Saravanan666/pypi-org huggingface dataset:
Package: bio2bel-drugbank
Converts DrugBank 5.0 to BEL.Installationbio2bel_drugbankcan be installed easily fromPyPIwith
the following code in your favorite terminal:$python3-mpipinstallbio2bel_drugbankor from the latest code onGitHubwith:$python3-mpipinstallgit+https://github.com/bio2bel/drugbank.git@masterSetupWarningDrugBank requires a bit of downloading and file organization. Will be documented soon.Python REPL>>>importbio2bel_drugbank>>>drugbank_manager=bio2bel_drugbank.Manager()>>>drugbank_manager.populate()Command Line Utilitybio2bel_drugbankpopulateCitationsWishart, D. S.,et al.(2018).DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Research, 46(D1), D1074–D1082.
Package: bio2bel-entrez
This package downloads, parses, and stores the Entrez Gene and HomoloGene databases then provides utilities for
converting to BEL.Installationbio2bel_entrezcan be installed easily fromPyPIwith
the following code in your favorite terminal:$python3-mpipinstallbio2bel_entrezor from the latest code onGitHubwith:$python3-mpipinstallgit+https://github.com/bio2bel/entrez.git@masterSetupEntrez can be downloaded and populated from either the Python REPL or the automatically installed command line
utility.Python REPL>>>importbio2bel_entrez>>>entrez_manager=bio2bel_entrez.Manager()>>>entrez_manager.populate()Command Line Utilitybio2bel_entrezpopulateCitationsMaglott, D.,et al.(2011).Entrez Gene: gene-centered information at NCBI. Nucleic Acids Research, 39(Database issue), D52–D57.
Package: bio2bel-expasy
This repository downloads and parses the enzyme classes from the ExPASy ENZYME databaseInstallationbio2bel_expasycan be installed easily fromPyPIwith the
following code in your favorite terminal:$python3-mpipinstallbio2bel_expasyor from the latest code onGitHubwith:$python3-mpipinstallgit+https://github.com/bio2bel/expasy.git@masteror check theinstallation instructions.SetupExPASy can be downloaded and populated from either the Python REPL or the automatically installed command line
utility.Python REPL>>>importbio2bel_expasy>>>expasy_manager=bio2bel_expasy.Manager()>>>expasy_manager.populate()Command Line Utilitybio2bel_expasypopulateProgrammatic InterfaceTo enrich the proteins in a BEL Graph with their enzyme classes, use:>>> from bio2bel_expasy import enrich_proteins
>>> graph = ... # get a BEL graph
>>> enrich_proteins(graph)CitationsGasteiger, E.,et al.(2003).ExPASy: The proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Research, 31(13), 3784–8.
Package: bio2bel-famplex
Protein complex and family hierarchies from FamPlex.Installationbio2bel_famplexcan be installed easily fromPyPIwith the following code in your favorite terminal:$python3-mpipinstallbio2bel_famplexor from the latest code onGitHubwith:$python3-mpipinstallgit+https://github.com/bio2bel/famplex.gitSetupFamPlex can be downloaded and populated from either the
Python REPL or the automatically installed command line utility.Python REPL>>>importbio2bel_famplex>>>famplex_manager=bio2bel_famplex.Manager()>>>graph=famplex_manager.to_bel()
Package: bio2bel-go
Converts the Gene Ontology (GO) to BELInstallationbio2bel_gocan be installed easily fromPyPIwith
the following code in your favorite terminal:$python3-mpipinstallbio2bel_goor from the latest code onGitHubwith:$python3-mpipinstallgit+https://github.com/bio2bel/go.git@masterSetupGO can be downloaded and populated from either the Python REPL or the automatically installed command line
utility.Python REPL>>>importbio2bel_go>>>go_manager=bio2bel_go.Manager()>>>go_manager.populate()Command Line Utilitybio2bel_gopopulateCitationAshburner, M.,et al.(2000).Gene ontology: tool for the unification of biology.
