Saravanan666/pypi-org huggingface dataset:
Package: seqsum
SeqsumRobust individual and aggregate checksums for nucleotide sequences. Accepts data from either standard input orfast[a|q][.gz|.zst|.xz|.bz2]files. Generatesindividualchecksums for each sequence, and anaggregatechecksum-of-checksums for a collection of sequences. Warnings are shown for duplicate sequences and within-collection checksum collisions at the chosen bit depth. Sequences are uppercased prior to hashing withxxHash(xxh3_128) and may optionally be normalised (with-n) to use only the charactersACGTN-. Read IDs and base quality scores do not inform the checksum. Outputs TSV or JSON to stdout.A typical use case is for determining whether reordered, renamed or otherwise bit-inexact fasta/fastq files have equivalent sequence composition. Another use is generating the shortest possible collision-free identifiers for sequence collections.Install (Python 3.10+)# Latest releasepipinstallseqsum# Latest commitgitclonehttps://github.com/bede/seqsum
pipinstall./seqsum# Developmentgitclonehttps://github.com/bede/seqsum.gitcdseqsum
pipinstall--editable'.[dev]'pytestCLI usage# Fasta with one record$seqsumntMN908947.fasta
MN908947.3ca5e95436b957f93# Fasta with two records$seqsumntMN908947-BA_2_86_1.fasta
MN908947.3ca5e95436b957f93
BA.2.86.1d5f014ee6745cb77
aggregate837cfd6836b9a406# Fastq (gzipped) with 1m records, redirected to file, with progress bar$seqsumntillumina.r12.fastq.gz--progress>checksums.tsv
Processed1000000records(317kit/s)INFO:Foundduplicatesequences# Fasta via stdin%catMN908947.fasta|seqsumnt-
MN908947.3ca5e95436b957f93$seqsumnt-husage:seqsumnt[-h][-n][-s][-bBITS][-i][-a][-j][-p]input

Robustindividualandaggregatechecksumsfornucleotidesequences.Acceptsinput
fromeitherstdinorfast[a|q][.gz|.zst|.xz|.bz2]files.Generatesindividual
checksumsforeachsequence,andanaggregatechecksum-of-checksumsfora
collectionofsequences.Warningsareshownforduplicatesequencesand
within-collectionchecksumcollisionsatthechosenbitdepth.Sequencesare
uppercasedpriortohashingwithxxHashandmayoptionallybenormalisedtouseonly
thecharactersACGTN-.ReadIDsandbasequalityscoresdonotinformthechecksum

positionalarguments:inputpathtofasta/qfile(or-forstdin)options:-h,--helpshowthishelpmessageandexit-n,--normalisereplaceUwithTandcharactersotherthanACGT-withN(default:False)-s,--strictraiseerrorforcharactersotherthanABCDGHKMNRSTVWY-(default:False)-bBITS,--bitsBITSdisplayedchecksumlength(default:64)-i,--individualoutputonlyindividualchecksums(default:False)-a,--aggregateoutputonlyaggregatechecksum(default:False)-j,--jsonoutputJSON(default:False)-p,--progressshowprogressandspeed(default:False)Python usagefromseqsumimportlibfasta_contents=">read1\nACGT"checksums,aggregate_checksum=lib.sum_nt(fasta_contents)print(checksums)frompathlibimportPathfromseqsumimportlibchecksums,aggregate_checksum=lib.sum_nt(Path("tests/data/MN908947-BA_2_86_1.fasta"))print(checksums,aggregate_checksum)
Package: seqtag
https://pypi.org/project/seqtag/BiLSTM + CRF for sequence taggingThis is adapted from guillaumegenthial 's original implementation and is made configurable and easy to adapt and use.Requirements:This code is tested with all tensorflow versions from 1.3.0 to 1.10.0.tensorflow is not included in setup.py since, it will remove tensorflow-gpu.
Separately install tensorflow by followinghttps://www.tensorflow.org/install/for this module to work.Download the 300 dimnesional glove vectors fromhttps://nlp.stanford.edu/projects/glove/Installation:The stable version can be installed by running "pip install seqtag"How to Use:Create a training directory with train.txt and valid.txt (test.txt is optional)
set the config parameters as expected in a configuration file.An example of the configuration file can be found athttps://github.com/bedapudi6788/seqtag/blob/master/example_config.jsonTraining:from seqtag import trainertrainer.train(config_path = 'path_to_config_json')Running Predictions:from seqtag import predictormodel = predictor.load_model(path_to_config.json)predictor.predict(model, ['I', 'am', 'Batman'])['O', 'O', 'B-PER']For an usage example take a look athttps://github.com/bedapudi6788/Deep-Segmentation/. seqtag is used for sentence segmentation in this repo.
