Saravanan666/pypi-org huggingface dataset:
Package: biocypher
BioCypherLicensePythonPackageBuild statusTestsDockerDevelopmentContributions❓ DescriptionKnowledge graphs (KGs) are anapproach to knowledge
representationthat uses graph
structure to facilitate exploration and analysis of complex data, often
leveraging semantic information. They are popular in many research areas,
including the life sciences, due to their versatile use, for instance in data
storage, integration, reasoning, and more recently in artificial intelligence.
The creation of KGs is a complex task; BioCypher helps you in creating and
maintaining your own KG. For more overview, usage notes, and a tutorial, read
the docshere.📖 DocumentationTutorial and developer docs athttps://biocypher.org. For a quickstart into your
own pipeline, you can refer to ourproject
template, and for an overview of
existing and planned adapters for resources and outputs, as well as other
features, visit ourGitHub Project
Board.⚙️ Installation / UsageInstall the package from PyPI usingpip install biocypher. More comprehensive
installation and configuration instructions can be foundhere.Exemplary usage of BioCypher to build a graph database is shown in our tutorial
and the various pipelines we have created. You can find these on theComponents
Project Board.🤝 Getting involvedWe are very happy about contributions from the community, large and small!
If you would like to contribute to BioCypher development, please refer to
ourcontribution guidelines. :)If you want to ask informal questions, talk about dev things, or just chat,
please join our community athttps://biocypher.zulipchat.com!Imposter syndrome disclaimer:We want your help. No, really. There may be a little voice inside your head that is telling you that you're not ready, that you aren't skilled enough to contribute. We assure you that the little voice in your head is wrong. Most importantly, there are many valuable ways to contribute besides writing code.This disclaimer was adapted from thePoochproject.✍️ CitationThe BioCypher paper has been peer-reviewed inNature
Biotechnology. It is
available as a self-archived version onZenodo.  Before, it was available as a
preprint athttps://arxiv.org/abs/2212.13543.AcknowledgementsThis project has received funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No 965193 for DECIDER
and No 116030 for TransQST.
Package: bio-datasets
Bio-datasetsOpen-source collection of biology datasets and pre-trained embeddings. :dna: :closed_book:Descriptionbio-datasets is a collaborative framework that allows the user to fetch publicly available sequence-based protein datasets.
For these datasets, pre-trained contextual embeddings are also available.InstallationInstall the required dependencies withpip install bio-datasets.How it worksfrombiodatasetsimportlist_datasets,load_datasetprint(list_datasets())# Load your datasetpathogen=load_dataset("pathogen")# Display the available columns and embeddingsprint(pathogen)# Get data from your datasetX,y=pathogen.to_npy_arrays(input_names=["sequence"],target_names=["class"])embeddings=pathogen.get_embeddings("sequence","protbert","cls")# Get a full description of your datasetpathogen.display_description()How to contributeCheck out how to setup the project oradd a public datasetinCONTRIBUTING.md.
Package: biodblinker
biodblinkerA library for linking entities of biological knowledge bases.This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.Installingpip install biodblinkerInstalling from sourcepython setup.pyUsagefrombiodblinkerimportUniprotLinkeruniprot_linker=UniprotLinker()# Get list of all included uniprot accessionsuniprot_accessions=uniprot_linker.uniprot_idsselect_accessions=['P31946','P62258','Q04917']# Get the list of names for each accession in select_accessionsselect_names=uniprot_linker.convert_uniprot_to_names(select_accessions)# Get the list of kegg gene ids for each accession in select_accessionsselect_genes=uniprot_linker.convert_uniprot_to_kegg(select_accessions)Use Case - Linking uniprot proteins and mesh diseases via