The Gene Ontology Consortium. Nature Genetics, 25(1), 25–9.Linkshttp://geneontology.org/page/ontology-documentation
Package: bio2bel-hgnc
This package creates:HGNC BEL namespaceHGNC gene families BEL namespace,HGNC gene family membership BEL Scriptorthology BEL scriptInstallationbio2bel_hgnccan be installed easily fromPyPIwith the
following code in your favorite terminal:$python3-mpipinstallbio2bel_hgncor from the latest code onGitHubwith:$python3-mpipinstallgit+https://github.com/bio2bel/hgnc.git@masterSetupHGNC can be downloaded and populated from either the Python REPL or the automatically installed command line
utility.Python REPL>>>importbio2bel_hgnc>>>hgnc_manager=bio2bel_hgnc.Manager()>>>hgnc_manager.populate()Command Line Utilitybio2bel_hgncpopulateCitationsGray KA,et al.genenames.org: the HGNC resources in 2015. Nucleic
Acids Res. 2015 Jan;43(Database issue):D1079-85AcknowledgementsThis package heavily relies on Andrej Konotopez’s packagePyHGNC
Package: bio2bel-hippie
Converts protein-protein interactions in HIPPIE to BEL.Installationbio2bel_hippiecan be installed easily fromPyPIwith
the following code in your favorite terminal:$python3-mpipinstallbio2bel_hippieor from the latest code onGitHubwith:$python3-mpipinstallgit+https://github.com/bio2bel/hippie.git
Package: bio2bel-hmdd
Converts the Human microRNA Disease Database to BELCitationLi Y, Qiu C, Tu J, et al. HMDD v2.0: a database for experimentally supported human microRNA and disease associations. Nucleic Acids Research. 2014;42(Database issue):D1070-D1074. doi:10.1093/nar/gkt1023.AbstractHMDD (the Human microRNA Disease Database) is a database that curated experiment-supported evidence for human microRNA (miRNA) and disease associations. miRNAs are one class of important regulatory RNAs, which mainly repress gene express at the post-transcriptional level. Increasing reports have shown that miRNAs play important roles in various critical biological processes. For their importance, the dysfunctions of miRNAs are associated with a broad spectrum of diseases. The first version of HMDD was built on December 2007. Each entry in HMDD v1.0 has four items for annotation; they are miRNA name, disease name, the reference PubMed ID, and the evidence supporting the miRNA-association from the original paper. During the past five years, we updated HMDD for more than 30 times. HMDD v2.0 presents more detailed and comprehensive annotations to the human miRNA-disease association data, including miRNA-disease association data from the evidence of genetics, epigenetics, circulating miRNAs, and miRNA-target interactions. In addition, a “submission” function was implemented in the version 2.LinksDatabase:http://www.cuilab.cn/hmddData:http://www.cuilab.cn/files/images/hmdd2/alldata.txt
Package: bio2bel-hsdn
Converts the human symptoms-disease network produced by Zhou and Himmelstein to BELBig thanks to Daniel Himmelstein for preprocessing this data in his repository at:https://github.com/dhimmel/hsdnInstallationbio2bel_hsdncan be installed easily fromPyPIwith the following code in your favorite terminal:$python3-mpipinstallbio2bel_hsdnor from the latest code onGitHubwith:$python3-mpipinstallgit+https://github.com/bio2bel/hsdn.gitSetupThis repository does not currently cache the data, but only wraps conversion to BEL.Python REPL>>>importbio2bel_hsdn>>>hsdn_manager=bio2bel_hsdn.Manager()>>>graph=hsdn_manager.to_bel()
Package: bio2bel-interpro
Converts the InterPro protein families, domains, and other classes to ontological relations in BEL.Installationbio2bel_interprocan be installed easily fromPyPIwith the
following code in your favorite terminal:$python3-mpipinstallbio2bel_interproor from the latest code onGitHubwith:$python3-mpipinstallgit+https://github.com/bio2bel/interpro.git@masterSetupInterPro can be downloaded and populated from either the Python REPL or the automatically installed command line
utility.Python REPL>>>importbio2bel_interpro>>>interpro_manager=bio2bel_interpro.Manager()>>>interpro_manager.populate()Command Line Utilitybio2bel_interpropopulateProgrammatic InterfaceTo enrich the proteins in a BEL Graph with their InterPro entries (families, domains, sites, etc.) , use:>>> from bio2bel_interpro import enrich_proteins
>>> graph = ... # get a BEL graph
>>> enrich_proteins(graph)
Package: bio2bel-kegg
This package allows the enrichment of BEL networks with KEGG information by wrapping its RESTful API.
