TADLib 0.4.5.post1
A Library to Explore Chromatin Interaction Patterns for Topologically Associating Domains
Keywords: TAD Aggregation Preference AP sub-TAD hierarchy Hi-C cooler

.. note:: Since version 0.4.0, the default data format has changed to `cool <https://github.com/mirnylab/cooler>`_,
   to comply with 4DN standards.


Introduction
============
Chromosome conformation capture (3C) derived techniques, especially Hi-C, have
revealed that topologically associating domain (TAD) is a structural basis for
both chromatin organization and biological functions in three-dimensional (3D)
space. TAD is also hierarchically organized by smaller structural units, which
are relevant to biological functions. To systematically investigate the relationship
between structure and function, it is important to develop quantitative methods to
identify and measure the organization of TAD. TADLib is such a library to explore
the chromatin interaction patterns inside TAD from Hi-C chromatin interactions.

Currently, TADLib consists of two methods:

- Aggregation Preference (AP)
    AP is a quantitative parameter to measure the overall density of significant
    chromatin interactions inside TAD. Inspired by the observation that there exist
    great differences in chromatin interaction pattern among TADs, an empirical
    parameter called Aggregation Preference (AP) can be used to capture these
    aggregation degree of significant chromatin interactions. Application to human
    and mouse cell types (including both traditional Hi-C and in situ Hi-C data sets)
    shows that there exist heterogeneous structures among TADs and the structural
    rearrangement across cell types is significantly associated with transcriptional
    remodelling. [1]_
- Hierarchical TAD (HiTAD)
    HiTAD is a method to detect hierarchical TADs, including TADs, sub-TADs and
    smaller domains. Except local insulations, HiTAD further constrains TADs as the
    optimal domains to globally separate intra-chromosomal interactions. Under
    objective functions derived from chromatin interactions, HiTAD adopts an iterative
    optimization procedure to detect hierarchical TADs. HiTAD performs well in domain
    sensitivity, replicate reproducibility and inter cell-type conservation. Application
    to human and mouse cell types (including both traditional Hi-C and in situ Hi-C data
    sets) reveals that there exist common change types for hierarchical TADs, which are
    involved in shaping higher-order compartment, replication timing and transcriptional
    regulation. [2]_
    

Links
=====
- `Detailed Documentation <https://xiaotaowang.github.io/TADLib/>`_
    - `Installation <https://xiaotaowang.github.io/TADLib/install.html>`_
    - `Aggregation Preference <https://xiaotaowang.github.io/TADLib/calfea.html>`_
    - `Hierarchical TAD <https://xiaotaowang.github.io/TADLib/hitad.html>`_
    - `Detect Single-level TAD <https://xiaotaowang.github.io/TADLib/domaincaller.html>`_
    - `Visualization <https://xiaotaowang.github.io/TADLib/visualize.html>`_
- `Code Repository <https://github.com/XiaoTaoWang/TADLib>`_ (At GitHub, Track the package issue)
- `PyPI <https://pypi.python.org/pypi/TADLib>`_ (Download and Installation)
	
Citation
========
.. [1] Wang XT, Dong PF, Zhang HY, Peng C. Structural heterogeneity and functional diversity
   of topologically associating domains in mammalian genomes. Nucleic Acids Research, 2015,
   doi: 10.1093/nar/gkv684

.. [2] Wang XT, Cui W, Peng C. HiTAD: detecting the structural and functional hierarchies of
   topologically associating domains from chromatin interactions. Nucleic Acids Research, 2017,
   doi: 10.1093/nar/gkx735