KEGGimportrequestsfrombiodblinkerimportKEGGLinkerlinker=KEGGLinker()unique_pairs=set()url='http://rest.kegg.jp/link/hsa/disease'resp=requests.get(url)ifresp.ok:forlineinresp.iter_lines(decode_unicode=True):kegg_disease,kegg_gene=line.strip().split('\t')# strip the prefix from the diseasekegg_disease=kegg_disease.split(':')[1]# prefix is retained for genes as the ids are numericuniprot_protein=linker.convert_geneid_to_uniprot([kegg_gene])mesh_disease=linker.convert_disease_to_mesh([kegg_disease])iflen(uniprot_protein[0])==0:continueiflen(mesh_disease[0])==0:continueforproteininuniprot_protein[0]:fordiseaseinmesh_disease[0]:unique_pairs.add((protein,disease))forprotein,diseaseinunique_pairs:print(f'{protein}\tRELATED_DISEASE\t{disease}')Downloading mappingsWhen a biodblinker is initialized it verifies that all necessary mapping files are present and if not downloads the precompiled mappingsBuilding the mapping filesIt is also possible to generate the mappings from their sourcesNote this process will take several hours and requires a large ammount of disk space due to the size of the source files. The source files are removed once the mappings are generatedimportbiodblinkergen=biodblinker.MappingGenerator()gen.generate_mappings(<drugbank_username>,<drugbank_password>)Mapping sources and licensesBioDBLinker uses multiple sources to generate the mappings. BioDBLinker must be used in compliance with these licenses and citation policies where applicableSource DatabaseLicense TypeURLUniProtCC BY 4.0https://www.uniprot.org/help/licenseDrugbankCC BY NC 4.0https://www.drugbank.ca/legal/terms_of_useKEGGCustomhttps://www.kegg.jp/kegg/legal.htmlSiderCC BY-NC-SAhttp://sideeffects.embl.de/about/StitchCC BY 4.0http://stitch.embl.de/cgi/download.plHPACC BY SA 3.0https://www.proteinatlas.org/about/licenceCellosaurusCC BY 4.0https://web.expasy.org/cgi-bin/cellosaurus/faq#Q22FundingThe development of this module has been fully supported by the CLARIFY project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 875160.
Package: biodbs
bioDBsbioDBs is a Python package for getting data from biological databases.List of the databasesBioMartKEGGQuickGOHPAHow to useBioMartfrombiodbsimportBioMartds=BioMart.Dataset(dataset_name="hsapiens_gene_ensembl")ds.get_data(attribs=["ensembl_gene_id","ensembl_transcript_id","entrezgene_id","hgnc_symbol","uniprotswissprot"],ensembl_gene_id=["ENSG00000139618","ENSG00000272104"])KEGGfrombiodbsimportKEGG# list available databasesKEGG.list_database()kegg=KEGG.KEGGdb()# get entry linkskegg.list_entry_link(target_db="pathway",source_db="hsa")QuickGOfrombiodbsimportQuickGOqgo=QuickGO.QuickGOdb()# search by text and list the resultsqgo.list_search_result("mito")# save a GO-term tree chartqgo.save_chart(["GO:1903695","GO:1904922","GO:0000423","GO:0042645"],"test.png")Human Protein Atlas (HPA)frombiodbsimportHPAhpa=HPA.HPAdb()# list downloadable tsv filesprint(HPA.DOWNLOADABLE_DATA)# download tsv file from HPAhpa.download_HPA_data("normal_tissue",saved_path="./")
Package: biodec.recipe.riak
zc.buildout recipe for downloading and installing Riak.[![Build Status](https://secure.travis-ci.org/biodec/biodec.recipe.riak.png)](http://travis-ci.org/biodec/biodec.recipe.riak)see src/biodec/recipe/biodec/README.txtCreditsCode fromhttp://pypi.python.org/pypi/rod.recipe.rabbitmqpackage has been used
as a starting point.A brief documentationThis recipe takes a number of options:erlang-pathThe path where to find the erlc command (default = find it in your path).urlThe URL to download the Riak source distribution.prefixPrefix path (default = <buildout directory>).cookieOptional string passed as cookie to the erl runtime (-setcookie)TestsWe will define a buildout template used by the recipe:>>> buildout_cfg = """
... [buildout]
... parts = riakbuild
... offline = true
...
... [riakbuild]
... recipe = biodec.recipe.riak:build
... url = http://s3.amazonaws.com/downloads.basho.com/riak/CURRENT/riak-1.2.0.tar.gz
...
... """We’ll start by creating a buildout:>>> import os.path
>>> write('buildout.cfg', buildout_cfg)Running the buildout gives us:>>> print system(buildout)
Installing riakbuild.
...
