join($files)#'
+ -o $outFileName
+ #else if $submodule.command == "dataRange":
+ dataRange
+ -m $submodule.matrixFile
+ > $outFileTxt
+ #end if
+]]>
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ ((
+ submodule['command'] != "info"
+ ))
+
+
+
+
+ ((
+ submodule['command'] == "info"
+ ))
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/deepTools/source/galaxy/wrapper/correctGCBias.xml b/deepTools/source/galaxy/wrapper/correctGCBias.xml
new file mode 100644
index 0000000000000000000000000000000000000000..4d55b6c0fab1e5370b99979cfb90f4dd43d51e61
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/correctGCBias.xml
@@ -0,0 +1,83 @@
+
+ uses the output from computeGCBias to generate GC-corrected BAM/CRAM files
+
+ correctGCBias
+ deepTools_macros.xml
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/deepTools/source/galaxy/wrapper/deepTools_macros.xml b/deepTools/source/galaxy/wrapper/deepTools_macros.xml
new file mode 100644
index 0000000000000000000000000000000000000000..62dd96ee9f03e8cc93b23004bf9df5f91791a5be
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/deepTools_macros.xml
@@ -0,0 +1,946 @@
+
+
+ --numberOfProcessors "\${GALAXY_SLOTS:-4}"
+ 3.5.6
+ 22.05
+
+
+ deeptools
+ samtools
+
+
+ @BINARY@ --version
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ --plotWidth '$advancedOpt.plotWidth'
+ --plotHeight '$advancedOpt.plotHeight'
+
+
+
+
+
+
+
+
+
+ #if $advancedOpt.doExtendCustom.doExtend == 'custom':
+ --extendReads $advancedOpt.doExtendCustom.extendReadsValue
+ #else if $advancedOpt.doExtendCustom.doExtend == 'yes':
+ --extendReads
+ #end if
+ $advancedOpt.ignoreDuplicates
+ $advancedOpt.centerReads
+ #if $advancedOpt.minMappingQuality:
+ --minMappingQuality '$advancedOpt.minMappingQuality'
+ #end if
+ #if $advancedOpt.samFlagInclude:
+ --samFlagInclude $advancedOpt.samFlagInclude
+ #end if
+ #if $advancedOpt.samFlagExclude:
+ --samFlagExclude $advancedOpt.samFlagExclude
+ #end if
+ #if $advancedOpt.minFragmentLength:
+ --minFragmentLength $advancedOpt.minFragmentLength
+ #end if
+ #if $advancedOpt.maxFragmentLength:
+ --maxFragmentLength $advancedOpt.maxFragmentLength
+ #end if
+
+
+
+ $advancedOpt.metagene
+ #if $advancedOpt.transcriptID:
+ --transcriptID $advancedOpt.transcriptID
+ #end if
+ #if $advancedOpt.exonID:
+ --exonID $advancedOpt.exonID
+ #end if
+ #if $advancedOpt.transcript_id_designator:
+ --transcript_id_designator $advancedOpt.transcript_id_designator
+ #end if
+
+
+
+
+
+
+
+
+
+
+ #if str($plotting_type.zMin) != "":
+ --zMin $plotting_type.zMin
+ #end if
+ #if str($plotting_type.zMax) != "":
+ --zMax $plotting_type.zMax
+ #end if
+ --colorMap '$plotting_type.colorMap'
+ $plotting_type.plotNumbers
+ --plotTitle '$plotting_type.plotTitle'
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ #if $advancedOpt.used_multiple_regions.used_multiple_regions_options == 'no':
+ #if $advancedOpt.used_multiple_regions.clustering.clustering_options == 'kmeans':
+ #if int($advancedOpt.used_multiple_regions.clustering.k_kmeans) > 0:
+ --kmeans $advancedOpt.used_multiple_regions.clustering.k_kmeans
+ #end if
+ #end if
+ #if $advancedOpt.used_multiple_regions.clustering.clustering_options == 'hclust':
+ #if int($advancedOpt.used_multiple_regions.clustering.n_hclust) > 0:
+ --hclust $advancedOpt.used_multiple_regions.clustering.n_hclust
+ #end if
+ #end if
+ $advancedOpt.used_multiple_regions.silhouette
+ #end if
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. class:: infomark
+
+For more information on the tools, please visit our `help site`_.
+
+For support or questions please post to `Biostars`_. For bug reports and feature requests please open an issue `on github`_.
+
+This tool is developed by the `Bioinformatics and Deep-Sequencing Unit`_ at the `Max Planck Institute for Immunobiology and Epigenetics`_.
+
+.. _Biostars: http://biostars.org
+.. _on github: http://github.com
+.. _Bioinformatics and Deep-Sequencing Unit: http://www.ie-freiburg.mpg.de/bioinformaticsfac
+.. _Max Planck Institute for Immunobiology and Epigenetics: http://www3.ie-freiburg.mpg.de
+.. _help site: https://deeptools.readthedocs.org/
+
+
+
+
+ 10.1093/nar/gkw257
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ [A-Za-z0-9 =-_/+]+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 0:
+ --BED #echo ' '.join($files)#
+ --regionLabels #echo ' '.join($labels)#
+ #end if
+]]>
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ #if $source.ref_source=="history":
+ --genome $source.input1
+ #else:
+ --genome '$source.input1_2bit.fields.path'
+ #end if
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ ((
+ output['showOutputSettings'] == 'yes' and
+ output['saveMatrix'] is True
+ ))
+
+
+
+
+
+
+
+ ((
+ output['showOutputSettings'] == 'yes' and
+ output['saveSortedRegions'] is True
+ ))
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/deepTools/source/galaxy/wrapper/estimateReadFiltering.xml b/deepTools/source/galaxy/wrapper/estimateReadFiltering.xml
new file mode 100644
index 0000000000000000000000000000000000000000..925e8d03680d7da7cc19ad3f59a6a0bfc403acce
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/estimateReadFiltering.xml
@@ -0,0 +1,117 @@
+
+ estimates the number of reads that would be filtered given certain criteria
+
+ estimateReadFiltering
+ deepTools_macros.xml
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/deepTools/source/galaxy/wrapper/multiBamSummary.xml b/deepTools/source/galaxy/wrapper/multiBamSummary.xml
new file mode 100644
index 0000000000000000000000000000000000000000..dc1a504c40071de156a22e321dd78dbbb7259346
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/multiBamSummary.xml
@@ -0,0 +1,146 @@
+
+ calculates average read coverages for a list of two or more BAM/CRAM files
+
+ multiBamSummary
+ deepTools_macros.xml
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ outRawCounts is True
+
+
+ scalingFactors is True
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/deepTools/source/galaxy/wrapper/multiBigwigSummary.xml b/deepTools/source/galaxy/wrapper/multiBigwigSummary.xml
new file mode 100644
index 0000000000000000000000000000000000000000..cbcdde3a0cd769c48e8c95f5326edcba42c0d42d
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/multiBigwigSummary.xml
@@ -0,0 +1,138 @@
+
+ calculates average scores for a list of two or more bigwig files
+
+ multiBigwigSummary
+ deepTools_macros.xml
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ outRawCounts is True
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/deepTools/source/galaxy/wrapper/plotCorrelation.xml b/deepTools/source/galaxy/wrapper/plotCorrelation.xml
new file mode 100644
index 0000000000000000000000000000000000000000..d8c606401ea30cdcd78f030589d18d884ed2049c
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/plotCorrelation.xml
@@ -0,0 +1,181 @@
+
+ Create a heatmap or scatterplot of correlation scores between different samples
+
+ plotCorrelation
+ deepTools_macros.xml
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ outFileCorMatrix is True
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ "Data Library").