Furthermore, it is integrated in theComPath environmentfor pathway database comparison.If you find this package useful, please consider citing[domingofernandez2018]:[domingofernandez2018]Domingo-Fernandez, D.,et al(2018).ComPath: an ecosystem for exploring, analyzing,
and curating mappings across pathway databases.Npj Systems Biology and Applications, __5__(1), 3.WarningThis package createspartOfrelationships in BEL, but does not convert KEGG mechanistic
relationships to BEL. That functionality is implemented in thePathMe project.Installationbio2bel_keggcan be installed easily fromPyPIwith the
following code in your favorite terminal:$pipinstallbio2bel_keggor from the latest code onGitHubin development mode with:$gitclonehttps://github.com/bio2bel/kegg.git$cdkegg$pipinstall-e.SetupKEGG can be downloaded and populated from either the Python REPL or the automatically installed command line utility.Python REPL>>>importbio2bel_kegg>>>kegg_manager=bio2bel_kegg.Manager()>>>kegg_manager.populate()Command Line Utilitybio2bel_keggpopulateOther Command Line UtilitiesRun an admin site for simple querying and explorationpython3 -m bio2bel_kegg web(http://localhost:5000/admin/)Export gene sets for programmatic usepython3 -m bio2bel_kegg exportCitationKanehisa, Furumichi, M., Tanabe, M., Sato, Y., and Morishima, K.; KEGG: new perspectives on genomes,
pathways, diseases and drugs. Nucleic Acids Res. 45, D353-D361 (2017).Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M., and Tanabe, M.; KEGG as a reference resource
for gene and protein annotation. Nucleic Acids Res. 44, D457-D462 (2016).Kanehisa, M. and Goto, S.; KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27-30 (2000).
Package: bio2bel-mesh
Converts the MeSH hierarchy to BELInstallationbio2bel_meshcan be installed easily fromPyPIwith the
following code in your favorite terminal:$python3-mpipinstallbio2bel_meshor from the latest code onGitHubwith:$python3-mpipinstallgit+https://github.com/bio2bel/mesh.git@masterSetupMeSH can be downloaded and populated from either the Python REPL or the automatically installed command line utility.Python REPL>>>importbio2bel_mesh>>>mesh_manager=bio2bel_mesh.Manager()>>>mesh_manager.populate()Command Line Utilitybio2bel_meshpopulateLinksMeSH Tree BrowserSPARQL 1.1 SpecificationMeSH InformationMeSH FTPMeSH RDF Download
Package: bio2bel-mirbase
Enumerate miRBase namespace, produce equivalency table, and predicted MTI relationshipsInstallationbio2bel_mirbasecan be installed from the latest code onGitHubwith:$python3-mpipinstallgit+https://github.com/bio2bel/mirbase.git@masterReferenceshttp://www.mirbase.org/http://identifiers.org/mirbase/andhttp://identifiers.org/mirbase.mature/Ana Kozomara, Sam Griffiths-Jones;miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res 2014; 42 (D1): D68-D73.
Package: bio2bel-mirtarbase
Serializes text-mined miRNA-target interactions to BELInstallationbio2bel_mirtarbasecan be installed easily fromPyPIwith the
following code in your favorite terminal:$python3-mpipinstallbio2bel_mirtarbaseor from the latest code onGitHubwith:$python3-mpipinstallgit+https://github.com/bio2bel/mirtarbase.git@masterSetupmiRTarBase can be downloaded and populated from either the Python REPL or the automatically installed command line
utility.Python REPL>>>importbio2bel_mirtarbase>>>mirtarbase_manager=bio2bel_mirtarbase.Manager()>>>mirtarbase_manager.populate()Command Line Utilitybio2bel_mirtarbasepopulateCitationsChih-Hung Chou, et al; miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database.
