Saravanan666/pypi-org huggingface dataset:
Package: vcf2pandas
vcf2pandasvcf2pandasis a python package to convert vcf files topandasdataframes.Installpipinstallvcf2pandasDependenciespandas (2.1.0)pysam (0.21.0)UsageSelecting all columnsfromvcf2pandasimportvcf2pandasimportpandasdf_all=vcf2pandas("path_to_vcf.vcf")Selecting custom custom columns and samplesinfo_fields=["info_field_1","info_field_2"]sample_list=["sample_name_1","sample_name_2"]format_fields=["format_name_1","format_name_2"]df_selected=vcf2pandas("path_to_vcf.vcf",info_fields=info_fields,sample_list=sample_list,format_fields=format_fields,)Custom column orderingvcf2pandascan select custom/specific:INFO fieldssamplesFORMAT fieldsAnd order the selected columns based on the input list.E.g. The following list:info_fields=["DP","MQM","QA"]Gets the columns (in that order)INFO:DP    INFO:MQM    INFO:QANote that thisonly applies for INFO and FORMAT columns. That is, the samples will be ordered based on the VCF and not the input list.OutputINFO and FORMAT headingsINFO:INFO_FIELD                     e.g. INFO:DP
FORMAT:SAMPLE_NAME:FORMAT_FIELD     e.g. FORMAT:HG002:GTINFO fields not present for some variantsWhen certain INFO fields are not present for certain variants,vcf2pandasinserts a.instead in that cell. E.g. forvcf3_all.txtyou can seeINFO:GENEcolumn has.for the first 7 variants.ExamplesExample vcf and output files (dataframes as a .txt file) are available inexamples/Example Usagedf1=vcf2pandas("examples/vcf1.vcf")df2=vcf2pandas("examples/vcf2.vcf")df3_all=vcf2pandas("examples/vcf3.vcf")info=["DP"]samples=["HG002"]format_fields=["GT","AO"]df3_selected=vcf2pandas("examples/vcf3.vcf")To print to a text file:withopen("path_to_txt_file.txt")asf:f.write(df.to_string())
Package: vcf-annotate-polyphen
# vcf-annotate-polyphenA tool to annotate human VCF files with PolyPhen-2 effect measures.This tool only works on human variants,collects ClinVar scores,and assumes the VCF follows `hg19/GRCh37` conventions.## Install### via PyPi```$ pip install vcf-annotate-polyphen```### via Source Code```$ git checkout https://github.com/hammerlab/vcf-annotate-polyphen.git$ cd vcf-annotate-polyphen/$ python setup.py```## Usage### As a library```pythonimport vap  # Vcf-Annotate-Polyphen (VAP)import sqlalchemyfrom sqlalchemy import create_engineengine = creative_engine('sqlite:///polyphen-2.2.2-whess-2011_12.sqlite')conn = engine.connect()annotation = vap.annotate_variant(conn, 'chr14', 20344588, 'C', 'A')print ("Gene: {}; Protein: {}; Change: {}; ""HVar Prediction: {} (p: {}); HDiv Prediction: {} (p: {})") \.format(annotation.gene,annotation.protein,annotation.aa_change,annotation.hvar_pred,annotation.hvar_prob,annotation.hdiv_pred,annotation.hdiv_prob)# Gene: OR4K2; Protein: Q8NGD2; Change: H54Q;#  HVar Prediction: benign (p: 0.017); HDiv Prediction: benign (p: 0.008)```### Command line interfaceAfter installing the package, you can invoke the command line utility as follows:```$ vcf-annotate-polyphen --helpUsage: vcf-annotate-polyphen polyphen.whess.sqlite input.vcf output.vcfOptions:-h, --help  show this help message and exit```As listed above in the help text, this tool expects three arguments from the user:1. [PolyPhen-2 WHESS](ftp://genetics.bwh.harvard.edu/pph2/whess) in SQLite format2. Input VCF to be annotated3. Output VCF to be written with annotationsThe output file should have an additional `INFO` field as described below:```##INFO=<ID=PP2,Number=1,Type=String,Description="PolyPhen2 annotations in the following order:Gene name; UniProt id; Amino acid change; ClinVar effect category; Strength of effect (probability)">```which manifests itself for each variant description:```...2	165351172	.	T	A	.	PASS	SOMATIC;VT=SNP;PP2=.,.,.,.,.,.,.	GT:AD:BQ:DP:FA:SS	0:11,0:.:11:0.0:0	0/1:3,5:30.0:8:0.625:22	179247908	.	C	G	.	PASS	SOMATIC;VT=SNP;PP2=OSBPL6,Q9BZF3,N593K,probably damaging,0.998,probably damaging,1.0	GT:AD:BQ:DP:FA:SS	0:27,2:.:29:0.069:0	0/1:27,4:28.0:31:0.129:2...```Here is an annotated VCF: [example/TCGA-55-6543.annotated.vcf](example/TCGA-55-6543.annotated.vcf).### Example usage```$ cd example/# The following file is ~7 GB!!!$ wget "ftp://genetics.bwh.harvard.edu/pph2/whess/polyphen-2.2.2-whess-2011_12.sqlite.bz2"$ bunzip2 polyphen-2.2.2-whess-2011_12.sqlite.bz2$ vcf-annotate-polyphen ./polyphen-2.2.2-whess-2011_12.sqlite ./TCGA-55-6543.vcf ./TCGA-55-6543.annotated.vcf$ less ./TCGA-55-6543.annotated.vcf```
Package: vcf-batcher