Package: seqtag-keras
This repo is a fork ofhttps://github.com/Hironsan/anagoUpdated with support for tf 2.0 and newer keras versions.This repo is a fork ofhttps://github.com/Hironsan/anagoand is modified to suit my needs. The module's name is changed so that it doesn't conflict with the installation of the original repo and so that I can push my changes to PyPi.Unless you know why are you using this, don't use this module. Use the excellenthttps://github.com/Hironsan/anagoThe purpose of creating a fork (and a separate module) instead of using the excellent anago is as follows.Maintain compatibility with newer tf and keras versions.Anago's installation comes with ELMo, allennlp and some other stuff, which are not needed for the intended use cases of this repo (mainly DeepSegment).It's not easy to include a git repo link as a requirement in setup.py while publishing to PyPi. So, this module is renamed to seqtag-keras.**Full credit and thanks for the original implementation of this repo goes tohttps://github.com/Hironsan/andhttps://github.com/chakki-works/. **
Package: seqtolang
seqtolangseqtolangis a python library for multi-langauge documents identification.Seethispost for implementation details.Getting StartedInstall from source:$ git clone https://github.com/hiredscorelabs/seqtolang
$ cd seqtolang
$ python setup.py installor using PyPi:$ pip install seqtolangBasic usage:fromseqtolangimportDetectordetector=Detector()text="In Chinese, the French phrase 'Je rentre chez moi Je rentre chez moi' will be '我正在回家'"languages=detector.detect(text)print(languages)>>>[('fr',0.499),('en',0.437),('zh',0.062)]tokens=detector.detect(text,aggregated=False)print(tokens)>>>['eng','eng','eng','eng','eng','fra','fra','fra','fra','fra','fra','fra','fra','eng','eng','zho']seqtolangsupport 36 languages:['afr', 'eus', 'bel', 'ben', 'bul', 'cat', 'zho', 'ces', 'dan', 'nld', 'eng', 'est', 'fin', 'fra', 
'glg', 'deu', 'ell', 'hin', 'hun', 'isl', 'ind', 'gle', 'ita', 'jpn', 'kor', 'lat', 'lit', 'pol', 
'por', 'ron', 'rus', 'slk', 'spa', 'swe', 'ukr', 'vie']SupportGetting HelpYou can ask questions and join the development discussion onGithub IssuesLicenseApache License 2.0
Package: seq-tools
# py-seq-toolsFree python libraries for working with biological sequences, alignments, and formats. These libraries are part of Au-public scripts, I haven't documented them well yet.  The goal here is to put them into a package and get them better documented.Longer term, these were written in python 2, but it would be nice to either support python3 full compatability or to completely migrate them over to python3.The command line utilities of this package serve a dual purpose. 1) is to provide useful utilities you can run through the commandline, and 2) is to provide examples on how to make use the of the libraries included in this package.Access to the command line utilities can be found with`$ seq-tools <cli command>`Where cli command is the name of a cli module.To use the libraries in this distribution please consult the [API documentation](https://github.com/jason-weirather/py-seq-tools/blob/master/manual.pdf)You can install seq-tools with pip>From within the source directory you can try`$ pip install .`This should make the `seq-tools` command available as well as the `seqtools` library for import.This library includes the following modules:* seqtools* align* errors* format* bamindex* bed* bgzf* fasta* fastq* pacbio* psl* sam* graph* range* sequence* simulation* emitter* permute* randomsource* statistics* stream* structureAgain, details are available in the [API documentation](https://github.com/jason-weirather/py-seq-tools/blob/master/manual.pdf).