+
+Average coverages were computed over 10 kb bins for chromosome X,
+from bigWig files using ``multiBigwigSummary``. This was then used with ``plotCorrelation`` to make a heatmap of Spearman correlation coefficients.
+
+.. image:: $PATH_TO_IMAGES/plotCorrelation_galaxy_bw_heatmap_output.png
+ :width: 600
+ :height: 518
+
+The scatterplot could look like this:
+
+.. image:: $PATH_TO_IMAGES/plotCorrelation_scatterplot_PearsonCorr_bigwigScores.png
+ :width: 600
+ :height: 600
+
+-----
+
+@REFERENCES@
+]]>
+
+
+
diff --git a/deepTools/source/galaxy/wrapper/plotCoverage.xml b/deepTools/source/galaxy/wrapper/plotCoverage.xml
new file mode 100644
index 0000000000000000000000000000000000000000..6b50af26cbed4afec105aec8c425403725f3334d
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/plotCoverage.xml
@@ -0,0 +1,173 @@
+
+ assesses the sequencing depth of BAM/CRAM files
+
+ plotCoverage
+ deepTools_macros.xml
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ outRawCounts is True
+
+
+ coverageOpt.outCoverageMetrics is True
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/deepTools/source/galaxy/wrapper/plotEnrichment.xml b/deepTools/source/galaxy/wrapper/plotEnrichment.xml
new file mode 100644
index 0000000000000000000000000000000000000000..1e34adc96ad60da8e5fb95e0adc0c7297f5d28b7
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/plotEnrichment.xml
@@ -0,0 +1,213 @@
+
+ plots read/fragment coverage over sets of regions
+
+ plotEnrichment
+ deepTools_macros.xml
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ all(c in ' #abcdefghijklmnopqrstuvwxyz0123456789' for c in value)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ outRawCounts is True
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/deepTools/source/galaxy/wrapper/plotFingerprint.xml b/deepTools/source/galaxy/wrapper/plotFingerprint.xml
new file mode 100644
index 0000000000000000000000000000000000000000..dea1ccb9ececf3a7b1183a1a9e214672a583f664
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/plotFingerprint.xml
@@ -0,0 +1,192 @@
+
+ plots profiles of BAM files; useful for assessing ChIP signal strength
+
+ plotFingerprint
+ deepTools_macros.xml
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ ((
+ output['showOutputSettings'] == 'yes' and
+ output['saveRawCounts'] is True
+ ))
+
+
+
+
+ ((
+ output['showOutputSettings'] == 'yes' and
+ output['saveQualityMetrics'] is True
+ ))
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/deepTools/source/galaxy/wrapper/plotHeatmap.xml b/deepTools/source/galaxy/wrapper/plotHeatmap.xml
new file mode 100644
index 0000000000000000000000000000000000000000..79141cfc58bf5bccd4fd677c56a65cbf6a3c15ef
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/plotHeatmap.xml
@@ -0,0 +1,328 @@
+
+ creates a heatmap for score distributions across genomic regions
+
+ plotHeatmap
+ deepTools_macros.xml
+
+
+
+ 0:
+ --colorMap #echo " ".join($colorMap)#
+ #end if
+
+ --alpha '$advancedOpt.alpha'
+ #if str($advancedOpt.colorList).strip() != "":
+ --colorList $advancedOpt.colorList
+ #end if
+
+ #if str($advancedOpt.zMin).strip() != "":
+ --zMin $advancedOpt.zMin
+ #end if
+ #if str($advancedOpt.zMax).strip() != "":
+ --zMax $advancedOpt.zMax
+ #end if
+
+ #if str($advancedOpt.yMin).strip() != "":
+ --yMin $advancedOpt.yMin
+ #end if
+ #if str($advancedOpt.yMax).strip() != "":
+ --yMax $advancedOpt.yMax
+ #end if
+ #if str($advancedOpt.sortUsingSamples).strip() != "":
+ --sortUsingSamples $advancedOpt.sortUsingSamples
+ #end if
+ #if str($advancedOpt.clusterUsingSamples).strip() != "":
+ --clusterUsingSamples $advancedOpt.clusterUsingSamples
+ #end if
+
+ --xAxisLabel '$advancedOpt.xAxisLabel'
+ --yAxisLabel '$advancedOpt.yAxisLabel'
+
+ --heatmapWidth $advancedOpt.heatmapWidth
+ --heatmapHeight $advancedOpt.heatmapHeight
+
+ --whatToShow '$advancedOpt.whatToShow'
+
+ --startLabel '$advancedOpt.startLabel'
+ --endLabel '$advancedOpt.endLabel'
+
+ --refPointLabel '$advancedOpt.referencePointLabel'
+
+ #if $advancedOpt.samplesLabel and str($advancedOpt.samplesLabel).strip() != "":
+ --samplesLabel $advancedOpt.samplesLabel
+ #end if
+
+ #if $advancedOpt.regionsLabel and str($advancedOpt.regionsLabel).strip() != "":
+ --regionsLabel $advancedOpt.regionsLabel
+ #end if
+
+ #if $advancedOpt.plotTitle and str($advancedOpt.plotTitle.value) != "":
+ --plotTitle '$advancedOpt.plotTitle'
+ #end if
+
+ --legendLocation '$advancedOpt.legendLocation'
+
+ --labelRotation '$advancedOpt.labelRotation'
+
+ $advancedOpt.perGroup
+
+ @KMEANS_CLUSTERING@
+
+ #end if
+]]>
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+`_.
