Saravanan666/pypi-org huggingface dataset:
Package: biomart
Python API that consumes the biomart webservice.What it will do:Show all databases of a biomart serverShow all datasets of a biomart databaseShow attributes and filters of a biomart datasetRun your query formatted as a Python dict and return the Biomart response as TSV format.What it won’t do:Process and return the results as JSON,XML,etc.UsageImport Biomart modulefrom biomart import BiomartServerConnect to a Biomart Serverserver = BiomartServer( "http://www.biomart.org/biomart" )

# if you are behind a proxy
import os
server.http_proxy = os.environ.get('http_proxy', 'http://my_http_proxy.org')

# set verbose to True to get some messages
server.verbose = TrueInteract with the biomart server# show server databases
server.show_databases() # uses pprint behind the scenes

# show server datasets
server.show_datasets() # uses pprint behind the scenes

# use the 'uniprot' dataset
uniprot = server.datasets['uniprot']

# show all available filters and attributes of the 'uniprot' dataset
uniprot.show_filters()  # uses pprint
uniprot.show_attributes()  # uses pprintRun a search# run a search with the default attributes - equivalent to hitting "Results" on the web interface.
# this will return a lot of data.
response = uniprot.search()
response = uniprot.search( header = 1 ) # if you need the columns header

# response format is TSV
for line in response.iter_lines():
  line = line.decode('utf-8')
  print(line.split("\t"))

# run a count - equivalent to hitting "Count" on the web interface
response = uniprot.count()
print(response.text)

# run a search with custom filters and default attributes.
response = uniprot.search({
  'filters': {
      'accession': 'Q9FMA1'
  }
}, header = 1 )

response = uniprot.search({
  'filters': {
      'accession': ['Q9FMA1', 'Q8LFJ9']  # ID-list specified accessions
  }
}, header = 1 )

# run a search with custom filters and attributes (no header)
response = uniprot.search({
  'filters': {
      'accession': ['Q9FMA1', 'Q8LFJ9']
  },
  'attributes': [
      'accession', 'protein_name'
  ]
})Shortcut function: connect directly to a biomart datasetThis is short in code but it might be long in time since the module needs to fetch all server’s databases to find your dataset.from biomart import BiomartDataset

interpro = BiomartDataset( "http://www.biomart.org/biomart", name = 'entry' )