Nucleic Acids Res 2016; 44 (D1): D239-D247. doi: 10.1093/nar/gkv1258Linkshttp://mirtarbase.mbc.nctu.edu.tw/
Package: bio2bel-msig
This package allows the enrichment of BEL networks with MSigDB information.
Furthermore, it is integrated in theComPath environmentfor pathway database comparison.If you find this package useful, please consider citing[domingofernandez2018]:[domingofernandez2018]Domingo-Fernandez, D.,et al(2018).ComPath: an ecosystem for exploring, analyzing,
and curating mappings across pathway databases.Npj Systems Biology and Applications, __5__(1), 3.WarningThis package createspartOfrelationships in BEL. MSigDB does not contain mechanistic relationships,
but it include simplifications of several sources (KEGG, WikiPathways, Reactome, PID) that do have mechanistic
relationships. Those sources can be converted to BEL with thePathMe project.Installationbio2bel_msigcan be installed easily fromPyPIwith the
following code in your favorite terminal:$pipinstallbio2bel_msigor from the latest code onGitHubin development mode with:$gitclonehttps://github.com/bio2bel/msig.git$cdmsig$pipinstall-e.SetupThe package expects you have downloaded the gene sets from MSigDB following the instructions and terms stated in
theirwebsite.The environment variableBIO2BEL_MSIG_PATHshould be set to the directory where the gene set files in the GMT format
are stored. Optionally, this can be directly overridden with the keyword argument topopulate()in the REPL or as
a flag in the command line utility.Python REPL>>>importbio2bel_msig>>>msig_manager=bio2bel_msig.Manager()>>>msig_manager.populate()Command Line Utilitybio2bel_msigpopulateOther Command Line UtilitiesRun an admin site for simple querying and explorationpython3 -m bio2bel_msig web(http://localhost:5000/admin/)Export gene sets for programmatic usepython3 -m bio2bel_msig exportCitationSubramanian, A.,et al.(2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545-15550.Liberzon, A.,et al(2011). Molecular signatures database (MSigDB) 3.0. Bioinformatics, 27(12), 1739-1740.
Package: bio2bel-pfam
Installationbio2bel_pfamcan be installed easily fromPyPIwith the following code in your favorite terminal:$python3-mpipinstallbio2bel_pfamor from the latest code onGitHubwith:$python3-mpipinstallgit+https://github.com/bio2bel/pfam.gitSetupPFAM can be downloaded and populated from either the
Python REPL or the automatically installed command line utility.Python REPL>>>importbio2bel_pfam>>>pfam_manager=bio2bel_pfam.Manager()>>>pfam_manager.populate()Command Line Utilitybio2bel_pfampopulate
Package: bio2bel-reactome
This package allows the enrichment of BEL networks with Reactome information by wrapping its RESTful API.
Furthermore, it is integrated in theComPath environmentfor pathway database
comparison.InstallationThis code can be installed withpip3 install git+https://github.com/bio2bel/reactome.gitThese two resources will be installed together with this package and can be quickly loaded by running the following
commands in your terminal:python3 -m bio2bel_hgnc populatepython3 -m bio2bel_chebi populateInstallationbio2bel_reactomecan be installed easily fromPyPIwith the
following code in your favorite terminal:$python3-mpipinstallbio2bel_reactomeor from the latest code onGitHubwith:$python3-mpipinstallgit+https://github.com/bio2bel/reactome.git@masterSetupReactome can be downloaded and populated from either the Python REPL or the automatically installed command line
utility.The following resources will be automatically installed and loaded in order to fully populate the tables of the
database:Bio2BEL CHEBIBio2BEL HGNCPython REPL>>>importbio2bel_reactome>>>reactome_manager=bio2bel_reactome.Manager()>>>reactome_manager.populate()Command Line Utilitybio2bel_reactomepopulateOther Command Line UtilitiesRun an admin site for simple querying and explorationpython3 -m bio2bel_reactome web(http://localhost:5000/admin/)Export gene sets for programmatic usepython3 -m bio2bel_reactome exportCitationFabregat, Antonio et al. “The Reactome Pathway Knowledgebase.” Nucleic Acids Research 44.Database issue (2016):
D481–D487. PMC. Web. 6 Oct. 2017.Croft, David et al. “The Reactome Pathway Knowledgebase.” Nucleic Acids Research 42.Database issue (2014): D472–D477.