+
+-----
+
+@REFERENCES@
+]]>
+
+
+
diff --git a/deepTools/source/galaxy/wrapper/plotPCA.xml b/deepTools/source/galaxy/wrapper/plotPCA.xml
new file mode 100644
index 0000000000000000000000000000000000000000..27b781f84be38f12ad2a7410896fbf24a72de192
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/plotPCA.xml
@@ -0,0 +1,124 @@
+
+ Generate principal component analysis (PCA) plots from multiBamSummary or multiBigwigSummary output
+
+ plotPCA
+ deepTools_macros.xml
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ outFileNameData
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/deepTools/source/galaxy/wrapper/plotProfiler.xml b/deepTools/source/galaxy/wrapper/plotProfiler.xml
new file mode 100644
index 0000000000000000000000000000000000000000..cdccb81558cf65768b42dd13ff00b4a8e7431fc8
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/plotProfiler.xml
@@ -0,0 +1,251 @@
+
+
+ creates a profile plot for score distributions across genomic regions
+
+
+ plotProfile
+ deepTools_macros.xml
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ all(c in ' #abcdefghijklmnopqrstuvwxyz0123456789' for c in value)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ advancedOpt['showAdvancedOpt'] == "yes" and advancedOpt['outFileNameData'] is True
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/deepTools/source/galaxy/wrapper/static/images/GC_bias_simulated_reads_2L.png b/deepTools/source/galaxy/wrapper/static/images/GC_bias_simulated_reads_2L.png
new file mode 100644
index 0000000000000000000000000000000000000000..7eaf340d154b0b0d8a5049b689866b99b03140b0
Binary files /dev/null and b/deepTools/source/galaxy/wrapper/static/images/GC_bias_simulated_reads_2L.png differ
diff --git a/deepTools/source/galaxy/wrapper/static/images/QC_GCplots_input.png b/deepTools/source/galaxy/wrapper/static/images/QC_GCplots_input.png
new file mode 100644
index 0000000000000000000000000000000000000000..45f7f285c2dac2e75c88882d36dd686436ae6ba4
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/static/images/QC_GCplots_input.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:efcd2153f26eeaed58f5a45505100663afbe84ed1c0821ad972d287151420f6c
+size 100598
diff --git a/deepTools/source/galaxy/wrapper/static/images/QC_fingerprint.png b/deepTools/source/galaxy/wrapper/static/images/QC_fingerprint.png
new file mode 100644
index 0000000000000000000000000000000000000000..3b38f1222c86ba6e6e9da71a2fe8e5da47d19189
Binary files /dev/null and b/deepTools/source/galaxy/wrapper/static/images/QC_fingerprint.png differ
diff --git a/deepTools/source/galaxy/wrapper/static/images/QC_multiBamSummary_humanSamples.png b/deepTools/source/galaxy/wrapper/static/images/QC_multiBamSummary_humanSamples.png
new file mode 100644
index 0000000000000000000000000000000000000000..3482fd4f695487fcf12c541f611402fac2f30ab4
Binary files /dev/null and b/deepTools/source/galaxy/wrapper/static/images/QC_multiBamSummary_humanSamples.png differ
diff --git a/deepTools/source/galaxy/wrapper/static/images/QC_plotCoverage.png b/deepTools/source/galaxy/wrapper/static/images/QC_plotCoverage.png
new file mode 100644
index 0000000000000000000000000000000000000000..181b00e2d34050a9faf8b6b7bebd9ac97b26934c
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/static/images/QC_plotCoverage.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b689ba6dafa5b7a1c71225fccb27d5db7481fc7883a85000f368e52debdb0d6
+size 138073
diff --git a/deepTools/source/galaxy/wrapper/static/images/bamCompare_output.png b/deepTools/source/galaxy/wrapper/static/images/bamCompare_output.png
new file mode 100644
index 0000000000000000000000000000000000000000..53b17075efb77a9a3cc22b8105ce1e6836f0beac
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/static/images/bamCompare_output.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e1c86e4e5a4260c29aa391fb70fb1330580a6c37c0d916a90482d9f47b1250d7
+size 474371
diff --git a/deepTools/source/galaxy/wrapper/static/images/bamCoverage_output.png b/deepTools/source/galaxy/wrapper/static/images/bamCoverage_output.png
new file mode 100644
index 0000000000000000000000000000000000000000..7e964a673bd80900bba39bd84cdd7cfe85f22f19
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/static/images/bamCoverage_output.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:912e976544b916f2e2f070029e969a310c92d1ea7243e00b1a750e7b8cee033b
+size 494281
diff --git a/deepTools/source/galaxy/wrapper/static/images/bamFP_galaxy_output.png b/deepTools/source/galaxy/wrapper/static/images/bamFP_galaxy_output.png
new file mode 100644
index 0000000000000000000000000000000000000000..a6adc286c2960c3ab5daa8b94f5b445fa9044020
Binary files /dev/null and b/deepTools/source/galaxy/wrapper/static/images/bamFP_galaxy_output.png differ
diff --git a/deepTools/source/galaxy/wrapper/static/images/bamPEFragmentSize_output.png b/deepTools/source/galaxy/wrapper/static/images/bamPEFragmentSize_output.png
new file mode 100644
index 0000000000000000000000000000000000000000..e3b291427073d4defacf56680491e8f866a4bae1
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/static/images/bamPEFragmentSize_output.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:356c74388e209d2b35bed681b2a12eed65e6dd632570be92116f50b7ba35a8ae
+size 370769
diff --git a/deepTools/source/galaxy/wrapper/static/images/bigwigCompare_output.png b/deepTools/source/galaxy/wrapper/static/images/bigwigCompare_output.png
new file mode 100644
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diff --git a/deepTools/source/galaxy/wrapper/test-data/bamCoverage_result4.bw b/deepTools/source/galaxy/wrapper/test-data/bamCoverage_result4.bw
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diff --git a/deepTools/source/galaxy/wrapper/test-data/bamPEFragmentSize_histogram_result1.png b/deepTools/source/galaxy/wrapper/test-data/bamPEFragmentSize_histogram_result1.png
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index 0000000000000000000000000000000000000000..3b1d56741fb537ab68477fbfddb37e104f3e086f
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diff --git a/deepTools/source/galaxy/wrapper/test-data/bamPEFragmentSize_lengths1.txt b/deepTools/source/galaxy/wrapper/test-data/bamPEFragmentSize_lengths1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4247425b2ffd4da868efe2a9920d1f166769db7b
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/test-data/bamPEFragmentSize_lengths1.txt
@@ -0,0 +1,5 @@
+#bamPEFragmentSize
+Size Occurrences Sample
+241 1 bowtie2 test1.bam
+242 1 bowtie2 test1.bam
+251 1 bowtie2 test1.bam
diff --git a/deepTools/source/galaxy/wrapper/test-data/bamPEFragmentSize_result1.txt b/deepTools/source/galaxy/wrapper/test-data/bamPEFragmentSize_result1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..95115e2f57da3d955b9322c2da9a16ac29a62170
