Saravanan666/pypi-org huggingface dataset:
Package: biopantograph
Author:Nicolas LoiraDate:2016-04Biopantograph is a collection of python modules and programs for the reconstruction, curation and validation of metabolic models. It reads and writesSBMLfiles.InstallationThe recommended installation method is:pip install biopantographPantograph requireslibsbml, butpipwill take care of that requirement. If you don’t have permissions to install at the system level, try:pip install biopantograph--user. You can also clone and contribute from theBitBucket repository.ToolsThe basic script you need to know ispantograph, which will help you to reconstruct a metabolic model for your target species.pantographtakes as input an existing SBML model as a template, orthology data between your template and your target genes (fromInparanoidand/orOrthoMCL) and other optional data. Invokepantographas follows:$ pantograph -x scaffold.xml -i inparanoid.data -m orthomcl.data -s org1 -t org2 -o out.xml -l out.logWhere`scaffold.txt`is an existing, well-curated SBML metabolic model, used as a template for the reconstruction;inparanoid.datais the output from Inparanoid, comparing the genes from the template and the reconstructed model;orthomcl.datais the output from OrthoMCL (whereorg1is the template model id andorg2is the reconstructed model id);out.xmlis the SBML reconstructed model andout.logis the reconstruction log. You can reconstruct a model using any of the two orthology methods (Inparanoid or OrthoMCL), although better results are obtained using both.Thebiopantographpackage includes many other tools that can be useful for your reconstruction. Try them with the parameter--helpto obtain usage information.ToolDescriptionpantographCreate a new model, from a template model and orthology filesptg_addbiggreactionsAdd reactions from a list of BiGG ids, to a modelptg_projectorCreate a new model, using an existing one as templateptg_curateSBMLAdd new species/reactions to a model, from a CSVptg_inparanoid2relParses Inparanoid output for ptg_projectorptg_omcl2relParses OrthoMCL output for ptg_projectorThe modules provided can be used to develop your own applications:ModuleDescriptionpathtastictoolsAlgorithms to reconstruct metabolic models from a templateSBMLeditorHigh-level modifications to SBML modelsBiGGModule to handle calls to theBiGG Web APIBiGG2SBMLAdd elements from BiGG API to a SBML fileSBMLutilsHigh-level utilitiesBiopantograph has been tested under Linux and OS X. For more information, please visit the official Pantograph website:http://pathtastic.gforge.inria.fr. More documentation coming soon!ReferenceIf you use Pantograph for your own reconstructions, please cite:Nicolas Loira, Anna Zhukova and David J Sherman.Pantograph: A template-based method for genome-scale metabolic model reconstruction.J Bioinform Comput Biol, 13(2): 1550006, 2015.
DOI: 10.1142/S0219720015500067For any question, please contact: Nicolas Loiranloira@gmail.comLicenseThis project is open source and freely available under theGLP v2 license.
Package: biopartitioner
Bio PartitionerInstallFor Userpip install biopartitionerDemo Code:frombiopartitioner.factoryimportPartitionerFactorypartitioner_caller=PartitionerFactory.create_partitioner("vcf")dataset="fixtures/scaffold.vcf"vcf_partioner=partitioner_caller(dataset=dataset,partition_num=8)vcf_partioner.partition()You'll see vcf partition files at current folder.For DeveloperPython dependencies:virtualenv venv; . venv/bin/activatepip install poetrypoetry installNpm dependencies, for linter, formatter and commit linter (optional):brew install npmnpm ciRunnpm run testYou'll see 10 vcf partition files at your folderTesttest:npm run testRun all linter before commitment would save some effort:npm run check
Package: bio-past
PAST software is build on a variational graph convolutional auto-encoder designed for spatial transcriptomics which integrates prior information with Bayesian neural network, captures spatial information with self-attention mechanism and enables scalable application with ripple walk sampler strategy. PAST could effectively characterize spatial domains and facilitate various downstream analysis through integrating spatial information and reference from various sources. Besides, PAST also enable time and memory-efficient application on large datasets while preserving global spatial patterns for better performance. Importantly, PAST could also facilitate accurate annotation of spatial domains and thus provide biological insights.