==> rel (compile)
==> riakbuild (compile)
Compiled src/etop_txt.erl
...Re-run buildout:>>> print system(buildout)
Updating riakbuild.Changes1.0.0a1 (2012-10-29)first public release.
Package: bioeval
bioevalCoNLL-2000 style evaluation of data using BIO and BEISO representation for
mutli-token entities (i.e. chunks).InstallIn the root folder execute:pip install bioevalChange Logpypi releaseand automated CI releasesbioevalnow supports pandasDataFameobjects throughbioeval.evaluate_df.UsageThe library supports two ways of evaluating span annotation. The first is the
native format way while the second uses a pandas DataFrame format.Native input formatThe native input format is a set of tuples, where each tuple signifies the
group of tokens in a span. Tokens are also denoted by tuples that are supposed
to be unique. The user can achieve that uniqueness through adding a unique
identifier to each token as in the example bellow.frombioevalimportevaluate# gold chunkschunk={((1,'Gold','N','B-NP'),),((2,'is','V','B-MV'),),((3,'green','J','B-AP'),),((4,'.','.','B-NP'),),((5,'The','D','B-NP'),(6,'red','J','I-NP'),(7,'square','N','I-NP')),((8,'is','V','B-MV'),),((9,'very','A','B-AP'),(10,'boring','J','I-AP')),((11,'.','.','O'),)}# candidate chunksguess_chunk={((1,'Gold','N','B-NP'),),((2,'is','V','I-NP'),),((3,'green','J','B-AP'),),((4,'.','.','B-NP'),),((5,'The','D','B-NP'),(6,'red','J','I-NP')),((7,'square','N','O'),),((8,'is','V','B-MV'),),((9,'very','A','B-AP'),(10,'boring','J','I-AP')),((8,'.','.','O'),)}# evaluationf1,pr,re=evaluate(gold_sequence=chunk,guess_sequence=guess_chunk,chunk_col=3)print(f1)# 71.43Dataframe formatThe library supports dataframes input through the use of theevaluate_dfmethod, which needs the additionalchunkcolandguesscolparameters to
specify the gold and candidate spans.importpandasaspdfrombioevalimportevaluate_df# input data as a JSON parsed to a DataFrame objectdf=pd.DataFrame([{'chunktag':'B-foo','guesstag':'B-foo'},{'chunktag':'I-foo','guesstag':'I-foo'},{'chunktag':'O','guesstag':'O'},{'chunktag':'B-bar','guesstag':'B-bar'},{'chunktag':'B-foo','guesstag':'B-foo'},{'chunktag':'O','guesstag':'O'},{'chunktag':'B-foo','guesstag':'B-foo'},{'chunktag':'I-foo','guesstag':'I-foo'},{'chunktag':'B-bar','guesstag':'B-bar'},{'chunktag':'I-bar','guesstag':'I-bar'},{'chunktag':'O','guesstag':'O'},{'chunktag':'B-foo','guesstag':'B-foo'},{'chunktag':'B-bar','guesstag':'I-foo'},{'chunktag':'B-foo','guesstag':'B-foo'},{'chunktag':'I-foo','guesstag':'B-foo'}])f1,pr,re=evaluate_df(df=df,chunkcol='chunktag',guesscol='guesstag')print(f1)>>>62.5
Package: bioevents
A lightweight library for managing and manipulating time series events,
with a focus on biosensing applications.Free software: MIT licenseDocumentation:https://bioevents.readthedocs.io.FeaturesRobust tooling for working with time series events.A hypnogram class with visualization, manipulation, and characterization functionalityCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.