response = interpro.search({
  'filters': { 'entry_id': 'IPR027603' },
  'attributes': [ 'entry_name', 'abstract' ]
})
Package: biomass
BioMASSis a computational framework for modeling and analysis of biological signaling systems in Python.Documentation:https://biomass-core.rtfd.ioSource code:https://github.com/biomass-dev/biomassBug reports:https://github.com/biomass-dev/biomass/issuesCiting in your work:https://biomass-core.rtfd.io/en/latest/citing.htmlIt provides useful tools for numerical simulation, parameter estimation, network analysis, and result visualization.InstallationThe BioMASS library is available at thePython Package Index (PyPI).$pipinstallbiomassBioMASS supports Python 3.8 or newer.ReferencesImoto, H., Zhang, S. & Okada, M. A Computational Framework for Prediction and Analysis of Cancer Signaling Dynamics from RNA Sequencing Data—Application to the ErbB Receptor Signaling Pathway.Cancers12, 2878 (2020).https://doi.org/10.3390/cancers12102878Imoto, H., Yamashiro, S. & Okada, M. A text-based computational framework for patient -specific modeling for classification of cancers.iScience25, 103944 (2022).https://doi.org/10.1016/j.isci.2022.103944AuthorHiroaki ImotoLicenseApache License 2.0
Package: biomatx
pybiomatxPython API for managing the Biomatx home automation systemRequirementspybiomatx requires Python >=3.9InstallationIt is recommended to install withpipx, ifpipxhaven't been installed yet, refer to thepipx's docs$pipxinstallbiomatxAlternatively, install withpipto the user site:$python-mpipinstall--userbiomatx
Package: biomedicus
BioMedICUSThe BioMedical Information Collection and Understanding System (BioMedICUS) is a system for large-scale text analysis and processing of biomedical and clinical reports. The system is being developed by the Natural Language Processing and Information Extraction Program at the University of Minnesota Institute for Health Informatics.This is a collaborative project that aims to serve biomedical and clinical researchers, allowing for customization with different texts.More information about BioMedICUS can be found on ourwebsite.PrerequisitesPython >=3.9,<=3.11.Java JDK 11+. Note, you will need to have the"java" command on the your "$PATH".InstallationInstallation instructions are available on our website.GuidesAdditional guides for usage and development are also available on our website.ContactBioMedICUS is developed by theNLP/IE Groupat the University of Minnesota Institute for Health Informatics. You can contact us atnlp-ie@umn.edu.
Package: biomedicus-client
BioMedICUS ClientThe BioMedical Information Collection and Understanding System (BioMedICUS) is a system for large-scale text analysis and processing of biomedical and clinical reports. The system is being developed by the Natural Language Processing and Information Extraction Program at the University of Minnesota Institute for Health Informatics.This is a collaborative project that aims to serve biomedical and clinical researchers, allowing for customization with different texts.More information about BioMedICUS can be found on ourwebsite.This package contains the client code for processing documents using deployed BioMedICUS services. The package containing the services themselves isbiomedicus.PrerequisitesPython 3.8+Java JDK 11+. Note, you will need to have the"java" command on the your "$PATH".InstallationInstallation instructions are available on our website.GuidesAdditional guides for usage and development are also available on our website.ContactBioMedICUS is developed by theNLP/IE Groupat the University of Minnesota Institute for Health Informatics. You can contact us atnlp-ie@umn.edu.
Package: biometalib
# BiometalibBiometalib is a set of useful libraries and tools for working with SRA biological metadata.## InstallationThis library is designed for python 3+ and can be installed withpiporconda.### PipBiometalib can be installed using pip.`bash pip install-ybiometalib `Or the latest version can be installed by pip.`bash pip installgit+https://github.com/jfear/sramongopip installgit+https://github.com/jfear/biometalib`### Conda [Suggested]First make sure you have a working installation of Anaconda, I suggest
[Miniconda](https://conda.io/miniconda.html).`bash conda install-cjfear biometalib `## Attribute SelectorAttribute selector is a helper script for selecting which attributes you want
to focus on for a project. The biological metadata submitted by users contain a
variety of different types of attributes. Sometimes these include things like
misspellings or different forms of a word, it also includes attributes that are
unique to a single project. This tool is to be used to quickly curate these
columns. Attribute selector uses aYAMLformatted file to store attribute
decisions.In the YAML file,selected attributeswill be the keys. When merging
multiple attributes into a singleselected attributethey will be stored as
values. For example:` sex: - sex - Sex - gender `Here theselected attributesexhas the attributesSexandgenderassociated with it.  There is also a specialselected attributeignorethat will store a list
of attributes that you want to ignore.Using the BioSample selection sheet I have created a starting YAML that can be