Package: biopeaks
General Informationbiopeaksis a straightforward graphical user interface for feature extraction from electrocardiogram (ECG), photoplethysmogram (PPG) and breathing biosignals.
It processes these biosignals semi-automatically with sensible defaults and offers the following functionality:processes files in the open biosignal formatsEDF,OpenSignals (Bitalino)as well as plain text files (.txt, .csv, .tsv)interactive biosignal visualizationbiosignal segmentationbenchmarked, automatic extrema detection (R-peaks in ECG, systolic peaks in PPG, exhalation troughs and inhalation
peaks in breathing signals) with signal-specific, sensible defaultsautomatic state-of-the-artartifact correctionfor ECG and PPG extremamanual editing of extremaextraction of instantaneous features: (heart- or breathing-) rate and period, as well as breathing amplitude.csv export of extrema and instantaneous features for further analysis (e.g., heart rate variability)automatic analysis of multiple files (batch processing)Installationbiopeakscan be installed from PyPI:pip install biopeaksAlternatively, on Windows, downloadbiopeaks.exeand run it. Running the executable does not require a Python installation.You can find more details on the installationhere.DocumentationHave a look at theuser guideto get started withbiopeaks.Contributors welcome!Improvements or additions to the repository (documentation, tests, code) are welcome and encouraged.
Spotted a typo in the documentation? Caught a bug in the code? Ideas for improving the documentation,
increase test coverage, or adding features to the GUI? Get started with thecontributor guide.CitationPlease refer to thebiopeaks paperin The Journal of Open Source Software.ChangelogHave a look at thechangelogto get an overview of what has changed throughout the versions ofbiopeaks.
Package: biopipe
Pipeable commandline utilities for simple bioinformatics research. Currently on version v0.1.10.Free software: MIT licenseInstallationpip install biopipeDocumentationhttp://python-biopipe.readthedocs.io/en/latestDevelopmentTo run the all tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.1.0 (2018-04-21)First release on PyPI.
Package: bioplexpy
BioPlexPyPython-side access to PPI data from Gygi labInstallationThe package can be installed through pypi or through githubpip install bioplexpyorpip install git+https://github.com/ccb-hms/BioPlexPy.git#egg=BioPlexPyUsageSee theBioPlex examples notebookfor basic usage and how to obtain
BioPlex datasets.https://bioplexpy.readthedocs.io/en/latest/Running Testspython -m doctest BioPlexPy/to run the respective test for the fileWarning: By default, doctests will not print anything if tests are successful.
Add the-voption to print verbose output.
Package: bioplot
# BioplotBiotplot aims to help to draw amnio acid sequences with your defined colors.## Requirements* matplotlib > 3.0.2## how to install```$ pip install bioplot```## how to use```pythonimport matplotlib.pyplot as pltimport numpy as npfig = plt.figure()ax = fig.add_subplot(111)bplot = Bioplot(ax)seq = "MEKEKKVKYFLRKSAFGLASVSAAFLVGSTVFAVDSPIEDTPIIRNGGELTNLLGNSETTLALRNEESATADLTAAAVADTVAAAAAENAGAAAWEAAAAADALAKAKADALKEFNKYGVSDYYKNLINNAKTVEGVKDLQAQVVESAKKARISEATDGLSDFLKSQTPAEDTVKSIELAEAKVLANRELDKYGVSDYHKNLINNAKTVEGVKDLQAQVVESAKKARISEATDGLSDFLKSQTPAEDTVKSIELAEAKVLANRELDKYGVSDYYKNLINNAKTVEGVKALIDEILAALPKTDTYKLILNGKTLKGETTTEAVDAATAEKVFKQYANDNGVDGEWTYDDATKTFTVTEKPEVIDASELTPAVTTYKLVINGKTLKGETTTEAVDAATAEKVFKQYANDNGVDGEWTYDDATKTFTVTEKPEVIDASELTPAVTTYKLVINGKTLKGETTTKAVDAETAEKAFKQYANDNGVDGVWTYDDATKTFTVTEMVTEVPGDAPTEPEKPEASIPLVPLTPATPIAKDDAKKDDTKKEDAKKPEAKKEDAKKAETLPTTGEGSNPFFTAAALAVMAGAGALAVASKRKED"colors = np.random.randn(len(seq))bplot.aaplot2d(seq, colors=colors, max_width=20)plt.savefig("example.png")```## sample![sample](https://github.com/0h-n0/bioplot/raw/master/asset/example.png "sample")
Package: bioplottemplates
