Saravanan666/pypi-org huggingface dataset:
Package: bio-pyvol
PyVOL: Protein Pocket Visualization, Segmentation, and Characterization![image](https://img.shields.io/pypi/v/bio_pyvol.svg) ![image](https://img.shields.io/pypi/l/bio_pyvol.svg)See the [Full Project Documentation](https://schlessingerlab.github.io/pyvol/).
![image](docs/source/_static/overview_image_v01.png)OverviewPyVOL is a python library packaged into a [PyMOL](https://pymol.org/2/)
GUI for identifying protein binding pockets, partitioning them into
sub-pockets, and calculating their volumes. PyVOL can be run as a PyMOL
plugin through its GUI or the PyMOL prompt, as an imported python
library, or as a command-line program. Visualization of results is
exclusively supported through PyMOL though exported surfaces are
compatible with standard 3D geometry visualization programs. The project
is hosted on github by the Schlessinger Lab. Please access the
repository to view code or submit bugs. The package has been most
extensively tested with PyMOL 2.3+ running Python 3.7. Support for all
python versions 2.7+ is intended but not as thoroughly tested. Support
for PyMOL 1.7.4+ without the GUI is as yet incomplete. Unfortunately,
PyVOL can not currently run on MacOS Catalina due to its restrictions on
running 32-bit executables. The Mac-compatible MSMS executable is not
yet available in a 64-bit form.Quick Installation into PyMOL 2.0+PyVOL can be installed into any python environment, but installing
directly into PyMOL 2.0+ is easiest. Download the
[basic GUI installer](https://github.com/schlessingerlab/pyvol/blob/master/installers/pyvol-installer.zip);
and then use the PyMOL plugin manager to install that file:
Plugins –&gt; Plugin Manager –&gt; Install New Plugin –&gt; Install from local file –&gt;
Choose file…This installs the PyVOL GUI. Select
Plugins –&gt; PyVOL –&gt; Settings –&gt; Install PyVOL from PyPI to
fetch PyVOL and any missing dependencies. Once PyVOL has been installed,
the location of MSMS must be added to the path. In the MSMS Settings
panel, common locations for the executable can be searched. Once an
executable has been identified and is displayed, Change MSMS Path can be
clicked to make that executable visible to the back-end. The GUI should
then display that it can find MSMS. For academic users and non-academic
users with the Schrodinger incentive PyMOL distribution, installation is
now complete. For all others see the full [installation documentation](https://schlessingerlab.github.io/pyvol/install.html).Example Basic RunA simple calculation using the PyMOL prompt is to load a protein of
interest and then run the pocket command. This is an example for the
Sorafenib-bound structure of BRAF:` {.sourceCode .python} fetch '1uwh' pocketprotein="1uwhand chain B" `
Package: biorad1sc-reader
Python Package to read, parse, convert *.1sc files generated by Bio-Rad
Quantity One software.Documentation at:http://biorad1sc-reader.readthedocs.io/An optional dependency:numpy.  If present, image operations in
biorad1sc_reader will run 4x to 5x faster.Also installs the following command-line executables:bio1sc2tiffconverts *.1sc files to *.tif images. (typebio1sc2tiff--help)bio1scmetareports all metadata contained in each *.1sc file to a text file.
(typebio1scmeta--help)bio1screadreports details on a *.1sc file’s internal structure.