📑️ VCF BatcherThis is a Rust crate to cut VCF (variant call files) into smaller batches, intended to be used for multiprocessing or distributed computing.🧰️ InstallationDepending on what your goals are, you can use this tool as aCLIor as a library in
🦀️ Rust or 🐍️ Python.Installing the CLIIn order to install the program as a CLI, you will need to havecargoinstalled.Instructions to install cargoOnce you have it, you can run the following command in your terminal to install the VCF batcher.cargo install vcf_batcherInstalling Rust CrateIn order to install the tool as a rust crate, you can add it to yourCargo.tomldependencies or
run:cargo add vcf_batcherYou can find the crate documentation ondocs.rs.Installing python bindingsWe provide python bindings for the VCF batcher which can be installed viapip.pip install vcf-batcher🪄️ UsageCLIUsing the CLI after installing can be done through thevcf_batcher_clicommand.vcf_batcher_cli path/to/your_file.vcf path/to/ouput/directoryBy default, this will create batches with 25'000 samples each. If you'd like to override this
default, you can do so by providing a custom--batch-sizeor-bargument:vcf_batcher_cli -b 1000 path/to/your_file.vcf path/to/ouput/directoryLibraryAfter installing either the rust crate or python module, you can use the provided function.🦀️ Rustpubfnextract_variants_to_batches(file_path:&str,batch_size:usize,output_path:&Path,compression_level:Option<Compression>)🐍️ Pythonvcf_batcher.py_extract_variants_to_batches(input_file,batches_folder,batch_size,)LicenseThe software is licensed under theMIT License.
Package: vcf-creator
vcf-creatorGenerate vCard file from CSVSetupCloning the repository:# Clone the repogitclonehttps://github.com/animesh-chouhan/vcf-creator.gitcdvcf-creator# Run the sample csv filepython3-mvcf_creatorsample.csvRunning tests# If in vc-creator foldercdvcf_creator/tests# Run the testpython3test_vcf.pyInstallation:To install it right away, type:pip3installvcf_creatorHelp:python3-mvcf_creator--helpORvcf_creator--helpRunning the script:python3-mvcf_creator<csv-file-name>ORvcf_creator<csv-file-name>Import the module in your project:fromvcf_creatorimportvcard_formatter,vcard_generatorvcard_formatter(arguments)vcard_generator(arguments)CSV File InstructionsThe contact CSV file can have the following headers all in smallcase:namephoneorganisationemailaddressbirthday (mm/dd/yyyy)The headers can be in any orderMake sure that no fields are emptyUsage exampleFor more examples and usage, please refer to theWiki.ContributingFork the repo (https://github.com/animesh-chouhan/vcf-creator/)Create your feature branch (git checkout -b feature/fooBar)Commit your changes (git commit -am 'Add some fooBar')Push to the branch (git push origin feature/fooBar)Create a new Pull RequestLicenseMIT License
copyright (c) 2021Animesh Singh Chouhan. Please have a look at thelicensefor more details.
Package: vcfempy
A Python package implementing the Voronoi Cell Finite Element Method (VCFEM)
for analysis of problems in geomechanics.For information on how to install the package and to read the documentation,
visit theRTDpage. For information about the project, to view source code,
and to report issues visit the project repo page onGitHub. For more
information and to discuss collaboration contactBrandon Karchewski.