_this is an experimental project._Plotting templates common in biological sciences.Installationpip install bioplottemplatesYou can also install the in-development version with:pip install https://github.com/joaomcteixeira/python-bioplottemplates/archive/master.zipDocumentationhttps://python-bioplottemplates.readthedocs.io/DevelopmentTo run the all tests run:toxChangelog0.1.1 (2020-01-14)addedplotlabeldinterface forlabel_dots.0.1.0 (2019-12-20)addedxmin,xmax,yminandymaxoptions inplots.param.plot.Implemented client interface forparam.0.0.4 (2019-12-15)Implementedcli_labeldotsand generalcliinterfaceImplementedlabel_dotsplot templateRemoved spellcheck from Travis-CI (PR164)0.0.3 (2019-10-14)Supports just Python >= 3.6Coverage details reported just from Travis-CIAdded new stats badgesRemoved CI for OSX.0.0.0 (2019-10-10)First release on PyPI.
Package: bio-pm
A point mutation analyzing tool for nucleotide sequenceInstallationInstall through pip:pip install bio-pmOr manually (assuming all required modules are installed on your system):python ./setup.py installRequirementsPython >= 2.7biopythonExamplesAnalyze point mutation status usingpm.analyze(seq, stdseq, translate=True)>>>importpm>>>>>>stdseq='ATGGGCGC'>>>seq_with_gap='ATGGGCG-C'>>>pm.analyze(seq_with_gap,stdseq)<pm.status.NAobjectwith:gaps=1,nt_pm=1,aa_pm=0,stdseq='ATGGGCGC'>>>>Quickly compare betweenpm.statusobjectsp.statusobjects with same stdseqs have their internal order. That isY > Conserved > PM > NA.>>>importpm>>>>>>stdseq="ATGGGCGCT">>>seq_without_pm='ATGGGCGCT'>>>seq_conserved="ATGGGCGCC">>>seq_with_pm='ATGGGCGAT'>>>status_Y=pm.analyze(seq_without_pm,stdseq)>>>status_Conserved=pm.analyze(seq_conserved,stdseq)>>>status_PM=pm.analyze(seq_with_pm,stdseq)>>>>>>status_Y>status_Conserved>status_PMTrue>>>Help generate HGVS-like mutation formatCodes continues fromQuickly compare the point mutation status objects>>>frompm.patternimportmutant_to_str>>>>>>status_PM.pattern<pm.pattern.TranslatedPatternobjectat0x2b03c9cfdc18>>>>>>>fornt_pm,aa_pminstatus_PM.pattern.list():...print(mutant_to_str(*nt_pm)+'|'+mutant_to_str(*aa_pm))...8C>A|3A>DLicenceMIT licensed. See the bundledLICENSEfile for more details.Authorsbio-pmwas written byEkeyme Mo.
Package: bioportal
BioPortal APIsBioPortal APIs es un paquete de Python que permite accesar las bases de datos públicas del BioPortal del Departamento de Salud de Puerto Rico.Este paquete le permite a uno descargar y guardar datos públicos del BioPortal a cualquier momento. Presenta un formato sencillo para facilitar su uso a cualquiera que lo desee.PrerequisitosAntes de comenzar, asegúrese de haber cumplido los siguientes requisitos:Tienes instalado una versión de Python >= 3.6Estas utilizando una computadora con Windows, Mac, o Linux.Instalando BioPortal APIsPara instalar BioPortal APIs, abre una ventana del Command Line o Terminal y corra:pipinstallbioportalUsando BioPortal APIsSiga estos pasos en un programa de Python para descargar datos de APIs públicos:frombioportalimportBioPortalClientcliente=BioPortalClient()casos_por_coleccion=cliente.descargar_dataset('Casos por fecha de coleccion')casos_por_coleccion.to_csv('Casos_por_fecha_coleccion.csv')El resultado será un archivo conteniendo una tabla en formato csv.