(typebio1scread--help)Current Status“Alpha” quality softwareAPI subject to changeNo known problems in operation.Reading Image Data OKSaving Image from file to new TIFF file OKReader.save_img_as_tiff()Reader.save_img_as_tiff_sc()Reading all metadata OKReader.get_metadata()Reader.get_metadata_compact()Reading certain image Metadata from Data Block 7 OKReader.get_img_summary()Includes:Scanner Name: <Scanner Name>Number of Pixels: (<img_size_x> x <img_size_y>)Image Area: (<img_size_x> mm x <img_size_y> mm)Scan Memory Size: <total_img_size> KbOld file name: <old_file_name>New file name: <new_file_name><File Directory>New Image AcquiredSave As…Quantity One <Quantity One Version String>python2 (lack of) supportI’m currently unwilling to support python2If you’d like python2 support, it’s DIY. :)Examples# setup reader with input fileimportbio1sc_readermyreader=bio1sc_reader.Reader("my_biorad_file.1sc")# get list/dict of all metadata in 1sc filemy_img_metadata=myreader.get_metadata()# get a more succinct data structure of all metadata in 1sc filemy_img_metadata=myreader.get_metadata_compact()# get a quick summary of some metadata about the image in the 1sc filemy_img_metadata=myreader.get_img_summary()# Different options for writing image data out as a TIFF filemyreader.save_img_as_tif("unscaled_brightness.tif")myreader.save_img_as_tif("unscaled_inverted_brightness.tif",invert=True)myreader.save_img_as_tif_sc("scaled_brightness.tif")myreader.save_img_as_tif_sc("scaled_brightness_more.tif",scale=0.8)myreader.save_img_as_tif_sc("scaled_inverted_brightness.tif",invert=True)File Format DocumentationFile Specification for Bio-Rad 1sc filesRelated InfoBio-Rad Image Lab SoftwareFor Mac, only version 5.x supports 1sc files. (Click on the Download tab to find it.)Bio-Rad Quantity One SoftwareGenerates 1sc files from Bio-Rad instruments.Bio Formats: Bio-Rad Gel 1sc file formatBioRadGelReader.java sourceForum post: Bio-Format 1sc to jpg batch conversionreStructuredText Syntax
Package: biorefineries
ContentsWhat is the Bioindustrial-Park?InstallationDocumentationBug reportsLicense informationWhat is the Bioindustrial-Park?The Bioindustrial-Park is the premier repository for complete biorefinery
models and results generated with BioSTEAM. The repository is meant to
foster accessibility and deeper communication within the BioSTEAM biorefinery
simulation community. All biorefinery models are in the “biorefineries”
package, as an installable package for complete biorefinery models in BioSTEAM.InstallationGet the latest version of biorefineries fromhttps://pypi.python.org/pypi/biorefineries/If you have an installation of Python with pip, simple install it with:$ pip install biorefineriesTo get the git version, run:$ git clonehttps://github.com/BioSTEAMDevelopmentGroup/Bioindustrial-Park.gitDocumentationDocumentation for BioSTEAM’s premier biorefinery models are available on the web:http://biosteam.readthedocs.io/Bug reportsTo report bugs, please use the biorefineries’s Bug Tracker at:git://github.com/yoelcortes/Bioindustrial-ParkLicense informationSeeLICENSE.txtfor information on the terms & conditions for usage
of this software, and a DISCLAIMER OF ALL WARRANTIES.
Package: biorepo
BiorepoManage your bioinformatics soft with one command lineInstallationpipinstallbiorepoUsagebiorepo--help# biorepo.toml[biorepo]version="0.0.1"name="align"os=["linux"][install.bsalign]bin=["bsalign"]run=["make clean","make"]git_url="http://hub.nuaa.cf/ruanjue/bsalign.git"biorepoinstall
Package: biorosetta