PMC. Web. 6 Oct. 2017.
Package: bio2bel-sider
Converts the Side Effect Resource (SIDER) to BEL.Installationbio2bel_sidercan be installed easily fromPyPIwith
the following code in your favorite terminal:$python3-mpipinstallbio2bel_sideror from the latest code onGitHubwith:$python3-mpipinstallgit+https://github.com/bio2bel/sider.gitSetupSIDER can be downloaded and populated from either the Python REPL or the automatically installed command line
utility.Python REPL>>>importbio2bel_sider>>>sider_manager=bio2bel_sider.Manager()>>>sider_manager.populate()Command Line Utilitybio2bel_siderpopulateCitationsKuhn, M.,et al.(2016).The SIDER database of drugs and side effects. Nucleic Acids Research, 44(D1), D1075–D1079.
Package: bio2bel-uniprot
Converts UniProt to BELInstallationbio2bel_uniprotcan be installed easily fromPyPIwith
the following code in your favorite terminal:$python3-mpipinstallbio2bel_uniprotor from the latest code onGitHubwith:$python3-mpipinstallgit+https://github.com/bio2bel/uniprot.git@master
Package: bio2bel-wikipathways
This package allows the enrichment of BEL networks with WikiPathways information by wrapping its RESTful API.
Furthermore, it is integrated in theComPath environmentfor pathway database
comparison.Installationbio2bel_wikipathwayscan be installed easily fromPyPIwith
the following code in your favorite terminal:$python3-mpipinstallbio2bel_wikipathwaysor from the latest code onGitHubwith:$python3-mpipinstallgit+https://github.com/bio2bel/wikipathways.git@masterSetupWikiPathways can be downloaded and populated from either the Python REPL or the automatically installed command line
utility.The following resources will be automatically installed and loaded in order to fully populate the tables of the
database:Bio2BEL HGNCPython REPL>>>importbio2bel_wikipathways>>>wikipathways_manager=bio2bel_wikipathways.Manager()>>>wikipathways_manager.populate()Command Line Utilitybio2bel_wikipathwayspopulateOther Command Line UtilitiesRun an admin site for simple querying and explorationpython3 -m bio2bel_wikipathways web(http://localhost:5000/admin/)Export gene sets for programmatic usepython3 -m bio2bel_wikipathways exportCitationSlenter, D.N., et al WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research
Nucleic Acids Research, (2017) doi.org/10.1093/nar/gkx1064Kutmon, M., et al. WikiPathways: capturing the full diversity of pathway knowledge Nucl. Acids Res., 44, D488-D494
(2016) doi:10.1093/nar/gkv1024Kelder, T., et al. WikiPathways: building research communities on biological pathways. Nucleic Acids Res. 2012
Jan;40(Database issue):D1301-7
Package: bio96
Many medium-throughput experiments produce data in 24-, 96-, or 384-well plate
format.  However, it can be a challenge to keep track of which wells (e.g. A1,
B2, etc.) correspond to which experimental conditions (e.g. genotype, drug
concentration, replicate number, etc.) for large numbers of experiments.  It
can also be a challenge to write analysis scripts flexible enough to handle the
different plate layouts that will inevitably come up as more and more
experiments are run.Thebio96package solves these challenges by introducing a TOML-based file
format that succinctly describes the organization of wells on plates.  The file
format is designed to be human-readable and -writable, so it can serve as a
standalone digital record.  The file format can also parsed bybio96to
help write analysis scripts that will work regardless of how you (or your
collaborators) organize wells on your plates.DocumentationSee the complete documentationhere.