Además, puede ver cuales APIs estan disponibles usando:cliente.datasets_disponibles()Bases de datos disponibles:
  1   'Cantidades totales de pruebas reportadas'
  2   'Pruebas unicas con informacion minima'
  3   'Pruebas unicas con ID de paciente y fechas en tiempo local de Puerto Rico'
  4   'Pruebas unicas con ID de paciente y fechas en tiempo internacional UTC'
  5   'Pruebas diarias para grafica de dashboard de Salud'
  6   'Pruebas por fecha de coleccion'
  7   'Pruebas por fecha de reporte'
  8   'Pruebas por fecha de coleccion y entidad'
  9   'Total de TDF por fecha reportada de llegada'
 10   'Total de TDF por municipio'
 11   'Casos por fecha de coleccion'
 12   'Casos por fecha de creacion en sistema'
 13   'Casos por grupo de edad'
 14   'Casos por ciudad'
 15   'Casos por region'
 16   'Resumen de Escuelas Publicas y Privadas'Nota:Si algun API esta bajo mantenimiento y/o desconectado, este paquete debería advertir sobre cual error en particular esta ocurriendo. Levante un "Issue" en este repositorio o contactenos incluyendo algun screenshot y descripción de como llego al error si encuentra algo extraño.Contribuyendo a desarrollar este programaPara contribuir al crecimiento de BioPortal APIs, sigue estos pasos:Bifurca este repositorio.Crea una rama:git checkout -b <nombre_rama>.Haga sus cambios y guardelos usando un commit:git commit -m '<commit_message>'Empujalos a la rama original:git push origin bioportal-apis/<localizacion>Crea un pull request.Alternativamente, puede rebuscar la documentación de GitHub paracrear un pull request.ContribuyentesContactoEn caso de alguna pregunta, puede contactarnos a través dejose.zavala@salud.pr.gov.LicenciaEste proyecto esta licenciado bajo los terminos de laLicencia MIT.
Package: bioprinter
Bioprinter (webpagehere) is a Python package for producing living art. It transforms an image into files that a liquid dispenser can use toprintthe image to a plate using pigmented yeast or bacteria.Here are two examples of bio-art:Dolly drawn with baker yeast (white), violacein-producing yeast (black), and carotene-producing yeast (orange).British flag drawn with 3 different strains of the bacteriumE. coli.These bio-prints (and many others!) were presented at the University of Edinburgh’s SynthSys Open days 2016 (strain selection and acoustic printing by Paulina Kanigowska).Bioprinter is released onGithubunder the MIT licence (Copyright 2015 Edinburgh Genome Foundry), everyone is welcome to contribute!Bioprinter was written at the Edinburgh Genome Foundry byZulkoafter an original idea and Matlab code by Mike Shen (project on Github).InstallationIf you have PIP installed:pip install ez_setup bioprinterOr unzip the source code in a directory and type in a terminal:python setup.py installUsageThe image file to be printed can have any resolution, but note that the width/height ratio of the plate the image is printed on is 1.5. Ensure that a specific color is used to mark the un-pigmented background of the image, here we use blue:Run the following code in Python or save indolly.py:frombioprinterimportbioprintbioprint(image_filename="/path/to/image/dolly.jpeg",output_filename="dolly.csv",bg_color=[0,0,255],# blue background represents empty wellspigments_wells={"A1":[0,0,0],# black yeast in source well A1"A2":[250,120,10],# orange yeast in well A2"A3":[255,255,255]},# white yeast in well A3quantified_image_filename="dolly_preview.jpeg")The saved script can be executed in a terminal withpython dolly.py. This will produce adolly.csvfile as well as a preview image of the final printing (so that you can check if the image looks good at this low resolution).Prepare a source plate with the right pigmented yeasts in wells A1, A2, A3, use an agar plate as the destination plate, and feeddolly.csvto theLabcyte Echo Acoustic Liquid Handler(tested with Labcyte Echo 555 Series). Once the printing is finished, incubate 2 days at 30C (it would be one day at 37C for bacteria). Enjoy the result!