Package: sequana-bioconvert
This is is thebioconvertpipeline from theSequanaprojectOverview:convert NGS format from one to another using BioconvertInput:whatever input format accepted by BioconvertOutput:whatever output format accepted by BioconvertStatus:productionCitation:Cokelaer et al, (2017), ‘Sequana’: a Set of Snakemake NGS pipelines, Journal of Open Source Software, 2(16), 352, JOSS DOI doi:10.21105/joss.00352InstallationThis package depends on Python only and singularity. To installsequana_bioconvert, just install this package as
follows:pip install sequana_bioconvertForsingularity, we recommend to use a conda environment:conda create --name bioconvert -y python=3.8 singularity
conda activate bioconvert
pip install sequana_bioconvertUsagesequana_bioconvert --helpYou need to provide the type of conversion you wish to perform with the–commandargument. You also need to tell the type of extensions expected
including the compression (gz, bz2 or dsrc recognised). Finally, the–input-directoryand–input-patternmust be used to find the input
files.:sequana_bioconvert --input-directory . --input-ext fastq.gz --output-ext
    fasta.gz --command fastq2fasta --input-pattern "*.fastq.gz"This command creates a directory with the pipeline and configuration file. You will then need
to execute the pipeline as follows:cd bioconvert
sh bioconvert.sh  # for a local runThis launches a snakemake pipeline. Symbolic links to the input data are created in
the ./input directory and results stored in the ./output directory.Some conversions require external standalones. We recommend to use our singularity image.
To do so, add–use-apptaineroptions when you initialse the pipeline. You can also
specify where to store the singularity image once for all using–apptainer-prefix:sequana_bioconvert --input-directory . --input-ext fastq.gz --output-ext
    fasta.gz --command fastq2fasta --input-pattern "*.fastq.gz"
    --use-apptainer --apptainer-prefix ~/imagesSee bioconvert.readthedocs.io for more details aboutbioconvertitself.If you are familiar with snakemake, you can retrieve the pipeline itself and its
configuration files and then execute the pipeline yourself with specific parameters:snakemake -s bioconvert.rules -c config.yaml --cores 4 --stats stats.txtOr usesequanixinterface.RequirementsThis pipelines requires the following executable(s) installed with sequana_bioconvert: bioconvertAll dependencies and external dependencies related to bioconvert are available through the apptainer used by thissequana_bioconvertpipeline.Rules and configuration detailsHere is thelatest documented configuration fileto be used with the pipeline. Each rule used in the pipeline may have a section in the configuration file.ChangelogVersionDescription1.0.0Uses bioconvert 1.0.00.10.0Add container0.9.0Version using new sequana/sequana_pipetools framework0.8.1Working version0.8.0First release.Contribute & Code of ConductTo contribute to this project, please take a look at theContributing Guidelinesfirst. Please note that this project is released with aCode of Conduct. By contributing to this project, you agree to abide by its terms.
Package: sequana-depletion
This is is thedepletionpipeline from theSequanaprojectOverview:select or deplete reads from input FastQ files given a referenceInput:Fastq FilesOutput:Fastq FilesStatus:productionCitation:Cokelaer et al, (2017), ‘Sequana’: a Set of Snakemake NGS pipelines, Journal of Open Source Software, 2(16), 352, JOSS DOI doi:10.21105/joss.00352InstallationInstall this package as follows:pip install sequana_depletionit requireshttps://sequana.readthedocs.ioandhttps://bioconvert.readthedocs.iofor the bam to fastq conversionUsagesequana_depletion --help
sequana_depletion --input-directory DATAPATH --reference hg38.fa --mode depletion
sequana_depletion --input-directory DATAPATH --reference covid.fa --mode selectionThis creates a directory with the pipeline and configuration file. You will then need
to execute the pipeline:cd depletion
sh depletion.sh  # for a local runThis launch a snakemake pipeline. If you are familiar with snakemake, you can
retrieve the pipeline itself and its configuration files and then execute the pipeline yourself with specific parameters:snakemake -s depletion.rules -c config.yaml --cores 4 --stats stats.txtOr usesequanixinterface.RequirementsThis pipelines requires the following executable(s):bwasamtoolsbamtools#.. image::https://raw.githubusercontent.com/sequana/depletion/master/sequana_pipelines/depletion/dag.pngDetailsThis pipeline runsdepletionin parallel on the input fastq files (paired or not).
A brief sequana summary report is also produced.Rules and configuration detailsHere is thelatest documented configuration fileto be used with the pipeline. Each rule used in the pipeline may have a section in the configuration file.ChangelogVersionDescription0.3.0use click and new sequana_pipetools. convert to pyproject0.2.0handle paired/unpaired datarefactorise to use containers/apptainers0.1.0First release.