BioRosettaThis is a package to map gene names between different naming conventions.Motivation: while there are popular packages for gene identifier mapping (e.g. ENSG, NCBI, HGNC) in the R environment (e.g. AnnotationDB), there is no standard solution in python.Package FeaturesimportbiorosettaasbrSource-based system: Instead of relying on a single repository for mapping gene identifiers, biorosetta integrates results from different repositories, or "sources". Biorosetta supports two types of sources under the same interface. (1)Local sources: biorosetta downloads a local version of the conversion tables from popular repositories (Ensembl Biomart, HGNC Biomart). Best option for highly reproducible gene conversion outputs that do not change over time (e.g. for scientific article preparation). (2)Remote sources: biorosetta interfaces to remote web service applications (MyGene) to convert gene names. Best option for highly up-to-date conversion.Priority system: The user can specify an order of source priority, so that when different sources produce a different conversion output it is possible to define the most trusted result.Conversion report: For critical gene mapping applications, biorosetta can optionally generate a report table that specifies the mapping output of each separate source and highlight where there have been mismatches between outputs, so that these mapping results can be investigated further.Multi-hits policies: When multiple possible mapping outputs ("hits") are found, one can choose the policy for integrating them: "all": concatenates all the ID outputs with a pipe ("|") symbol (e.g. "foo|bar|baz"). "consensus": compares the output hits across different sources to select the ID that appears most frequently across different sources. E.g. if source A outputs "foo|bar" and source B outputs "bar|baz" then "bar" is selected as the final output.Gene name synonyms: Gene symbols have many synonyms. When available within the source, biorosetta integrates the synonym information for one-way mapping from gene symbol to any other ID.Speed: Biorosetta performs vectorized operations to achieve maximum efficiency.Currently implemented sources and gene identifiersSources:Ensembl Biomart (https://useast.ensembl.org/biomart/martview): Local sourceHGNC Biomart (GeneNames) (https://biomart.genenames.org/): Local sourceMyGene (https://mygene.info): Remote sourceGene Identifiers:"ensg": Ensembl gene ID (all sources)"entr": NCBI gene ID (entrezgene, all sources)"symb": Gene name (symbol, all sources)"ensp": Ensembl protein ID (ENSP, Ensembl Biomart only)"hgnc": HGNC ID (Ensembl Biomart and HGNC Biomart only)UsageSee up-to-date documentation and examples atgithub repo.
Package: bio-rtd
bio-rtd librarybio-rtdlibrary is a python library for modeling
residence time distributions (RTD)
of integrated continuous biomanufacturing processes.VersionCurrent version: 0.7.1RequirementsPython 3.7.+Core dependencies:numpy
scipyPackages for visual representation in examples:bokeh
xlrdPackages for importing data from Excel in examples:pandasSet upUsing package manager:pip install bio_rtdTo run examples, download or clone the github repo:git clone https://github.com/open-biotech/bio-rtd.gitand set local destination (/path_to/bio-rtd) as a working directory
or add it to the python path.Getting startedExamples can be run as scripts:python examples/models/single_pcc.pyModel examples ending with_gui.pycan be run viabokeh servecommand:bokeh serve examples/models/integrated_mab_gui.pyorpython bokeh serve examples/models/integrated_mab_gui.pyFor more information see theDocumentation.DocumentationThe documentation can be accessed athttps://open-biotech.github.io/bio-rtd-docs/or by building a local version.To build a local version of documentation install the following packages:pip install sphinx sphinx_autodoc_typehints sphinx_rtd_themerun command:make htmland opendocs/build/html/index.htmlwith a web browser.Meta informationDistributed under the MIT license. SeeLICENSEfor more information.For technical issues, bug reports and feature request useissue tracker.If you want to contribute to the code, seeDevelopers guide.If you are using the library in your projects please letus know.