Package: bioprov
BioProv - W3C-PROV provenance documents for bioinformaticsCodeTestsDocsBioProv is a Python library forW3C-PROVrepresentation of bioinformatics workflows.
It enables you to quickly write workflows and to describe relationships between samples, files, users and programs.Please see thetutorialsfor a more detailed introduction and
visitReadTheDocsfor the complete documentation.Quickstart>>>importbioprovasbp# Create samples and file objects>>>sample=bp.Sample("mysample")>>>genome=bp.File("mysample.fasta","genome")>>>sample.add_files(genome)# Create programs>>>output=sample.files["blast_out"]=bp.File("mysample.blast.tsv","blast_out")>>>blastn=bp.Program("blastn",params={"-query":sample.files["genome"],"-db":"mydb.fasta","-out":output})>>>sample.add_programs(blastn)# Run programs>>>sample.run_programs()# Save your project>>>proj=bp.Project((sample,),tag="example_project")>>>proj.to_json()# Create PROV documents>>>prov=bp.BioProvDocument(proj)# Save in PROVN or graphical format>>>prov.write_provn()# human-readable text format>>>prov.dot.write_pdf()# graphical formatBioProv also has a command-line application to run preset workflows.$ bioprov -h
usage: bioprov [-h] [--show_config | --show_db | --clear_db | -v | -l]
               {genome_annotation,blastn,kaiju} ...

BioProv command-line application. Choose a command to begin.

optional arguments:
  -h, --help            show this help message and exit
  --show_config         Show location of config file.
  --show_db             Show location of database file.
  --clear_db            Clears all records in database.
  -v, --version         Show BioProv version
  -l, --list            List Projects in the BioProv database.

workflows:
  {genome_annotation,blastn,kaiju}BioProv is built with theBiopythonandPandaslibraries.You can import data into BioProv using Pandas objects.# Read csv straight into BioProv>>>samples=bp.read_csv("my_dataframe.tsv",sep="\t",sequencefile_cols="assembly")# Alternatively, use a pandas DataFrame>>>df=pd.read_csv("my_dataframe.tsv",sep="\t")# [...] manipulate your df>>>df["assembly"]="assembly_directory/"+df["assembly"]# Now load from your df>>>project=bp.from_df(df,sequencefile_cols="assembly",source_file="my_dataframe.tsv")# `samples` becomes a Project dict-like object>>>sample1=project['sample1']# You can also export your sample and associated files and attributes as a dataframe>>>project.to_csv()Installation# Install from pip$pipinstallbioprov# Install from conda$condainstall-cconda-forge-cbiocondabioprov# Install from source$gitclonehttps://github.com/vinisalazar/bioprov&&cdbioprov;# download$condaenvcreate-fenvironment.yaml&&condaactivatebioprov;# install dependencies$pipinstall.&&pytest;# install and testImportant!BioProv requiresProdigalto be tested. Otherwise tests will fail.Contributions are welcome!BioProv is in active development and no warranties are provided (please see the License).DependenciesBioProv requires the follow dependencies to run. Also see thesetupandenvironmentfiles.biopythoncoolnamecoverallsdataclassespandasprodigalprovprovstore-apipydotpytestpytest-covtqdmtinydb
Package: biopytk
BioPyTKPersonal Toolkit for Bioinformatics and Computational Biology pipelines.