Package: bioexplorer
Blue Brain BioExplorer Python SDKThe bioexplorer package provides an extended python API for the Blue Brain BioExplorer applicationInstallation1. From the Python Package Index(venv)$ pip install bioexplorer2. From sourceClone the repository and install it:(venv)$ git clone https://github.com/BlueBrain/BioExplorer.git
(venv)$ cd BioExplorer/bioexplorer/pythonsdk
(venv)$ pip install .APIConnect to running Blue Brain BioExplorer instancefrombioexplorerimportBioExplorerbio_explorer=BioExplorer('localhost:5000')SnapshotThe following example illustrates how to connect to the Blue Brain BioExplorer and export a snapshot of the current view to disk. The snapshot is exported to the /tmp folder, with a resolution of 512x512, and with 16 samples per pixel.frombioexplorerimportBioExplorer,MovieMakerbio_explorer=BioExplorer('localhost:5000')movie_maker=MovieMaker(bio_explorer)movie_maker.create_snapshot(renderer='bio_explorer',path='/tmp',base_name='test',,size=[512,512],samples_per_pixel=16)MovieThe following example illustrates how to connect to the Blue Brain BioExplorer and generate a set of frames according to some given camera control points. Frames are exported to the /tmp folder.frombioexplorerimportBioExplorer,MovieMakerbio_explorer=BioExplorer('localhost:5000')movie_maker=MovieMaker(bio_explorer)control_points=[{'apertureRadius':0.0,'direction':[0.0,0.0,-1.0],'focusDistance':0.0,'origin':[0.5,0.5,1.5],'up':[0.0,1.0,0.0]},{'apertureRadius':0.0,'direction':[-0.482,-0.351,-0.802],'focusDistance':0.0,'origin':[2.020,1.606,3.030],'up':[-0.199,0.936,-0.289]}]movie_maker.build_camera_path(control_points=control_points,nb_steps_between_control_points=50,smoothing_size=50)movie_maker.set_current_frame(10)movie_maker.create_movie(path='/tmp',size=[512,512],samples_per_pixel=16,start_frame=10,end_frame=20)Upload to pypitwineuploaddist/*
Package: biofaker
BioFakerA Python package that generates fake biodata.Free software: MIT licenseDocumentation:https://biofaker.readthedocs.ioGitHub repo:https://github.com/robertopreste/biofakerFeaturescreate fake DNA sequences;create fake RNA sequences;TBCUsagePlease refer to theUsagesection of the documentation for further information.InstallationPlease refer to theInstallationsection of the documentation for further information.CreditsThis package was created withCookiecutterand thecc-pypackageproject template.History0.1.0 (2020-04-07)First release.0.1.1 (2020-06-07)Add method to create fake DNA sequences.0.1.2 (2020-06-08)Add method to create fake RNA sequences.
Package: biofile
Bioinformatics Tool: bioFileIntroductionRetrieve data from various file formats used in RNA-Seq data analysis. The tool currently support:GTF file: genomic annotationsGFF file: genomic annoationsquick installationpip install biofileDevelopmentgit clone git@github.com:Tiezhengyuan/bio_file.git
cd bio_file
source venv/bin/activateRun unit testing:pytest tests/unittestsQuick tourProcess GFF:Retrieve annotations by features from <gff_file>. Multiple json files would be stored in <out_dir>from biofile import GFF
g = GFF(gff_file, out_dir)
g.split_by_features()Given an attribute, retrieve annotations from <gff_file>. and save dataframe in <out_dir>. Here, search all mRNA according to transcript_id. All related annotations are included. The output is transcript_id_mRNA.txt.from biofile import GFF
g = GFF(gff_file, out_dir)
g.parse_attributes('transcript_id', 'mRNA')Process GTF:Retrieve annotations by features from <gtf_file>. Multiple json files would be stored in <out_dir>from biofile import GTF
g = GTF(gtf_file, out_dir)
g.split_by_features()Given an attribute, retrieve annotations from <gtf_file>. and save dataframe in <out_dir>. Here, search all mRNA according to transcript_id. All related annotations are included. The output is transcript_id_mRNA.txt.from biofile import GTF
g = GTF(gtf_file, out_dir)
g.parse_attributes('transcript_id', 'mRNA')
Package: bioflex
bioflex: Python package for calling BioTuring APIbioflexallows scientists to use simple yet powerful commands to retrieve gene expression data,cell metadata from thousands of single-cell studies in BioTuring Database.InstallationpipinstallbioflexFor access token, register atBioTuring Data ScienceRequirementsRequestsNumPySciPytqdmH5PyExamplesCreate a connection using access token:importbioflexconnection=bioflex.connect('70d2acfda3a54ca6a4390699394****')List available databases:databases=connection.databases()[DataBase(id="5010c7d573ae4ff2b9691422b99aa2cd",
          name="BioTuring database",species="human",version=1),
DataBase(id="5010c7d573ae4ff2b9691422b99aa2cd",
          name="BioTuring database",species="human",version=2),
DataBase(id="5010c7d573ae4ff2b9691422b99aa2cd",
          name="BioTuring database",species="human",version=3)]Get database cell types gene expression summarydatabase=databases[2]database.get_celltypes_expression_summary(['CD3D','CD3E']){'CD3D': [Summary(name="B cell",sum=707108874.0,mean=4192.7096,rate=0.035,count=168652.0,total=4812967),
	Summary(name="CD4-positive, alpha-beta T cell",sum=9489987442.0,mean=4657.5619,rate=0.5283,count=2037544.0,total=3856590),
	...