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/test-data/bamPEFragmentSize_result1.txt
@@ -0,0 +1,46 @@
+
+
+Sample label: bowtie2 test1.bam
+Sample size: 3
+
+Fragment lengths:
+Min.: 241.0
+1st Qu.: 241.5
+Mean: 244.66666666666666
+Median: 242.0
+3rd Qu.: 246.5
+Max.: 251.0
+Std: 4.496912521077347
+MAD: 1.0
+Len. 10%: 241.2
+Len. 20%: 241.4
+Len. 30%: 241.6
+Len. 40%: 241.8
+Len. 60%: 243.8
+Len. 70%: 245.6
+Len. 80%: 247.4
+Len. 90%: 249.2
+Len. 99%: 250.82
+
+
+Read lengths:
+Sample size: 3
+
+Min.: 251.0
+1st Qu.: 251.0
+Mean: 251.0
+Median: 251.0
+3rd Qu.: 251.0
+Max.: 251.0
+Std: 0.0
+MAD: 0.0
+Len. 10%: 251.0
+Len. 20%: 251.0
+Len. 30%: 251.0
+Len. 40%: 251.0
+Len. 60%: 251.0
+Len. 70%: 251.0
+Len. 80%: 251.0
+Len. 90%: 251.0
+Len. 99%: 251.0
+
diff --git a/deepTools/source/galaxy/wrapper/test-data/bamPEFragmentSize_table1.txt b/deepTools/source/galaxy/wrapper/test-data/bamPEFragmentSize_table1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..0b0e4700441c20abb7ac0b497f727028ba178dd2
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/test-data/bamPEFragmentSize_table1.txt
@@ -0,0 +1,2 @@
+ Frag. Sampled Frag. Len. Min. Frag. Len. 1st. Qu. Frag. Len. Mean Frag. Len. Median Frag. Len. 3rd Qu. Frag. Len. Max Frag. Len. Std. Frag. Med. Abs. Dev. Frag. Len. 10% Frag. Len. 20% Frag. Len. 30% Frag. Len. 40% Frag. Len. 60% Frag. Len. 70% Frag. Len. 80% Frag. Len. 90% Frag. Len. 99% Reads Sampled Read Len. Min. Read Len. 1st. Qu. Read Len. Mean Read Len. Median Read Len. 3rd Qu. Read Len. Max Read Len. Std. Read Med. Abs. Dev. Read Len. 10% Read Len. 20% Read Len. 30% Read Len. 40% Read Len. 60% Read Len. 70% Read Len. 80% Read Len. 90% Read Len. 99%
+bowtie2 test1.bam 3 241.0 241.5 244.66666666666666 242.0 246.5 251.0 4.496912521077347 1.0 241.2 241.4 241.6 241.8 243.8 245.6 247.4 249.2 250.82 3 251.0 251.0 251.0 251.0 251.0 251.0 0.0 0.0 251.0 251.0 251.0 251.0 251.0 251.0 251.0 251.0 251.0
diff --git a/deepTools/source/galaxy/wrapper/test-data/bigwigAverage2.bw b/deepTools/source/galaxy/wrapper/test-data/bigwigAverage2.bw
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diff --git a/deepTools/source/galaxy/wrapper/test-data/bigwigCompare_result1.bw b/deepTools/source/galaxy/wrapper/test-data/bigwigCompare_result1.bw
new file mode 100644
index 0000000000000000000000000000000000000000..d906262136b4ac8f816a5d98b11244f337d5d304
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diff --git a/deepTools/source/galaxy/wrapper/test-data/bigwigCompare_result2.bg b/deepTools/source/galaxy/wrapper/test-data/bigwigCompare_result2.bg
new file mode 100644
index 0000000000000000000000000000000000000000..7f793b2be45aad4cafb212c31b46b690054a97eb
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/test-data/bigwigCompare_result2.bg
@@ -0,0 +1,3 @@
+ch1 0 400 1
+ch2 0 400 1
+ch3 0 400 1
diff --git a/deepTools/source/galaxy/wrapper/test-data/bowtie2 test1.bam b/deepTools/source/galaxy/wrapper/test-data/bowtie2 test1.bam
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+CGTCTTCATTTCCATGCGGTGCATTTTATGCGGACACTTCCTACAGGTAGCGTTGACCCTAATTTTGGTC
+GTCGGGTACGCAATCGCCGCCAGTTAAATAGCTTGCAAAATACGTGGCCTTATGGTTACAGTATGCCCAT
+CGCAGTTCGCTACACGCAGGACGCTTTTTCACGTTCTGGTTGGTTGTGGCCTGTTGATGCTAAAGGTGAG
+CCGCTTAAAGCTACCAGTTATATGGCTGTTGGTTTCTATGTGGCTAAATACGTTAACAAAAAGTCAGATA
+TGGACCTTGCTGCTAAAGGTCTAGGAGCTAAAGAATGGAACAACTCACTAAAAACCAAGCTGTCGCTACT
+TCCCAAGAAGCTGTTCAGAATCAGAATGAGCCGCAACTTCGGGATGAAAATGCTCACAATGACAAATCTG
+TCCACGGAGTGCTTAATCCAACTTACCAAGCTGGGTTACGACGCGACGCCGTTCAACCAGATATTGAAGC
+AGAACGCAAAAAGAGAGATGAGATTGAGGCTGGGAAAAGTTACTGTAGCCGACGTTTTGGCGGCGCAACC
+TGTGACGACAAATCTGCTCAAATTTATGCGCGCTTCGATAAAAATGATTGGCGTATCCAACCTGCA
+
diff --git a/deepTools/source/galaxy/wrapper/test-data/plotCorrelation_result1.png b/deepTools/source/galaxy/wrapper/test-data/plotCorrelation_result1.png
new file mode 100644
index 0000000000000000000000000000000000000000..726ff8bfa159b56345a2c37477eb6e1779af5a41
Binary files /dev/null and b/deepTools/source/galaxy/wrapper/test-data/plotCorrelation_result1.png differ
diff --git a/deepTools/source/galaxy/wrapper/test-data/plotCorrelation_result1.tabular b/deepTools/source/galaxy/wrapper/test-data/plotCorrelation_result1.tabular
new file mode 100644
index 0000000000000000000000000000000000000000..16d9470a659f736fcbcf7f7c3883adc4a446e859
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/test-data/plotCorrelation_result1.tabular
@@ -0,0 +1,4 @@
+#plotCorrelation --outFileCorMatrix
+ 'bowtie2 test1.bam' 'bowtie2 test1.bam'
+'bowtie2 test1.bam' 1.0000 1.0000
+'bowtie2 test1.bam' 1.0000 1.0000
diff --git a/deepTools/source/galaxy/wrapper/test-data/plotCorrelation_result2.png b/deepTools/source/galaxy/wrapper/test-data/plotCorrelation_result2.png
new file mode 100644
index 0000000000000000000000000000000000000000..c144cf38ab99b8069a2159c0d12971294a8d6728
Binary files /dev/null and b/deepTools/source/galaxy/wrapper/test-data/plotCorrelation_result2.png differ
diff --git a/deepTools/source/galaxy/wrapper/test-data/plotCoverage.metrics b/deepTools/source/galaxy/wrapper/test-data/plotCoverage.metrics
new file mode 100644
index 0000000000000000000000000000000000000000..3b18712bde4d6a6e4507fbfe9545e1e10cafc1f8
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/test-data/plotCoverage.metrics
@@ -0,0 +1,9 @@
+Sample Threshold Percent
+bowtie2 test1.bam 0 100.000
+bowtie2 test1.bam 0 100.000
+bowtie2 test1.bam 5 1.509
+bowtie2 test1.bam 5 1.509
+bowtie2 test1.bam 10 1.461
+bowtie2 test1.bam 10 1.461
+bowtie2 test1.bam 20 1.406
+bowtie2 test1.bam 20 1.406
diff --git a/deepTools/source/galaxy/wrapper/test-data/plotCoverage_result1.png b/deepTools/source/galaxy/wrapper/test-data/plotCoverage_result1.png
new file mode 100644
index 0000000000000000000000000000000000000000..ae7874e0e613a55ce4cd9b978ee25994a4abf5c8
Binary files /dev/null and b/deepTools/source/galaxy/wrapper/test-data/plotCoverage_result1.png differ
diff --git a/deepTools/source/galaxy/wrapper/test-data/plotCoverage_result1.tabular b/deepTools/source/galaxy/wrapper/test-data/plotCoverage_result1.tabular
new file mode 100644
index 0000000000000000000000000000000000000000..0fe93ac8485656511067a9aca06594882256cf39
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/test-data/plotCoverage_result1.tabular
@@ -0,0 +1,16571 @@
+#plotCoverage --outRawCounts
+#'chr' 'start' 'end' 'bowtie2 test1.bam' 'bowtie2 test1.bam'
+chrM 0 1 23.0 23.0
+chrM 1 2 35.0 35.0
+chrM 2 3 35.0 35.0
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+chrM 7 8 35.0 35.0
+chrM 8 9 35.0 35.0
+chrM 9 10 35.0 35.0
+chrM 10 11 35.0 35.0
+chrM 11 12 35.0 35.0
+chrM 12 13 35.0 35.0
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diff --git a/deepTools/source/galaxy/wrapper/test-data/plotEnrichment_output.png b/deepTools/source/galaxy/wrapper/test-data/plotEnrichment_output.png