Latest release:v0.1.2In this README:FeaturesUsageInitial setupExamplesFAQFeaturesThis package contains the following modules:⚙️bio_seqDNA/RNA sequencing tools.__init__()printSeq()nucFrequencyDict()validateSeq()percentGC()transcribe()dnaCompliment()rnaCompliment()dna_reverseCompliment()rna_reverseCompliment()getBasePairs()hammingDist()seqCompare()translate()seqSummary()get_reading_frames()getAllORFProteins()globalAlign()⚙️aa_seqAmino Acid & Polypeptide analysis tools.getProteinsFromRF()⚙️fasta_tkFASTA file analysis tools.Supports:.fasta,.fafile types.More support to come in later releasespercentGC_fasta()gcContentFromFASTA()getMaxGCFromFASTA()dictFromFASTA()parseFASTA()dfFromFASTA()UsageInitial setupcd to your desired install directory and usepipto install the package.pip install biopytkORNavigate to thePyPi BioPyTK download pageand install manually.Need help installing packages?Click here.ExamplesComing soon!FAQCan I use this package for my own work?Absolutely! Please see theLicenseinformation below.Will this package work with older versions of Python?It is recommended to use>Python 3.11.1as this was the version the package was built on. If future Python releases cause dependency issues, patches will be released.Why are you making this ifBioPythonexists?This is a personal project to practice bioinformatics tooling creation, package management, and overall explore bioinformatics. This is in no way intended to take over large open-source projects, but rather act as a sandbox for personal research and development!LicenseSteve-Cheney/biopytk is licensed under  theMIT License.Copyright (c) 2023 Stephen Cheney
Package: biopytorch
BioPyTorchPyTorch implementation of the learning rule proposed in "Unsupervised learning by competing hidden units", D. Krotov, J. J. Hopfield, 2019,https://www.pnas.org/content/116/16/7723InstallationInstall withpip:pipinstallbiopytorchUsageThe package provides two layers,BioLinearandBioConv2d, that respectively mirror the features ofnn.Linearandnn.Conv2dfrom PyTorch, with the added support of training with the alternative rule proposed by Krotov & Hopfield.They share all the same parameters of their analogues (except forBioConv2d, which currently does not support the use ofbias). To execute a single update step, call the methodtraining_step.See the example notebook innotebooksfor more details.Other filesdev/bioconv2d_dev.pycontains an alternative implementation ofBioConv2dusingF.unfold. The performance is significantly worse (especially for memory), so it should not be used in practice. However, the algorithm is easier to follow, and can be used to get a better understanding of the Krotov learning rule.slidescontains a few explanatory slidesnotebooks: examplesBenchmarkHyperparameters ($p$, $k$, $\Delta$ - for their meaning, see theslides, or the docstrings) are optimized with respect to the validation accuracy of classification on the CIFAR-10 dataset, using theOptunalibrary.Specifically, the architecture (taken from [2]) is as follows:The(Classifier)segment is inserted in different positions - after(1),(2), ... - such that the change in performance given by deeper layers may be measured.Depending on the number of hebbian layers preceding the(Classifier), the performance obtained with the best hyperparameters found is as follows:#layers12345Accuracy (val)69.2067.1364.9159.8346.25Accuracy (test)67.0665.2263.0858.8645.45$p$28888$k$93572$\Delta$.08.34.25.235.335Dropout.2.25.05.1.1Params195k302k387k804k1.475M$p$, $k$ and $\Delta$ are the same for all theBioConv2dlayers.
When the full architecture is trained, different hyperparameters are used for the BioLinear layer. However, for the best run, they are exactly equal to the ones already used for the previousBioConv2d, which are reported in the table.Note that performance is slightly better than the results obtained in [2], here reported for reference:#layers12345Accuracy (this)67.0665.2263.0858.8645.45Accuracy ([2])63.9263.8158.2852.9941.78A full report on the hyperparameter optimization is available onwandb.Sources[1] Krotov, Hopfield, "Unsupervised learning by competing hidden units", 2019[2] Amato et al., "Hebbian Learning Meets Deep Convolutional Neural Networks", 2019