	Summary(name="corneal progenitor",sum=0.0,mean=0.0,rate=0.0,count=0.0,total=3973),
	Summary(name="nucleus pulposus progenitor cell",sum=0.0,mean=0.0,rate=0.0,count=0.0,total=2310)]}Create study instance, using study hash ID fromBioTuring studies:study=database.get_study('GSE96583_batch2')studyStudy(id="GSE96583_batch2",
      title="Multiplexed droplet single-cell RNA-sequencing using natural genetic variation (Batch 2)",
      reference="https://www.nature.com/articles/nbt.4042")Take a peek at study metadata:study.metalist[Metadata(id=0,name="Number of mRNA transcripts",type="Numeric"),
 Metadata(id=1,name="Number of genes",type="Numeric"),
 Metadata(id=2,name="Batch id",type="Category"),
 Metadata(id=3,name="Stimulation",type="Category"),
 Metadata(id=4,name="Author's cell type",type="Category")]Fetch a study metadata:metadata=study.metalist[4]metadataMetadata(id=4,name="Author's cell type",type="Category")metadata.fetch()metadata.valuesarray(['CD8 T cells', 'Dendritic cells', 'CD4 T cells', ...,
       'CD8 T cells', 'B cells', 'CD4 T cells'], dtype='<U17')Query genes:exp_mtx=study.query_genes(['CD3D','CD3E'],bioflex.UNIT_LOGNORM)exp_mtx<29065x2 sparse matrix of type '<class 'numpy.float32'>'
    with 15492 stored elements in Compressed Sparse Column format>Get study barcodes:study.barcodes()['GSM2560249_AAACATACCAAGCT-1',
 'GSM2560249_AAACATACCCCTAC-1',
 ...
 'GSM2560249_AATTGTGATTCACT-1',
 'GSM2560249_AATTGTGATTTCGT-1',
 ...]Get study features:study.features()['5S_RRNA',
 '5_8S_RRNA',
 ...
 'AC006273',
 'AC006277',
 ...]Get study full matrix:study.matrix(bioflex.UNIT_LOGNORM)<29065x64642 sparse matrix of type '<class 'numpy.float32'>'
	with 17570739 stored elements in Compressed Sparse Column format>Export Study:study.export_study(bioflex.EXPORT_H5AD){'download_link': 'https://talk2data.bioturing.com/api/export/a1003bad3dd146b28c7bda913a2fc3f0',
'study_hash_id': 'GSE96583_batch2'}For further information please check thedocumentation.
Package: biofluff
fluffSeefull fluff documentationfor detailed installation instructions and usage examples.Quick InstallationPlease note: from version 3.0 on, fluff only works on Python 3.6+ and Python 2.x will no longer be supported.
For a Python 2 version you can install fluff 2.1.4.
Keep in mind that most scientific Python software will stop supporting Python 2 in 2020:https://python3statement.org/.The most straightforward way to install fluff is withcondausing thebiocondachannel.If you have not used bioconda yet, first set up the necessary channels (in this order):$ conda config --add channels defaults
$ conda config --add channels bioconda
$ conda config --add channels conda-forgeNow you can install fluff:$ conda install biofluffQuick UsageSee the quick examples in theUsagesection.CitingIf you find fluff useful, please cite:Georgiou G, van Heeringen SJ. (2016) fluff: exploratory analysis and visualization of high-throughput sequencing data. PeerJ 4:e2209doi: 10.7717/peerj.2209.HelpFull documentation:http://fluff.readthedocs.io/.The preferred way to get support is through the GithubissuespageFinally, you can reach me bymailorTwitter.ContributorsSimon van Heeringen (@simonvh)Georgios Georgiou (@georgeg9)Zhuoqing Fang (@BioNinja)LicenseThis module is licensed under the terms of theMIT
license.