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diff --git a/deepTools/source/galaxy/wrapper/test-data/plotEnrichment_output.txt b/deepTools/source/galaxy/wrapper/test-data/plotEnrichment_output.txt
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+file featureType percent featureReadCount totalReadCount
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+bowtie2 test1.bam down 100.00 47 47
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diff --git a/deepTools/source/galaxy/wrapper/test-data/plotFingerprint_quality_metrics.tabular b/deepTools/source/galaxy/wrapper/test-data/plotFingerprint_quality_metrics.tabular
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+Sample AUC Synthetic AUC X-intercept Synthetic X-intercept Elbow Point Synthetic Elbow Point JS Distance Synthetic JS Distance % genome enriched diff. enrichment CHANCE divergence
+bowtie2 test1.bam 0.00493632029863651 0.481650684757865 0.984443061605476 1.1531044350267195e-24 0.9849408836341008 0.5232688298112538 nan 0.269004498068121 nan nan nan
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+#plotFingerprint --outRawCounts
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diff --git a/deepTools/source/galaxy/wrapper/test-data/plotPCA_result1.png b/deepTools/source/galaxy/wrapper/test-data/plotPCA_result1.png
new file mode 100644
index 0000000000000000000000000000000000000000..4ce59d7222ffdee1e5f9b2c394e2c0fdeef03e8e
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diff --git a/deepTools/source/galaxy/wrapper/test-data/plotPCA_result2.png b/deepTools/source/galaxy/wrapper/test-data/plotPCA_result2.png
new file mode 100644
index 0000000000000000000000000000000000000000..4ce59d7222ffdee1e5f9b2c394e2c0fdeef03e8e
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diff --git a/deepTools/source/galaxy/wrapper/test-data/plotPCA_result2.tabular b/deepTools/source/galaxy/wrapper/test-data/plotPCA_result2.tabular
new file mode 100644
index 0000000000000000000000000000000000000000..f2b79eed35cf63d669566507e48c7e404793cdcb
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/test-data/plotPCA_result2.tabular
@@ -0,0 +1,4 @@
+#plotPCA --outFileNameData
+Component bowtie2-test1.bam bowtie2-test1.bam Eigenvalue
+1 -0.7071067811865476 -0.7071067811865475 4.0
+2 -0.7071067811865475 0.7071067811865476 2.49319462166397e-32
diff --git a/deepTools/source/galaxy/wrapper/test-data/profiler_result1.png b/deepTools/source/galaxy/wrapper/test-data/profiler_result1.png
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diff --git a/deepTools/source/galaxy/wrapper/test-data/profiler_result2.png b/deepTools/source/galaxy/wrapper/test-data/profiler_result2.png
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diff --git a/deepTools/source/galaxy/wrapper/test-data/profiler_result2.tabular b/deepTools/source/galaxy/wrapper/test-data/profiler_result2.tabular
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index 0000000000000000000000000000000000000000..c6cab1174c637c5e1dd3f154367383a93e106783
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/test-data/profiler_result2.tabular
@@ -0,0 +1,3 @@
+bin labels -0.0Kb 0.0Kb
+bins 1.0 2.0
+bamCoverage_result4_bw_0 genes 2477942.875 2610260.125
diff --git a/deepTools/source/galaxy/wrapper/test-data/sequence.2bit b/deepTools/source/galaxy/wrapper/test-data/sequence.2bit
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diff --git a/deepTools/source/galaxy/wrapper/test-data/test_half.bw b/deepTools/source/galaxy/wrapper/test-data/test_half.bw
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diff --git a/deepTools/source/galaxy/wrapper/tool-data/deepTools_seqs.loc.sample b/deepTools/source/galaxy/wrapper/tool-data/deepTools_seqs.loc.sample
new file mode 100644
index 0000000000000000000000000000000000000000..72d912380803e519c45d03a6d54f88a881785fff
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/tool-data/deepTools_seqs.loc.sample
@@ -0,0 +1,27 @@
+#This is a sample file distributed with Galaxy that enables tools
+#to use a directory of 2bit genome files for use with deepTools. You will
+#need to supply these files and then create a deepTools_seqs.loc file
+#similar to this one (store it in this directory) that points to
+#the directories in which those files are stored. The deepTools_seqs.loc
+#file has this format:
+#
+#
+#
+#So, for example, if your deepTools_seqs.loc began like this:
+#
+#hg18 Human (Homo sapiens): hg18 /depot/data2/galaxy/twobit/hg18.2bit
+#hg19 Human (Homo sapiens): hg19 /depot/data2/galaxy/twobit/hg19.2bit
+#mm9 Mouse (Mus musculus): mm9 /depot/data2/galaxy/twobit/mm9.2bit
+#
+#then your /depot/data2/galaxy/twobit/ directory
+#would need to contain the following 2bit files:
+#
+#-rw-r--r-- 1 james universe 830134 2005-09-13 10:12 hg18.2bit
+#-rw-r--r-- 1 james universe 527388 2005-09-13 10:12 hg19.2bit
+#-rw-r--r-- 1 james universe 269808 2005-09-13 10:12 mm9.2bit
+#
+#Your deepTools_seqs.loc file should include an entry per line for
+#each file you have stored that you want to be available. Note that
+#your files should all have the extension '2bit'.
+#
+#Please note that the is also used as "Species name abbreviation".
diff --git a/deepTools/source/galaxy/wrapper/tool-data/lastz_seqs.loc.sample b/deepTools/source/galaxy/wrapper/tool-data/lastz_seqs.loc.sample
new file mode 100644
index 0000000000000000000000000000000000000000..76584c13da433509813ff7a874ba3fc33cecf8c8
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/tool-data/lastz_seqs.loc.sample
@@ -0,0 +1,29 @@
+#This is a sample file distributed with Galaxy that enables tools
+#to use a directory of 2bit genome files for use with deepTools.
+#This file is named lastz_seqs.loc file to make use of an already existing loc
+#file from the lastz tool that is created by the twobit data-manager.
+#You will need to supply these files and then create a lastz_seqs.loc file
+#similar to this one (store it in this directory) that points to
+#the directories in which those files are stored. The lastz_seqs.loc
+#file has this format:
+#
+#
+#
+#So, for example, if your lastz_seqs.loc began like this:
+#
+#hg18 Human (Homo sapiens): hg18 /depot/data2/galaxy/twobit/hg18.2bit
+#hg19 Human (Homo sapiens): hg19 /depot/data2/galaxy/twobit/hg19.2bit
+#mm9 Mouse (Mus musculus): mm9 /depot/data2/galaxy/twobit/mm9.2bit
+#
+#then your /depot/data2/galaxy/twobit/ directory
+#would need to contain the following 2bit files:
+#
+#-rw-r--r-- 1 james universe 830134 2005-09-13 10:12 hg18.2bit
+#-rw-r--r-- 1 james universe 527388 2005-09-13 10:12 hg19.2bit
+#-rw-r--r-- 1 james universe 269808 2005-09-13 10:12 mm9.2bit
+#
+#Your lastz_seqs.loc file should include an entry per line for
+#each file you have stored that you want to be available. Note that
+#your files should all have the extension '2bit'.