This way we know how much interest there is, what is the scope of usage,
the needs, etc. This information influences the future development of the project.E-mail:jure.sencar@boku.ac.atUniversity of Natural Resources and Life Sciences (BOKU), ViennaReferencing the libraryIf you are using this package for a scientific publication,
please add the following reference:Senčar, J., (2020) GitHub Repository,https://github.com/open-biotech/bio-rtd.AcknowledgementsThis work was supported by:The Federal Ministry for Digital and
Economic Affairs (bmwd), the Federal Ministry for Transport,
Innovation and Technology (bmvit),
the Styrian Business Promotion Agency SFG,
the Standortagentur Tirol,
Government of Lower Austria,
and ZIT - Technology Agency of the City of Vienna
through the COMET-Funding Program managed
by the Austrian Research Promotion Agency FFGBaxalta Innovations GmbH (now part of Takeda)Bilfinger Industrietechnik Salzburg GmbH
Package: biorxiv-cli
AboutA Python wrapper for the bioRxiv API.Install$pipinstallbiorxiv-cliUsageYou can usebioRxiv-CLIas a CLI to navigate bioRxiv or as a library to
query its API.CLICurrentlybioRxiv-CLIimplements one subcommand:$biorxivreadSUBJECTSThis will print the titles of the 30 most recent posts in one of the
comma-separated subjects. For example:If no subjects were given, it will assume by default that all subjects
were selected.APIThe previous CLI is built on top of a Python library that can be used on its
own to query bioRxiv’s API. For example:>>>frombiorxiv_cliimportClient>>>client=Client()>>>client.read([SUBJECTS])will achieve the same effect as$biorxivreadSUBJECTSAuthorJacopo Notarstefano (@Jaconotar)LicenseMIT
Package: biorxiv-retriever
BiorxivRetrieverSimple python retriever for Biorxiv articles.PurposeGiven a text query, search for relevant papers on Biorxiv and get their links and content.InstallInstall with pip:pip install biorxiv_retrieverAlternatively, clone this repo and install:git clone https://github.com/TalSchuster/BiorxivRetriever.git
python setup.py installAPIfrom biorxiv_retriever import BiorxivRetriever
br = BiorxivRetriever()
papers = br.query('covid remdesivir')To get only titles and links without metadata and full text (when possible):papers = br.query('covid remdesivir', metadata=False, full_text=False)To useRxivistas the search engine (provides extra metadata but doesn't have all papers):br = BiorxivRetriever(search_engine='rxivist')OutputExample output:[
{'title': 'Enisamium is a small molecule inhibitor of the influenza A virus and SARS-CoV-2 RNA polymerases',
'biorxiv_url': 'https://www.biorxiv.org//content/10.1101/2020.04.21.053017v2',
'posted': 'May 11, 2020.',
'abstract': 'Abstract Influenza A virus and coronavirus strains cause a mild to severe respiratory disease that can result in death. Although vaccines exist against circulating influenza A viruses, such vaccines are ineffective against emerging pandemic influenza A viruses. Currently, no vaccine exists against coronavirus infections, including pandemic SARS-CoV-2, the causative agent of the Coronavirus Disease 2019 (COVID-19). To combat these RNA virus infections, alternative antiviral strategies are needed. A key drug target is the viral RNA polymerase, which is responsible for viral RNA synthesis. In January 2020, the World Health Organisation identified enisamium as a candidate therapeutic against SARS-CoV-2. Enisamium is an isonicotinic acid derivative that is an inhibitor of multiple influenza B and A virus strains in cell culture and clinically approved in 11 countries. Here we show using in vitro assays that enisamium and its putative metabolite, VR17-04, inhibit the activity of the influenza virus and the SARS-CoV-2 RNA polymerase. VR17-04 displays similar efficacy against the SARS-CoV-2 RNA polymerase as the nucleotide analogue remdesivir triphosphate. These results suggest that enisamium is a broad-spectrum small molecule inhibitor of RNA virus RNA synthesis, and implicate it as a possible therapeutic option for treating SARS-CoV-2 infection. Unlike remdesivir, enisamium does not require intravenous administration which may be advantageous for the development of COVID-19 treatments outside a hospital setting.',
'full_text': '...'}
]
Package: biosamples-v4
BioSamples v4 Python LibraryThis is a small library which aims to provide a Python client
to access BioSamples v4 new API.The client is still under heavy development, so any contribution is very
well welcomed.Local DevelopmentThis project usespipenvto handle
both the virtual environment and the dependencies.