Package: bioformats-jar
bioformats_jardeprecatedThis package is deprecated.  You should usescyjavato download and import jars.  The functionality in this
package can be accomplished using scyjava as follows:importjpypeimportscyjavascyjava.config.endpoints.append('ome:formats-gpl:6.7.0')scyjava.start_jvm()loci=jpype.JPackage("loci")loci.common.DebugTools.setRootLevel("ERROR")This package remains only for packages that already depend on it.usagefrombioformats_jarimportget_loci,set_loci_log_level# start the JVM and get the loci moduleloci=get_loci()# optionally:set_loci_log_level("DEBUG")# by default "ERROR"The following environment variables can also be used:BIOFORMATS_VERSION- version of bioformats to use.  by default "LATEST"BIOFORMATS_LOG_LEVEL- logging level for loci tools. by default "ERROR"BIOFORMATS_LICENSE- license version of bioformats to use.  must be either"gpl"or"bsd".  By default"gpl"see also:Bioformats DocsJavadocswith the loci APIinstallpipinstallbioformats-jarcondainstall-cconda-forgebioformats-jar
Package: bioframe
Bioframe: Operations on Genomic Interval DataframesBioframe enables flexible and scalable operations on genomic interval dataframes in Python.Bioframe is built directly on top ofPandas. Bioframe provides:A variety of genomic interval operations that work directly on dataframes.Operations for special classes of genomic intervals, including chromosome arms and fixed-size bins.Conveniences for diverse tabular genomic data formats and loading genome assembly summary information.Read thedocs, including theguide, as well as thebioframe preprintfor more information.Bioframe is an Affiliated Project ofNumFOCUS.InstallationBioframe is available onPyPIandbioconda:pipinstallbioframeContributingInterested in contributing to bioframe? That's great! To get started, check out thecontributing guide. Discussions about the project roadmap take place on theOpen2C Slackand regular developer meetings scheduled there. Anyone can join and participate!Interval operationsKey genomic interval operations in bioframe include:overlap: Find pairs of overlapping genomic intervals between two dataframes.closest: For every interval in a dataframe, find the closest intervals in a second dataframe.cluster: Group overlapping intervals in a dataframe into clusters.complement: Find genomic intervals that are not covered by any interval from a dataframe.Bioframe additionally has functions that are frequently used for genomic interval operations and can be expressed as combinations of these core operations and dataframe operations, including:coverage,expand,merge,select, andsubtract.Tooverlaptwo dataframes, call:importbioframeasbfbf.overlap(df1,df2)For these two input dataframes, with intervals all on the same chromosome:overlapwill return the following interval pairs as overlaps:Tomergeall overlapping intervals in a dataframe, call:importbioframeasbfbf.merge(df1)For this input dataframe, with intervals all on the same chromosome:mergewill return a new dataframe with these merged intervals:See theguidefor visualizations of other interval operations in bioframe.File I/OBioframe includes utilities for reading genomic file formats into dataframes and vice versa. One handy function isread_tablewhich mirrors pandas’s read_csv/read_table but provides aschemaargument to populate column names for common tabular file formats.jaspar_url='http://expdata.cmmt.ubc.ca/JASPAR/downloads/UCSC_tracks/2022/hg38/MA0139.1.tsv.gz'ctcf_motif_calls=bioframe.read_table(jaspar_url,schema='jaspar',skiprows=1)TutorialsSee thisjupyter notebookfor an example of how to assign TF motifs to ChIP-seq peaks using bioframe.CitingIf you usebioframein your work, please cite:@article{bioframe_2022,author={Open2C and Abdennur, Nezar and Fudenberg, Geoffrey and Flyamer, Ilya and Galitsyna, Aleksandra and Goloborodko, Anton and Imakaev, Maxim and Venev, Sergey},doi={10.1101/2022.02.16.480748},journal={bioRxiv},title={{Bioframe: Operations on Genomic Intervals in Pandas Dataframes}},year={2022}}