+#
+#Please note that the is also used as "Species name abbreviation".
diff --git a/deepTools/source/galaxy/wrapper/tool_data_table_conf.xml.sample b/deepTools/source/galaxy/wrapper/tool_data_table_conf.xml.sample
new file mode 100644
index 0000000000000000000000000000000000000000..d76691f8bea38fe5c284dd884caeeaedd7b5848b
--- /dev/null
+++ b/deepTools/source/galaxy/wrapper/tool_data_table_conf.xml.sample
@@ -0,0 +1,8 @@
+
+
+
+
+
diff --git a/deepTools/source/gallery/Whyte_TypicalEnhancers_ESC.bed b/deepTools/source/gallery/Whyte_TypicalEnhancers_ESC.bed
new file mode 100644
index 0000000000000000000000000000000000000000..3ab3ef13fdd5b37d5fb50a4c355391e27d434c59
--- /dev/null
+++ b/deepTools/source/gallery/Whyte_TypicalEnhancers_ESC.bed
@@ -0,0 +1,8563 @@
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diff --git a/deepTools/source/gallery/hm_CpG_thumb.png b/deepTools/source/gallery/hm_CpG_thumb.png
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diff --git a/deepTools/source/gallery/hm_DNase.png b/deepTools/source/gallery/hm_DNase.png
new file mode 100644
index 0000000000000000000000000000000000000000..7d0a241c4a8755d356f89bc38028e5ad44acdb5b
--- /dev/null
+++ b/deepTools/source/gallery/hm_DNase.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2be698c9044bb75dff3ac52a9bf241dd6137991ce1b3a8c6c66f04fad31566a2
+size 602497
diff --git a/deepTools/source/gallery/hm_DNase_thumb.png b/deepTools/source/gallery/hm_DNase_thumb.png
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diff --git a/deepTools/source/gallery/hm_GC.png b/deepTools/source/gallery/hm_GC.png
new file mode 100644
index 0000000000000000000000000000000000000000..37d5197dff9b12429ffb6bb81c1da1f7c112f8e9
--- /dev/null
+++ b/deepTools/source/gallery/hm_GC.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7a04c1443116a6a9516eae9081e4db8f17151918d6e1e7a74b28db8b7ad14269
+size 1584715
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diff --git a/deepTools/source/gallery/hm_TATApsem.png b/deepTools/source/gallery/hm_TATApsem.png
new file mode 100644
index 0000000000000000000000000000000000000000..dc6ac19876d54b4e5a420281446eda0da4edb240
--- /dev/null
+++ b/deepTools/source/gallery/hm_TATApsem.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8972af72c506ac7d72a97a1c97ff4d1849555cc5e3aa187ec737c548d8c7392d
+size 131205
diff --git a/deepTools/source/gallery/hm_TATApsem_thumb.png b/deepTools/source/gallery/hm_TATApsem_thumb.png
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diff --git a/deepTools/source/gallery/hm_histonesGomez.png b/deepTools/source/gallery/hm_histonesGomez.png
new file mode 100644
index 0000000000000000000000000000000000000000..6becbca892261df1c04ceed0a2911c47e4560b9c
--- /dev/null
+++ b/deepTools/source/gallery/hm_histonesGomez.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8b7989b52babb51ad9984f5181b768170ea40cda34b3d174bc33765c4c06ce25
+size 1175435
diff --git a/deepTools/source/gallery/hm_histonesGomez_thumb.png b/deepTools/source/gallery/hm_histonesGomez_thumb.png
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diff --git a/deepTools/source/pyproject.toml b/deepTools/source/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..2072ec1f522adcf469899698e357d3ff145fe659
--- /dev/null
+++ b/deepTools/source/pyproject.toml
@@ -0,0 +1,79 @@
+[build-system]
+requires = [
+ "setuptools"
+]
+
+[project]
+name = "deepTools"
+version = "3.5.6"
+authors = [
+ {name="Fidel Ramirez"},
+ {name="Devon P Ryan"},
+ {name="Björn Grüning"},
+ {name="Friederike Dündar"},
+ {name="Sarah Diehl"},
+ {name="Vivek Bhardwaj"},
+ {name="Fabian Kilpert"},
+ {name="Andreas S Richter"},
+ {name="Steffen Heyne"},
+ {name="Thomas Manke"},
+ {email="bioinfo-core@ie-freiburg.mpg.de"}
+]
+requires-python = "> 3.8"
+dependencies = [
+ "numpy >= 2.0.0",
+ "scipy >= 0.17.0",
+ "matplotlib >= 3.5.0",
+ "pysam >= 0.14.0",
+ "numpydoc >= 0.5",
+ "pyBigWig >= 0.2.1",
+ "py2bit >= 0.2.0",
+ "plotly >= 4.9",
+ "deeptoolsintervals >= 0.1.8"
+]
+description = "Useful tools for exploring deep sequencing data."
+license = {file = "LICENSE.txt"}
+classifiers = [
+ "Intended Audience :: Science/Research",
+ "Topic :: Scientific/Engineering :: Bio-Informatics"
+]
+readme = "README.rst"
+[project.optional-dependencies]
+actions = [
+ "flake8",
+ "pytest",
+ "twine",
+ "build",
+ "planemo"
+]
+[project.urls]
+homepage = "https://pypi.python.org/pypi/deepTools/"
+documentation = "https://deeptools.readthedocs.io/en/latest/"
+repository = "https://github.com/deeptools/deepTools"
+
+[tool.setuptools]
+packages = ["deeptools"]
+
+[project.scripts]
+alignmentSieve = "deeptools.alignmentSieve:main"
+bamCompare = "deeptools.bamCompare:main"
+bamCoverage = "deeptools.bamCoverage:main"
+bamPEFragmentSize = "deeptools.bamPEFragmentSize:main"
+bigwigAverage = "deeptools.bigwigAverage:main"
+bigwigCompare = "deeptools.bigwigCompare:main"
+computeGCBias = "deeptools.computeGCBias:main"
+computeMatrix = "deeptools.computeMatrix:main"
+computeMatrixOperations = "deeptools.computeMatrixOperations:main"
+correctGCBias = "deeptools.correctGCBias:main"
+deeptools = "deeptools.deeptools_list_tools:main"
+estimateReadFiltering = "deeptools.estimateReadFiltering:main"
+estimateScaleFactor = "deeptools.estimateScaleFactor:main"
+multiBamSummary = "deeptools.multiBamSummary:main"
+multiBigwigSummary = "deeptools.multiBigwigSummary:main"
+plotCorrelation = "deeptools.plotCorrelation:main"
+plotCoverage = "deeptools.plotCoverage:main"
+plotEnrichment = "deeptools.plotEnrichment:main"
+plotFingerprint = "deeptools.plotFingerprint:main"
+plotHeatmap = "deeptools.plotHeatmap:main"
+plotPCA = "deeptools.plotPCA:main"
+plotProfile = "deeptools.plotProfile:main"
diff --git a/deepTools/source/scripts/convertChromsBigWig.py b/deepTools/source/scripts/convertChromsBigWig.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4db016c81ef70ef72af27f4f5c01fde4ea59a0d
--- /dev/null
+++ b/deepTools/source/scripts/convertChromsBigWig.py
@@ -0,0 +1,186 @@
+#!/usr/bin/env python
+
+import sys
+import pyBigWig
+import requests
+import re
+import argparse
+from argparse import RawTextHelpFormatter
+import itertools
+
+
+def parse_arguments(defaults):
+ parser = argparse.ArgumentParser(description='Convert chromosome names for bigwig files between ensembl, gencode and UCSC naming schemes\n' +
+ "Per default it writes to the same location as original file, however with a modified filename:\n" +
+ "eg. test.bw --> test.[toFormat]_chroms.bw\n" +
+ "Change this with the -o option!\n\n" +
+ "Mapping tables are taken from https://github.com/dpryan79/ChromosomeMappings\n\n" +
+ "Provided mapping options need to exactly match an existing file\n" +