Follow the instructions available on pipenv site to see how to install it.Once you have pipenv installed on you machine, just typepipenvinstall
Package: bioscience
bioScience: A new Python science library for High-Performance Computing Bioinformatics AnalyticsDeployment & Documentation & StatsBioScience is an advanced Python library designed to satisfy the growing data analysis needs in the field of bioinformatics by leveraging High-Performance Computing (HPC). This library encompasses a vast multitude of functionalities, from loading specialised gene expression datasets (microarrays, RNA-Seq, etc.) to pre-processing techniques and data mining algorithms suitable for this type of datasets. BioScience is distinguished by its capacity to manage large amounts of biological data, providing users with efficient and scalable tools for the analysis of genomic and transcriptomic data through the use of parallel architectures for clusters composed of CPUs and GPUs.BioScienceis featured for:Unified APIs, detailed documentation, and interactive examplesavailable to the community.Complete coveragefor generate biological results from gene co-expression datasets.Optimized modelsto generate results in the shortest possible time.Optimization of a High-Performance Computing (HPC) and Big Data ecosystem.InstallationIt is recommended to usepipfor installation. Please make surethe latest versionis installed, as bioScience is updated frequently:pipinstallbioscience# normal installpipinstall--upgradebioscience# or update if neededpipinstall--prebioscience# or include pre-release version for new featuresAlternatively, you could clone and run setup.py file:gitclonehttps://github.com/aureliolfdez/bioscience.gitpipinstall.Required Dependencies:Python>=3.10numpy>=1.26.0pandas>=2.1.1scikit-learn>=1.3.1numba>=0.58.0API demoimportbioscienceasbsif__name__=="__main__":# RNA-Seq dataset loaddataset=load(path="datasets/rnaseq.txt",index_gene=0,index_lengths=1,naFilter=True,head=0)# RNA-Seq preprocessingbs.tpm(dataset)# Binary preprocessingbs.binarize(dataset)# Data mining phaselistModels=bs.bibit(dataset,cMnr=2,cMnc=2,mode=3,deviceCount=1,debug=True)# Save resultsbs.saveGenes(path="/path/",models=listModels,data=dataset)Citing bioScience:bioScienceis published in(under review).
If you use bioScience in a scientific publication, we would appreciate citations to the following paper:Under reviewKey Links and Resources:View the latest codes on GithubView the documentation & APIView all examples
Package: bioscraping
#BioscrapingWeb scrapers to interact with remote databases programatically in Python
that makes a local cache of web data with sqlite3 to prevent excessive web
traffic.So far, implemented:[Uniprot](http://uniprot.org) by uniprot protein ID. (e.g. ‘Q8BP71’)[PubMed](www.ncbi.nlm.nih.gov/pubmed/) by PMID (e.g. ‘24213538’)#Install##Python 2.7.x and 3.xpip install bioscraping#TestReal unit tests are absent, but you can test basic functionality withpython test/not_a_real_test.py.#Usage##PubMedfrom bioscraping import PubMedClientpubmed = PubMedClient()defaults to writing a file called.bioscraping.pubmed.sqlite.db. UsePubMedClient(“:memory:”)for in-memory data storage.pubmed.fetch(<PMID>)Returns text with author and abstract for PMID.##Uniprotfrom bioscraping import UniprotClientuniprot = UniprotClient()defaults to writing a file called.bioscraping.uniprot.sqlite.db. UseUniprotClient(“:memory:”)for in-memory data storage.uniprot.fetch(<Uniprot ID>)Returns a dictionary of data parsed from xml.#Buyer bewareUniprotClient has a potential race condition and tempfile needs to be
implemented before it is safe for concurrent processes. (see TODO)
Package: bioshake_device
This Python package (bioshake_device) creates a class named
BioshakeDevice, which contains an instance of
serial_device2.SerialDevice and adds methods to it to interface to
Q.instruments BioShake devices.Authors:Peter Polidoro <polidorop@janelia.hhmi.org>License:BSDExample Usage:from bioshake_device import BioshakeDevice
dev = BioshakeDevice() # Might automatically find device if one available
# if it is not found automatically, specify port directly
dev = BioshakeDevice(port='/dev/ttyUSB0') # Linux
dev = BioshakeDevice(port='/dev/tty.usbmodem262471') # Mac OS X
dev = BioshakeDevice(port='COM3') # Windows
dev.get_description()
dev.shake_on(speed_target=1000) # speed_target (rpm)
dev.get_shake_actual_speed()
dev.shake_off()
dev.temp_on(temp_target=45) # temp_target (°C)
dev.get_temp_actual()
dev.temp_off()
devs = BioshakeDevices()  # Might automatically find all available devices
# if they are not found automatically, specify ports to use
devs = BioshakeDevices(use_ports=['/dev/ttyUSB0','/dev/ttyUSB1']) # Linux
devs = BioshakeDevices(use_ports=['/dev/tty.usbmodem262471','/dev/tty.usbmodem262472']) # Mac OS X
devs = BioshakeDevices(use_ports=['COM3','COM4']) # Windows
dev = devs[0]
Package: biosiglive
biosiglivebiosigliveis a python library dedicated to streaming and processing biosignals in real time from Nexus or Delsys Trigno Community.