Package: bio-arc
ARC (Antigen Receptor Classifier)Authors: Austin Crinklaw, Swapnil MahajanRequirements:Linux OSHMMER3NCBI Blast+Python 3+Python packages: Pandas, BioPythonInstallation:We provide a Dockerfile for ease of use.ARC can also be downloaded through PyPI using the following pip command.pipinstallbio-arcTesting Installation:A quick check for proper dependencies and successful installation can be performed by navigating to your pip package install directory (which can be located by executingpip show bio-arc) and running the following command:python3-marc_testPassing all unit-tests means that your system is configured properly and ready to classify some protein sequences.Usage:InputA fasta format file with one or more protein sequences.>1WBZ_A_alpha I H2-Kb
MVPCTLLLLLAAALAPTQTRAGPHSLRYFVTAVSRPGLGEPRYMEVGYVDDTEFVRFDSDAENPRYEPRARWMEQEGPEYWERETQKAKGNEQSFRVDLRTLLGYYNQSKGGSHTIQVISGCEVGSDGRLLRGYQQYAYDGCDYIALNEDLKTWTAADMAALITKHKWEQAGEAERLRAYLEGTCVEWLRRYLKNGNATLLRTDSPKAHVTHHSRPEDKVTLRCWALGFYPADITLTWQLNGEELIQDMELVETRPAGDGTFQKWASVVVPLGKEQYYTCHVYHQGLPEPLTLRWEPPPSTVSNMATVAVLVVLGAAIVTGAVVAFVMKMRRRNTGGKGGDYALAPGSQTSDLSLPDCKVMVHDPHSLA
>1WBZ_B_b2m I H2-Kb
MARSVTLVFLVLVSLTGLYAIQKTPQIQVYSRHPPENGKPNILNCYVTQFHPPHIEIQMLKNGKKIPKVEMSDMSFSKDWSFYILAHTEFTPTETDTYACRVKHASMAEPKTVYWDRDMCommandsUsing Fasta file as an input:python-mARCclassify-i/path/to/input.fasta-o/path/to/output.csvOutputOutput file has 4 columns in CSV format.First column named 'ID' is the description provoded in the fasta for each sequence.Second column named 'class' is the assigned molecule class for each sequence.e.g. MHC-I, MHC-II, BCR or TCR.The third column named 'chain_type' is the assigned chain type for each sequence.e.g. alpha, beta, heavy, lambda, kappa, scFv, TscFv or construct. These will also be labelled as V for variable domain or C for constant domain.The fourth column named 'calc_mhc_allele' is the MHC allele identified using groove domain similarity to MRO alleles.IDclasschain_typecalc_mhc_allele1WBY_A_alpha I H2-DbMHC-Ialpha V1WBY_B_b2m I H2-Db1HQR_A_alpha II HLA-DRA01:01/DRB501:01MHC-IIalpha CHLA-DRA*01:011HQR_B_beta II HLA-DRA01:01/DRB501:01MHC-IIbeta CHLA-DRB5*01:012CMR_H_heavyBCRheavy V2CMR_L_lightBCRkappa C4RFO_L_lightBCRlambda V3UZE_A_heavyBCRscFv1FYT_D_alphaTCRalpha V1FYT_E_betaTCRbeta C3TF7_C_alphaTCRTscFvHow it works:BCR and TCR chains are identified using HMMs. A given protein sequence is searched against HMMs built using BCR and TCR chain sequences from IMGT. HMMER is used to align an input sequence to the HMMs.MHC class I (alpha1-alpha2 domains) and MHC class I alpha and beta chain HMMs are downloaded from Pfam website. An input protein sequence is searched against these HMMs. A HMMER bit score threshold of 25 was used to identify MHC chain sequences.To identify MHC alleles, groove domains (G-domains) are assigned based on the MRO repository.IgNAR sequences are identified through querying against a custom blast database.References:Several methods for HMMER result parsing were sourced from ANARCI.Dunbar J and Deane CM. ANARCI: Antigen receptor numbering and receptor classification. Bioinformatics (2016)
Package: bioat
bioatthe Bioinformatic Analysis Toolsabout authorauthor:赵华男 | ZHAO Hua-nanemail:hermanzhaozzzz@gmail.comZhihu|BLOGIntroductionA pythonpackage&command line toolkitfor Bioinformatics and data science!<under development>!!Installation# supported platform: Linux / MacOS (intel & arm64) / WSL on Windowspipinstall--upgradebioatusage# list commandsbioatlist# check versionbioatversion# check information about bioatbioatabout# example usagebioatbamremove_clip--help
samtoolsview-htest_sorted_n.bam|bioatbamremove_clip|tailcircos plot