+ "[GENOME]_[FROM_FORMAT]2[TO_FORMAT].txt in this repo!",
+ usage='$ convertChroms BIGWIG', formatter_class=RawTextHelpFormatter)
+
+ parser.add_argument('bw_in_filename',
+ metavar='BIGWIG',
+ help='bigwig file that will be converted')
+
+ parser.add_argument('--genome', '-g',
+ action='store',
+ dest='genome',
+ help='Genome version of original bigwig \n' +
+ '(GRCm37|GRCm38|GRCh37|GRCh38|BDGP6|dm3|GRCz10|GRCz11|\n' +
+ 'JGI_4.2|MEDAKA1|R64-1-1|WBcel235|Zv9|galGal4|rn5|rn6)\n' +
+ '(default: %(default)s)',
+ default=defaults["genome"])
+
+ parser.add_argument('--fromFormat', '-f',
+ action='store',
+ dest='from_format',
+ help='Chr naming format of original bigwig (ensembl|gencode|UCSC) (default: %(default)s)',
+ default=defaults["fromFormat"])
+
+ parser.add_argument('--toFormat', '-t',
+ action='store',
+ dest='to_format',
+ help='Chr naming format of converted bigwig (ensembl|gencode|UCSC) (default: %(default)s)',
+ default=defaults["toFormat"])
+
+ parser.add_argument('--outFileName', '-o',
+ action='store',
+ dest='bw_out_filename',
+ help='Chr naming format of converted bigwig (ensembl|gencode|UCSC) (default: %(default)s)',
+ default=defaults["bw_out_filename"])
+
+ parser.add_argument('--baseURL', '-u',
+ action='store',
+ dest='base_url',
+ help='base url where the mapping tables can be found (default: %(default)s)\n' +
+ 'Local files can be given with \'file://[BASE_DIR]/\'',
+ default=defaults["base_url"])
+
+ parser.add_argument('--verbose', '-v',
+ action='store_true',
+ dest='verbose',
+ help='Be more verbose where possible (default: %(default)s)',
+ default=defaults["verbose"])
+
+ return parser
+
+
+def get_chromosome_mapping(genome="GRCm38", from_format="ensembl", to_format="UCSC", verbose=True, base_url='https://raw.githubusercontent.com/dpryan79/ChromosomeMappings/master/'):
+ """
+ creates a dict with chromosome name mappings according to provided conversion formats
+
+ default base URL access a github repo with conversion files,
+ but you can also give eg. a path to local directory
+ """
+ mapping_file = genome + '_' + from_format + '2' + to_format + '.txt'
+
+ if re.match('^file:[/]+.*', base_url):
+ base_url = re.sub("file:[/]*(/.*)", "\\1", base_url)
+
+ if verbose:
+ print("load mapping table (" + mapping_file + ') from ' + base_url)
+
+ tab = None
+
+ if re.match('^https?://.*', base_url):
+ try:
+ r = requests.get(base_url + '/' + mapping_file)
+ r.raise_for_status()
+ except requests.exceptions.RequestException as e:
+ print("\n", e, "\n\nPlease provide correct name (GENOME, FROM_FORMAT, TO_FORMAT) for a mapping table!\n")
+ sys.exit(1)
+ tab = r.text
+ elif re.match('^[/]+.*', base_url):
+ try:
+ tab = open(base_url + '/' + mapping_file).read()
+ except IOError as e:
+ print("\n", e, "\n\nPlease provide a correct name (GENOME, FROM_FORMAT, TO_FORMAT) for a mapping table!\n")
+ sys.exit(1)
+ else:
+ print("\nPlease provide a correct BASE_URL for a mapping table!\n")
+ sys.exit(1)
+
+ mapping_table = {}
+ for ent in tab.split("\n"):
+ if len(ent) == 0:
+ continue
+ pair = ent.split("\t")
+ if (len(pair[1]) <= 0):
+ # if (verbose):
+ # print("skip chrom \'" + pair[0] + "\' - cannot be mapped to "+to_format)
+ continue
+ mapping_table[pair[0]] = pair[1]
+
+ return mapping_table
+
+
+def convert_bigwig(mapping_table, bw_in_filename, bw_out_filename, verbose=False):
+ """
+ convert chromosome names of a bigwig file according to given mapping_table
+
+ it checks which chromosome names that can correctly mapped, all other chromosomes are skipped
+ """
+ bw = pyBigWig.open(bw_in_filename)
+ curr_chroms = bw.chroms()
+
+ final_mapping_table = {}
+ new_chroms = {}
+
+ for c in curr_chroms:
+ if c not in mapping_table:
+ if (verbose):
+ print("skip original chrom \'" + c + "\' - cannot be found in mapping table! Right GENOME & FROM_FORMAT?")
+ continue
+ final_mapping_table[c] = mapping_table[c]
+ new_chroms[mapping_table[c]] = curr_chroms[c]
+
+ if (len(new_chroms) <= 0):
+ print("No chromosomes found for mapping! Wrong 'FROM_FORMAT'?")
+ sys.exit(1)
+
+ bw_out = pyBigWig.open(bw_out_filename, "w")
+ bw_out.addHeader(list(new_chroms.items()))
+
+ for c in final_mapping_table:
+ c_int = bw.intervals(c)
+ c_map = final_mapping_table[c]
+ if verbose:
+ print("convert chromosome: ", c, " --> ", c_map)
+ bw_out.addEntries(list(itertools.repeat(c_map, len(c_int))), [x[0] for x in c_int], ends=[x[1] for x in c_int], values=[x[2] for x in c_int])
+
+ bw_out.close()
+ bw.close()
+
+ if (verbose):
+ print("\nbigwig conversion finished!\n")
+
+
+def main(args=None):
+
+ defaults = {
+ 'genome': 'GRCm38',
+ 'fromFormat': 'ensembl',
+ 'toFormat': 'UCSC',
+ 'verbose': False,
+ 'bw_out_filename': None,
+ 'base_url': 'https://raw.githubusercontent.com/dpryan79/ChromosomeMappings/master/'
+ }
+
+ args = parse_arguments(defaults).parse_args(args)
+
+ bw_out_filename = args.bw_out_filename
+ if args.bw_out_filename is None:
+ bw_out_filename = re.sub(r"(.[^\.]+)$", ".%s\\1" % (args.to_format + "_chroms"), args.bw_in_filename)
+ print("\noutput_file: " + bw_out_filename)
+
+ mapping_table = get_chromosome_mapping(genome=args.genome, from_format=args.from_format, to_format=args.to_format, verbose=args.verbose, base_url=args.base_url)
+
+ convert_bigwig(mapping_table, args.bw_in_filename, bw_out_filename, args.verbose)
+
+
+if __name__ == "__main__":
+ args = None
+ if len(sys.argv) == 1:
+ args = ["--help"]
+ main(args)
diff --git a/deepTools/source/scripts/mappabilityBigWig_to_unmappableBed.sh b/deepTools/source/scripts/mappabilityBigWig_to_unmappableBed.sh
new file mode 100644
index 0000000000000000000000000000000000000000..79caff54169157ab4e9d44c966852bfe286a341d
--- /dev/null
+++ b/deepTools/source/scripts/mappabilityBigWig_to_unmappableBed.sh
@@ -0,0 +1,5 @@
+#!/usr/bin/sh
+
+/package/UCSCTools/bigWigToBedGraph $1 _temp.bed
+cat _temp.bed | perl -lane '$id+=1; if($F[3]<1) { print "$F[0]\t$F[1]\t$F[2]\tunmap_$id\t0"}' | /package/BEDTools/bin/mergeBed > $2
+rm _temp.bed
diff --git a/deepTools/source/scripts/split_bed_into_multiple_files.py b/deepTools/source/scripts/split_bed_into_multiple_files.py
new file mode 100644
index 0000000000000000000000000000000000000000..2726a351841abfec871737744950cc3bc2c67e17
--- /dev/null
+++ b/deepTools/source/scripts/split_bed_into_multiple_files.py
@@ -0,0 +1,35 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""
+This script is use to split a bed file into
+smaller beds following the convention that I introduced
+in which a # indicates a group in the bed file
+
+Each bed group is saved under a different name
+corresponding to the name of the group
+
+Useful to split the results of heatmapper when the clustering
+option is used.