used when runningattribute_selector.To run the attributes selector on my public version of the Biometa database type:`bash # Download example YAML $ wget-Omy_attribute_selection.yamlhttps://raw.githubusercontent.com/jfear/biometalib/master/data/flybase_example.yaml$ attribute_selector--hostmongo.geneticsunderground.com--port27022--dbsra--usernamesra--passwordoliver--authenticationDatabaseuser-data--configmy_attribute_selection.yaml `attribute_selectoris an interactive command line tool. Iterates overall
attribute column names that are not alreadyselected attributesin the
YAML. The current attribute is displayed in red. At the prompt you can type:kto set the current attribute as aselected attribute[keep]rto rename the current attribute, this will set the current attribute as
value of the renamedselected attribute[rename]iadds current attribute to ignore list [ignore]eshow example values listed under the current attribute [example]sshow attributes with similar names (fuzzy string match). Hereselected attributeswill appear in yellow [similar]nskip and go to the next attribute [next]quitexit out of the program, but save progress.
Package: biometatools
biometatoolsA repository of tools for performing meta analysis on high throughput
sequencing data and genome features defined as genomic regions. Sequencing
data are most often requested in indexed BAM file format and features in BED
file format.Examples of some questions that these tools are useful for:What is the distribution of reads around the 5' ends of genes?What is the distribution of reads along the length of all mRNAs?Installationpip install biometatools
Package: biome-text
Natural Language Processing library built with AllenNLPQuick LinksDocumentationFeaturesState-of-the-art and not so state-of-the-art models trained withyour own datawith simple workflows.Efficient data readingfor (large) datasets in multiple formats and sources (CSV, Parquet, JSON, etc.).Modular configuration and extensibilityof models, datasets and training runs programmatically or via config files.Use viaclior as plain Python (e.g., inside a Jupyter Notebook)Compatible with AllenNLPInstallationFor the installation we recommend setting up a freshcondaenvironment:condacreate-nbiomepython~=3.7.0pip>=20.3.0
condaactivatebiomeOnce the conda environment is activated, you can install the latest release via pip:pipinstall-Ubiome-textAfter installingbiome.text, the best way to test your installation is by running thebiome.textcli command:biome--helpGet startedThe best way to see howbiome.textworks is to go through ourfirst tutorial.Please refer to ourdocumentationfor more tutorials, detailed user guides and how you cancontributetobiome.text.LicensingThe code in this project is licensed under Apache 2 license.
Package: biometrics
# biometricsPackage to generate sample based biometrics[![Build Status](https://travis-ci.com/msk-access/biometrics.svg?branch=master)](https://travis-ci.com/msk-access/biometrics) [![PyPi](https://img.shields.io/pypi/v/biometrics.svg?)](https://pypi.python.org/pypi/biometrics)Free software: Apache Software License 2.0Documentation:https://msk-access.gitbook.io/biometrics/GitHub:https://github.com/msk-access/biometrics/## InstallationFrom pypi:pip install biometricsFrom conda (python v3.6 only):conda create -n myenv -c bioconda -c conda-forge -c msk-access python=3.6 biometricsHistory0.1.1 (2021-01-07)First release on PyPI.
Package: biom-format
BIOM: Biological Observation Matrixhttp://www.biom-format.orgThe Biological Observation Matrix (BIOM) format or: how I learned to stop
worrying and love the ome-ome
Daniel McDonald, Jose C Clemente, Justin Kuczynski, Jai Ram Rideout,
Jesse Stombaugh, Doug Wendel, Andreas Wilke, Susan Huse, John Hufnagle,
Folker Meyer, Rob Knight, J Gregory Caporaso
GigaScience 2012, 1:7.
Package: bioml-tasks
BioML Tasks Python clientThis is a Python client forBioML Tasks. It lets you run ML APIs from your Python code or Jupyter notebook.Installpipinstallbioml_tasksAuthenticateBefore running any Python scripts that use the API, you need to set your BioML Tasks API token in your environment.Grab your token frombiomltasks.com/settingsand set it as an environment variable:export BIOML_TASKS_API_TOKEN=<your token>We recommend not adding the token directly to your source code, because you don't want to put your credentials in source control. If anyone used your API key, their usage would be charged to your account.Run a modelCreate a new Python file and add the following code:>>>importbioml_tasks>>>bioml_tasks.run("<user_name>/<api_name>",input={"prompt":"a 19th century portrait of a wombat gentleman","another_arg":"its value"})Errors and Exceptionsbioml_tasks.exceptions.MlApiNotFoundError- Raised when trying to run an API that does not exist, or that the user doesn't have access to.bioml_tasks.exceptions.MlApiNotDeployedError- Raised when trying to run an API that exists, but is not deployed.bioml_tasks.exceptions.MlApiDeploymentError- Raised when trying to run an API that has been deployed but is not running. This is usually due to insufficient compute or incorrect setup code.bioml_tasks.exceptions.MlApiError- An error from the deployed ML API.bioml_tasks.exceptions.BioMlTasksError- A generic error for all other errors.bioml_tasks.exceptions.BioMlTasksException- The base class that all the above errors inherit from.DevelopmentSeeCONTRIBUTING.md