Please have a look to the documentation for more information aboutbiosiglive.How to installBiosiglive can be installed from anaconda, pip or from the sources.
Please refer to thedocumentationto install biosiglivebiosigliveAPISeveral modules are implemented in biosiglive to allow versatile use. Please refer to thedocumentationfor more information.
You can also look at theexampleto have a better overview of how to use biosiglive.ExamplesA set of example is provided in theexamplesfolder. Please take a look at thedocumentation example pagefor description of each example.How to contributeYou are welcome to contribute to this project by following the steps describes in thehow to contributepage.How to citeIf you are using the biosig live library please cite us by following the guidelines availablehere.
Package: biosimulations-bigg
BioSimulations-BiGGBioSimulations-BiGG provides a command-line application for publishing
theBiGG model repositoryof flux balance
models to theBioSimulationsrepository
for simulation projects.This command-line program is run weekly by the GitHub action in this
repository.InstallationRequirementsPython >= 3.7pipInstall latest revision from GitHubpip install git+https://github.com/biosimulations/biosimulations-bigg.git#biosimulations-biggAPI documentationAPI documentation is availablehere.LicenseThis package is released under theMIT license. BiGG is
licensed under theBiGG License.Development teamThis package was developed by theKarr Labat the Icahn School of Medicine at Mount Sinai in New York and theCenter for Reproducible Biomedical
Modeling. BiGG was developed by
theSystems Biology Research
Groupat the University of
California, San Diego with assistance from the contributors listedhere.Contributing to BioSimulations-BiGGWe enthusiastically welcome contributions to BioSimulations-BiGG! Please
see theguide to contributingand thedeveloper’s
code of conduct.AcknowledgementsThis work was supported by National Institutes of Health award
P41EB023912.Questions and commentsPlease contact theBioSimulations
Teamwith any questions or comments
about this package. Please contact theBiGG
Teamwith any questions
or comments about BiGG.
Package: biosimulations-modeldb
BioSimulations-ModelDBBioSimulations-ModelDB provides a command-line application for
publishing theModelDB model
repositoryof neural models
to theBioSimulationsrepository for
simulation projects.This command-line program is run weekly by the GitHub action in this
repository.InstallationRequirementsPython >= 3.7pipXPPInstall latest revision from GitHubgit clone --recurse-submodules https://github.com/biosimulations/biosimulations-modeldb
pip install biosimulations-modeldb/API documentationAPI documentation is availablehere.LicenseThis package is released under theMIT license.Development teamThis package was developed by theKarr Labat the Icahn School of Medicine at Mount Sinai in New York and theCenter for Reproducible Biomedical
Modeling. ModelDB was developed by
theSense Labat Yale University
with assistance from the contributors listedhere.Contributing to BioSimulations-ModelDBWe enthusiastically welcome contributions to BioSimulations-ModelDB!
Please see theguide to contributingand thedeveloper’s code of conduct.AcknowledgementsThis work was supported by National Institutes of Health award
P41EB023912.Questions and commentsPlease contact theBioSimulations
Teamwith any questions or comments
about this package. Please contact theModelDB
Teamwith any questions or comments
about ModelDB.