+
+:Authors: fidel.ramirez@gmail.com
+"""
+
+import sys
+
+i = 0
+tempArray = []
+
+for line in sys.stdin:
+ if line[0] == '#':
+ clusterName = line[1:].strip()
+ tempArray.append("#" + clusterName + "\n")
+ open(clusterName + ".bed", 'w').write("".join(tempArray))
+ tempArray = []
+ continue
+
+ tempArray.append(line)
+
+if len(tempArray) > 0:
+ clusterName = "no_name"
+ open(clusterName + ".bed", 'w').write("".join(tempArray))
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..2a163c16f4781c03ef2978d50d2ee76392e2db95
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,13 @@
+fastmcp
+fastapi
+uvicorn[standard]
+pydantic>=2.0.0
+numpy >= 2.0.0
+scipy >= 0.17.0
+matplotlib >= 3.5.0
+pysam >= 0.14.0
+numpydoc >= 0.5
+pyBigWig >= 0.2.1
+py2bit >= 0.2.0
+plotly >= 4.9
+deeptoolsintervals >= 0.1.8
diff --git a/run_docker.ps1 b/run_docker.ps1
new file mode 100644
index 0000000000000000000000000000000000000000..bb00d15642400544640657a429b24014bb41059c
--- /dev/null
+++ b/run_docker.ps1
@@ -0,0 +1,35 @@
+cd $PSScriptRoot
+
+$ErrorActionPreference = "Stop"
+
+$entryName = if ($env:MCP_ENTRY_NAME) { $env:MCP_ENTRY_NAME } else { "deepTools" }
+$entryUrl = if ($env:MCP_ENTRY_URL) { $env:MCP_ENTRY_URL } else { "http://localhost:7860/mcp" }
+$imageName = if ($env:MCP_IMAGE_NAME) { $env:MCP_IMAGE_NAME } else { "deepTools-mcp" }
+
+$mcpDir = Join-Path $env:USERPROFILE ".cursor"
+$mcpPath = Join-Path $mcpDir "mcp.json"
+if (!(Test-Path $mcpDir)) { New-Item -ItemType Directory -Path $mcpDir | Out-Null }
+
+$config = @{}
+if (Test-Path $mcpPath) {
+ try { $config = Get-Content $mcpPath -Raw | ConvertFrom-Json } catch { $config = @{} }
+}
+
+# Rebuild mcpServers as ordered and append the entry last
+$serversOrdered = [ordered]@{}
+if ($config -and ($config.PSObject.Properties.Name -contains "mcpServers") -and $config.mcpServers) {
+ $existing = $config.mcpServers
+ if ($existing -is [pscustomobject]) {
+ foreach ($p in $existing.PSObject.Properties) { if ($p.Name -ne $entryName) { $serversOrdered[$p.Name] = $p.Value } }
+ } elseif ($existing -is [System.Collections.IDictionary]) {
+ foreach ($k in $existing.Keys) { if ($k -ne $entryName) { $serversOrdered[$k] = $existing[$k] } }
+ }
+}
+$serversOrdered[$entryName] = @{ url = $entryUrl }
+$config = @{ mcpServers = $serversOrdered }
+
+$config | ConvertTo-Json -Depth 10 | Set-Content -Path $mcpPath -Encoding UTF8
+Write-Host ("Updated $entryName in " + $mcpPath + " -> " + $entryUrl)
+
+docker build -t $imageName .
+docker run --rm -p 7860:7860 $imageName
diff --git a/run_docker.sh b/run_docker.sh
new file mode 100644
index 0000000000000000000000000000000000000000..704424a976398b9c3953740b37989d98fc14c4b0
--- /dev/null
+++ b/run_docker.sh
@@ -0,0 +1,82 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+# Switch to the directory where this script is located
+cd "$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)"
+
+mcp_entry_name="${MCP_ENTRY_NAME:-deepTools}"
+mcp_entry_url="${MCP_ENTRY_URL:-http://localhost:7860/mcp}"
+mcp_dir="${HOME}/.cursor"
+mcp_path="${mcp_dir}/mcp.json"
+mkdir -p "${mcp_dir}"
+
+if command -v python3 >/dev/null 2>&1; then
+python3 - "${mcp_path}" "${mcp_entry_name}" "${mcp_entry_url}" <<'PY'
+import json, os, sys
+path, name, url = sys.argv[1:4]
+cfg = {"mcpServers": {}}
+if os.path.exists(path):
+ try:
+ with open(path, "r", encoding="utf-8") as f:
+ cfg = json.load(f)
+ except Exception:
+ cfg = {"mcpServers": {}}
+if not isinstance(cfg, dict):
+ cfg = {"mcpServers": {}}
+servers = cfg.get("mcpServers")
+if not isinstance(servers, dict):
+ servers = {}
+ordered = {}
+for k, v in servers.items():
+ if k != name:
+ ordered[k] = v
+ordered[name] = {"url": url}
+cfg = {"mcpServers": ordered}
+with open(path, "w", encoding="utf-8") as f:
+ json.dump(cfg, f, indent=2, ensure_ascii=False)
+PY
+elif command -v python >/dev/null 2>&1; then
+python - "${mcp_path}" "${mcp_entry_name}" "${mcp_entry_url}" <<'PY'
+import json, os, sys
+path, name, url = sys.argv[1:4]
+cfg = {"mcpServers": {}}
+if os.path.exists(path):
+ try:
+ with open(path, "r", encoding="utf-8") as f:
+ cfg = json.load(f)
+ except Exception:
+ cfg = {"mcpServers": {}}
+if not isinstance(cfg, dict):
+ cfg = {"mcpServers": {}}
+servers = cfg.get("mcpServers")
+if not isinstance(servers, dict):
+ servers = {}
+ordered = {}
+for k, v in servers.items():
+ if k != name:
+ ordered[k] = v
+ordered[name] = {"url": url}
+cfg = {"mcpServers": ordered}
+with open(path, "w", encoding="utf-8") as f:
+ json.dump(cfg, f, indent=2, ensure_ascii=False)
+PY
+elif command -v jq >/dev/null 2>&1; then
+ name="${mcp_entry_name}"; url="${mcp_entry_url}"
+ if [ -f "${mcp_path}" ]; then
+ tmp="$(mktemp)"
+ jq --arg name "$name" --arg url "$url" '
+ .mcpServers = (.mcpServers // {})
+ | .mcpServers as $s
+ | ($s | with_entries(select(.key != $name))) as $base
+ | .mcpServers = ($base + {($name): {"url": $url}})
+ ' "${mcp_path}" > "${tmp}" && mv "${tmp}" "${mcp_path}"
+ else
+ printf '{ "mcpServers": { "%s": { "url": "%s" } } }
+' "$name" "$url" > "${mcp_path}"
+ fi
+else
+ echo "Warning: neither python nor jq found; skipped updating ~/.cursor/mcp.json" >&2
+fi
+
+docker build -t deepTools-mcp .
+docker run --rm -p 7860:7860 deepTools-mcp