Package: sequana-downsampling
This is is thedownsamplingpipeline from theSequanaprojectOverview:downsample NGS data setsInput:a set of FastQ or FASTA filesOutput:a set of downsampled filesStatus:productionCitation(sequana):Cokelaer et al, (2017), ‘Sequana’: a Set of Snakemake NGS pipelines, Journal of Open Source Software, 2(16), 352, JOSS DOI doi:10.21105/joss.00352Citation(pipeline):InstallationYou must install Sequana first:pip install sequanaThen, just install this package:pip install sequana_downsamplingUsagesequana_downsampling --help
sequana_downsampling --input-directory DATAPATHH
sequana_downsampling --downsampling-method random --downsampling-max-entries 100
sequana_downsampling --downsampling-method random_pct --downsampling-percent 10 --downsampling-input-format fasta --input-pattern "whatever*fasta"Note that the current implementation handles fastq files (zipped or not) and
fasta files (uncompressed only)This creates a directory with the pipeline and configuration file. You will then need
to execute the pipeline:cd downsampling
sh downsampling.sh  # for a local runThis launch a snakemake pipeline. If you are familiar with snakemake, you can
retrieve the pipeline itself and its configuration files and then execute the pipeline yourself with specific parameters:snakemake -s downsampling.rules -c config.yaml --cores 4 --stats stats.txtOr usesequanixinterface.Examples of a set of FastQ zipped files in the current directory:sequana_downsampling –run –downsampling-method random_pct
cd downsampling
make cleanThis will create a directory calleddownsampling, and randomly select 10% of
the input reads for each file with extension .fastq.gz in the current directory.
Since-runis used, the pipeline is executed automatically. The following
commands will enter into the directory and called a Makefile. This will clean
the directory for temporary files.RequirementsThis pipelines requires the following executable(s):sequanapigzDetailsThis pipeline runsdownsamplingin parallel on the input fastq or fasta files (paired or not). If paired, the one-to-one mapping is conserved.It can take as input a set of FastQ files, or FastA files. by
default, the pipeline with randomly select 1000 entries from each input files.
You can increase this number using –downsampling-max-entries option. If you
prefer to select a percentage of the entries instead, you can change the
downsamping method as follows:--downsampling-method random_pctand change the value if needed (default is 10%):--downsampling-percent 20Note that input FastQ can be gzipped. Output files are gzipped. FastA input
files must be compressed for nowRules and configuration detailsHere is thelatest documented configuration fileto be used with the pipeline. Each rule used in the pipeline may have a section in the configuration file.ChangelogVersionDescription0.8.5cope with R1/R2 paired data properly. Improved make file0.8.4add missing MANIFEST to include missing requirements.txt0.8.3comply with new API from sequana_pipetools 0.2.40.8.2add a –run option to execute the pipeline directly0.8.1fix input and N in the random selection0.8.0First release.
Package: sequana-laa
This is is thelaapipeline from theSequanaprojectOverview:Perform amplicon analysis on Pacbio data sets including variant and phylogenyInput:A set of CCS files from pacbio in FastQ formatsOutput:variant calling, phylogney, consensus genomes, etcStatus:production but may changeCitation:Cokelaer et al, (2017), ‘Sequana’: a Set of Snakemake NGS pipelines, Journal of Open Source Software, 2(16), 352, JOSS DOI doi:10.21105/joss.00352This pipeline was used in :L’Honneur et al (polyomavirus, 2022)https://pubmed.ncbi.nlm.nih.gov/34979561/Kali et al (rabies,2021),https://pubmed.ncbi.nlm.nih.gov/33444703/Claireaux et al. (gene involved in HIV, 2022) accepted, not yet on pubmedInstallationYou must install Sequana first:pip install sequanaThen, just install this package:pip install sequana_laaUsagesequana_laa --help
sequana_laa --input-directory DATAPATHThis creates a directory with the pipeline and configuration file. You will then need
to execute the pipeline:cd laa
sh laa.sh  # for a local runThis launch a snakemake pipeline. If you are familiar with snakemake, you can
retrieve the pipeline itself and its configuration files and then execute the pipeline yourself with specific parameters:snakemake -s laa.rules -c config.yaml --cores 4 --stats stats.txt --wrapper-prefix git+file:///home/cokelaer/Work/github/forked/sequana-wrappersOr usesequanixinterface.RequirementsThis pipelines requires the following executable(s):vtfreebayesigvtoolssequanasnpeff (optional)samtoolsbamtoolsminimap2DetailsThis pipeline runs amplicon analysis on long reads data from pacbio sequencers.Rules and configuration detailsHere is thelatest documented configuration fileto be used with the pipeline. Each rule used in the pipeline may have a section in the configuration file.ChangelogVersionDescription0.10.0full integration with latest wrapeprs and apptainers from damona0.9.0add singularity containers0.8.0First release.