Package: biomodels-restful-api-client
BioModels RESTful Web ServicesIntroductionThis package implements theRESTfulAPI exposed in BioModels repository. All API endpoints can be followed up atBioModels' RESTful Web Services.Installationpip install biomodels-restful-api-clientExamplesFetch model metadata>>> from biomodels_restful_api_client import services as bmservices
>>> bmservices.get_model_info("BIOMD0000000900")
{'name': 'Bianca2013 - Persistence analysis in a Kolmogorov-type model for cancer-immune system competition', ..., 
'submissionId': 'MODEL1912180002', 'publicationId': 'BIOMD0000000900'}Fetch model identifiers>>> bmservices.get_model_identifiers()
{'hits': 2874, 'models': ['BIOMD0000000001', 'BIOMD0000000002', 'BIOMD0000000003', 'BIOMD0000000004', ...,
'MODEL9808533471', 'MODEL9810152478', 'MODEL9811206584']}DevelopersTung NguyenContactBioModels DevelopersLicencePlease readLICENSEfor information on the software availability and distribution.
Package: bio-MOSAIC
Ortholog detection (OD) is a critical first step for a variety of comparative genomic and phylogenetic inference tasks. In general, existing OD methods can be classified as tree-based, graph-based, or a hybrid of the two. Comparative performance between these approaches varies by dataset, however, frustrating attempts to identify a single best method. In contrast, MOSAIC improves ortholog detection by leveraging complementary between diverse OD methods. The result is striking gains in the number of detected orthologs relative to existing approaches, while simultaneously maintaining or improving functional-, phylogenetic-, and sequence identity-based measures of ortholog quality.
Package: bionemo
BioNeMo Service Python ClientThe BioNeMo Service Python Client provides a python API into the BioNeMo inference service.
The BioNeMo service usesNVIDIA Tritoninference infrastructure to deploy ML models relevant to biology and chemistry applications.
This client exposes easy-to-use python functions that call directly to the inference service, allowing users to use state-of-the-art models with little setup.Learn more and apply for access to BioNeMo here.Example: Generate novel protein sequences and perform foldingfrombionemo.apiimportBionemoClientfromtimeimportsleep# Create a client instanceapi=BionemoClient("APIKEY")# Generate novel proteinsnovel_proteins=api.protgpt2_sync(max_length=200,num_return_sequences=10)# Request folding of novel proteins in parallelsubmitted_requests=[]forproteininnovel_proteins["generated_sequences"]:request_id=api.openfold_async(protein)submitted_requests.append(request_id)# Wait for results, write to diskwhilelen(submitted_requests):sleep(10)forrequest_idinsubmitted_requests:ifapi.fetch_task_status(request_id)=="DONE":folded_protein=api.fetch_result(request_id)withopen(str(request_id)+".pdb","w")asf:f.write(folded_protein)submitted_requests.remove(request_id)Currently, the following models and workflows are available for inference:Protein Structure Prediction:AlphaFold-2OpenFoldESMFoldProtein Embedding:ESM-2ESM-1nvDocking:DiffdockProtein Sequence Generation:ProtGPT2Small Molecule Embedding and Generation:MegaMolBartMoFlowMolMIMMultiple Sequence Alignment
Package: bionemo-controlled-generation
BioNemo Controlled GenerationBioNemo controlled generation is a python library to facilitate guided generation of novel molecules using the BioNemo
cloud service.For details, API documentation, and examples, please visit theBioNemo service documentation.Installationbionemo-controlled-generationis available as a pip package.pipinstallbionemobionemo-controlled-generationLicensePlease read the terms of usagehere.
Package: bionic
BionicBionic is a framework for analyzing and modeling data in Python.  It's designed
to help youiterate faster on your research, and help your colleaguesreuse your code more easily.Bionic is in alpha and evolving rapidly.  We recommend it for research projects
where the dataset fits in memory.  We do not recommend it for pipelines running
in production.Check out thefull documentation,
or go straight toGet
Started.InstallationBionic can be installed from PyPI:pip install bionic[standard]You'll probably want to installGraphvizas well.
See theInstallation
docsfor more details on installing and configuring Bionic's dependencies.ContributingSee theContributionsection of our docs.LicenseCopyright 2019 Square, Inc.Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Package: bionic-writer