Package: sequana-merge-flowcells
This is is themerge_flowcellspipeline from theSequanaprojectOverview:Merge gzipped FastQ files from several flowcellsInput:set of identically named FastQ files from several directoriesOutput:merged FastQ files stored in an output directory.Status:matureCitation:Cokelaer et al, (2017), ‘Sequana’: a Set of Snakemake NGS pipelines, Journal of Open Source Software, 2(16), 352, JOSS DOI doi:10.21105/joss.00352InstallationYou must install Sequana first:pip install sequanaThen, just install this package:pip install sequana_merge_flowcellsUsagesequana_pipelines_merge_flowcells --help
sequana_pipelines_merge_flowcells --input-directory DATAPATHThis creates a directory with the pipeline and configuration file. You will then need
to execute the pipeline:cd merge_flowcells
sh merge_flowcells.sh  # for a local runThis launch a snakemake pipeline. If you are familiar with snakemake, you can
retrieve the pipeline itself and its configuration files and then execute the pipeline yourself with specific parameters:snakemake -s merge_flowcells.rules -c config.yaml --cores 4 --stats stats.txtOr usesequanixinterface.RequirementsThis pipelines requires the following executable(s):sequanapigzzcatDetailsThis pipeline runsmerge_flowcellsin parallel on the input fastq files (paired or not).
You have to provide at least two subdirectories. You may provide more.
The input FastQ files must be zipped in the current version. The input FastQ
files found in the first directory muts be found in all subsequent directories.Rules and configuration detailsHere is thelatest documented configuration fileto be used with the pipeline. Each rule used in the pipeline may have a section in the configuration file.ChangelogVersionDescription0.0.1First release.
Package: sequana-pacbio-qc
This is is thepacbio_qcpipeline from theSequanaprojetOverview:Quality control for pacbio datafiles (raw data or CCS files).Input:BAM files provided by Pacbio Sequencers.Output:HTML reports with various plots including taxonomic plotStatus:productionDocumentation:This README file, the Wiki from the github repository (link above) andhttps://sequana.readthedocs.ioCitation:Cokelaer et al, (2017), ‘Sequana’: a Set of Snakemake NGS pipelines, Journal of Open Source Software, 2(16), 352, JOSS DOI doi:10.21105/joss.00352InstallationJust install this package:pip install sequana_pacbio_qcYou will needsamtoolsandkraken2(optional) for the taxonomic analysis.Usagesequana_pacbio_qc --help
sequana_pacbio_qc --input-directory DATAPATHIf you want to filter out some BAM files, you may use the pattern in tab ‘input data’.In the configuration tab, in the kraken section add as many databases
as you wish. You may simply unset the first database to skip the taxonomy, which
is experimental.This creates a directory with the pipeline and configuration file. You will then need
to execute the pipeline:cd pacbio_qc
sh pacbio_qc.sh  # for a local runThis launch a snakemake pipeline. If you are familiar with snakemake, you can
retrieve the pipeline itself and its configuration files and then execute the pipeline yourself with specific parameters:snakemake -s pacbio_qc.rules -c config.yaml --cores 4 --stats stats.txtOr usesequanixinterface.RequirementsThis pipelines requires the following executable(s):sequanasamtoolskraken2multiqcDetailsThis pipeline takes as inputs a set of BAM files from Pacbio sequencers. It
computes a set of basic statistics related to the read lengths. It also shows
some histograms related to the GC content, SNR of the diodes and the number of passes
Finally, a quick taxonomy can be performed using Kraken. HTML reports
are created for each sample as well as a multiqc summary page.Kraken databases are not provided with the pipeline. This step is optional and
not used by default.ChangelogVersionDescription1.0.1fix missing import in the summary1.0.0Uses latest wrappers and graphviz apptainers0.11.0Release to use latests sequana_pipetools framework0.10.0Update to use latest tools from sequana framework0.9.0First release of sequana_pacbio_qc using latest sequana rules and
modules (0.9.5)Contribute & Code of ConductTo contribute to this project, please take a look at theContributing Guidelinesfirst. Please note that this project is released with aCode of Conduct. By contributing to this project, you agree to abide by its terms.Rules and configuration detailsHere is thelatest documented configuration fileto be used with the pipeline. Each rule used in the pipeline may have a section in the configuration file.