Bionic WriterBionicWriterisaPythonlibrarytowritetextwithBionicReadingstyleforanykindofformat.About Bionic Reading methodFrom:bionic-reading.com/Bionic Reading® revises texts so that the most concise parts of words are highlighted. This guides the eye over the text and the brain remembers previously learned words more quickly. Save precious time. Gain advantages. Learn new things faster.Does Bionic Reading really speed-up your reading speed? According to thispaper, probably not. Whatsoever it's still a nice screen reading technique that seems to help many people to stay focused during their reading sessions.UsageInstallationpipinstallbionic-writerQuickstart examplesWrite a Bionic Reading text in Markdown format:importbionic_writertext="Lorem ipsum dolor sit amet, consectetur adipiscing elit."bionic_md=bionic_writer.write(text=text,affix="**",postfix="**")print(bionic_md)Output:**Lo**rem **ip**sum **do**lor **s**it **am**et, **conse**ctetur **adipi**scing **el**it.Write a Bionic Reading text in HTML format:importbionic_writertext="Lorem ipsum dolor sit amet, consectetur adipiscing elit."bionic_html=bionic_writer.write(text=text,affix="<b>",postfix="</b>")print(bionic_html)Output:<b>Lo</b>rem <b>ip</b>sum <b>do</b>lor <b>s</b>it <b>am</b>et, <b>conse</b>ctetur <b>adipi</b>scing <b>el</b>it.
Package: bionorm
bionormnormalizes and validates genomic data files prior to further processing
or inclusion in a data store such as that of theLegume Federation.PrerequisitesPython 3.6 or greater is required.
This package is tested under Linux and MacOS using Python 3.7.Installation for UsersInstall via pip or (better yet)pipx:pipx install bionormbionormcontains some long commands and many options.  To enable command-line
completion forbionormcommands, execute the following command if you are usingbashas your shell:eval "$(_BIONORM_COMPLETE=source_bash bionorm)"For DevelopersIf you plan to developbionorm, you’ll need to install
thepoetrydependency manager.
If you haven’t previously installedpoetry, execute the command:curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | pythonNext, get the master branch from GitHubgit clone https://github.com/legumeinfo/bionorm.gitChange to thebionorm/directory and install with poetry:poetry install -vRunbionormwithpoetry:poetry run bionormUsageInstallation puts a single script calledbionormin your path.  The usage format is:bionorm [GLOBALOPTIONS] COMMAND [COMMANDOPTIONS][ARGS]Global OptionsThe following options are global in scope and, if used, must be placed beforeCOMMAND. Not all commands support every global option:-v, –verboseLog debugging info to stderr.-q, –quietSuppress logging to stderr.–no-logfileSuppress logging to file.-e, –warnings_as_errorsTreat warnings as fatal (for testing).CommandsA listing of commands is available viabionorm--help.
The currently implemented commands are:prefix_fastaPrefix FASTA files for data store standard.prefix_gffPrefix and sort GFF3 file for data store standard.buscoPerform BUSCO checks.detectorDetect/correct incongruencies among files.fastaCheck for GFF/FASTA consistency.generate_readmeGenerates a README file with details of genome.indexIndexes FASTA file.Each command has itsCOMMANDOPTIONS, which may be listed with:bionorm COMMAND --helpProject StatusLatest ReleaseGitHubLicenseTravis BuildCoverageCode GradeDependenciesPre-commitIssues
Package: bionty-base
Bionty-baseAccess public biological ontologies.User docs:hereDeveloper docs:hereInstallationBionty-base is a Python package available forpipinstallbionty-baseEntitiesGene-Ensembl,NCBI GeneProtein-UniprotOrganism-Ensembl Species.NCBI TaxonomyCellLine-Cell Line OntologyCellType-Cell OntologyCellMarker-CellMarkerTissue-UberonDisease-Mondo,Human Disease,ICDPhenotype-Human Phenotype,Phecodes,PATO,Mammalian Phenotype,Zebrafish PhenotypePathway-Gene Ontology,Pathway OntologyExperimentalFactor-Experimental Factor OntologyDevelopmentalStage-Human Developmental Stages,Mouse Developmental StagesDrug-Drug OntologyEthnicity-Human Ancestry OntologyBFXPipeline- largely based onnf-coreBioSample-NCBI BioSample attributesCheck outsources.yamlfor details.Didn't see your favorite source or version? Bionty isextendable!Entity versionsimportbionty_baseasbt# display currently used sourcesbt.display_currently_used_sources()# display all managed sourcesbt.display_available_sources()# local yaml file specifying all managed sourcesbt.LOCAL_SOURCES# access to the Mondo ontologydisease=bt.Disease(source="mondo")# access to the Human Disease ontologydisease=bt.Disease(source="doid",version="2023-01-30")
Package: bionumpy
Documentation:https://bionumpy.github.io/bionumpy/What is BioNumPy?BioNumPy is a Python library, built on top of NumPy, for enabling array programming on biological datasets in Python.
BioNumPy aims to make it easy to read common bioinformatics file formats efficiently into NumPy-like data structures
that enable efficient operations and analysis of the data. Working in BioNumPy should feel much like working in NumPy.Getting startedInstall with pip:$ pip install bionumpyImport BioNumPy and read your data, e.g.:>>> import bionumpy as bnp
>>> import numpy as np
>>> f = bnp.open("example_data/big.fq.gz")
>>> # chunk contains the sequences of reads and NumPy-functions can be used
>>> for chunk in f:
...      print(np.sum(chunk.sequence == "G"))
53686Check out the getting started guide and various tutorials in thedocumentation.You can also find the latest version of theBioNumPy manuscript here.
Package: biopandas
Biopandas is a python package for working with molecular structures
in pandas DataFrames.ContactIf you have any questions or comments about biopandas,
please feel free to contact me via
eMail:mail@sebastianraschka.comor Twitter:https://twitter.com/rasbtThis project is hosted athttps://github.com/rasbt/biopandasThe documentation can be found athttp://rasbt.github.io/biopandas/