Package: vcferr
vcferrThevcferrmodule is a lightweight error simulation framework. The tool operates on an input VCF and can probabilistically simulate the following error models for biallelic SNPs:rarr = Heterozygous drop out: (0,1) or (1,0) to (0,0)aara = Homozygous alt drop out: (1,1) to (0,1)rrra = Heterozygous drop in: (0,0) to (0,1)raaa = Homozygous alt drop in: (0,1) or (1,0) to (1,1)aarr = Double homozygous alt drop out: (1,1) to (0,0)rraa = Double homozygous alt drop in: (0,0) to (1,1)In addition to error models, the tool includes functionality to inject probability of missingness:ramm = Heterozygous to missing: (0,1) or (1,0) to (.,.)rrmm = Homozygous ref to missing: (0,0) to (.,.)aamm = Homozygous alt to missing: (1,1) to (.,.)InstallationThevcferrtool is delivered as a Python module.To install from PyPi:pip install vcferrAlternatively, clone thevcferrGitHub repository and usepipfrom the root of the directory:pip install .Note that the following dependencies are used byvcferr:Python >=3.6.xpysamrandomclickUsageThe examples below demonstrate basic usage with theexample.vcf.gzin thedata/directory of thevcferrGitHub repository.The following is a basic example that simulates 20% heterozygous dropout:vcferr data/example.vcf.gz --sample='sample1' --p_rarr=0.2By default,vcferrwill stream output of the VCF with errors simulated. However, if an argument is given for"output_vcf"then the VCF will be written to disk:vcferr data/example.vcf.gz --sample='sample1' --output_vcf=data/sample1sim.example.vcf.gz --p_rarr=0.2Note that multiple kinds of error can be simulated simulatenously:vcferr data/example.vcf.gz --sample='sample1' --p_rarr=0.2 --p_raaa=0.1 --p_rrra=0.2 --p_aara=0The tool can also simulate missingness:vcferr data/example.vcf.gz --sample='sample1' --p_rarr=0.2 --p_raaa=0.1 --p_rrra=0.2 --p_aara=0 --p_rrmm=0.5
Package: vcfparser
Python parser for parsing the genomics and transcriptomics VCF data.Free software: MIT licenseDocumentation:https://vcfparser.readthedocs.io.FeaturesNo external dependency except pythonMinimalistic in natureWith lots of control to api usersInstallationTo install vcfparser, run this command in your terminal:$pipinstallvcfparserIn order to build from source, you can follow :advanced tutorial <advanced-install>Usage>>> from vcfparser import VcfParser
>>> vcf_obj = VcfParser('input_test.vcf')Get meta information about the vcf file>>> metainfo = vcf_obj.parse_metadata()
>>> metainfo.fileformat
'VCFv4.2'
>>> metainfo.filters_
[{'ID': 'LowQual', 'Description': 'Low quality'}, {'ID': 'my_indel_filter', 'Description': 'QD < 2.0 || FS > 200.0 || ReadPosRankSum < -20.0'}, {'ID': 'my_snp_filter', 'Description': 'QD < 2.0 || FS > 60.0 || MQ < 40.0 || MQRankSum < -12.5 || ReadPosRankSum < -8.0'}]>>> metainfo.alt_
[{'ID': 'NON_REF', 'Description': 'Represents any possible alternative allele at this location'}]
>>> metainfo.sample_names
['ms01e', 'ms02g', 'ms03g', 'ms04h', 'MA611', 'MA605', 'MA622']
>>> metainfo.record_keys
['CHROM', 'POS', 'ID', 'REF', 'ALT', 'QUAL', 'FILTER', 'INFO', 'FORMAT', 'ms01e', 'ms02g', 'ms03g', 'ms04h', 'MA611', 'MA605', 'MA622']Get Records from vcf file>>> records = vcf_obj.parse_records()
Here records is an generator.
>>> first_record = next(records)
>>> first_record.CHROM
'2'
>>> first_record.POS
'15881018'
>>> first_record.REF
'G'
>>> first_record.ALT
'A,C'
>>> first_record.QUAL
'5082.45'
>>> first_record.FILTER
['PASS']
>>> first_record.get_mapped_samples()
{'ms01e': {'GT': './.', 'PI': '.', 'GQ': '.', 'PG': './.', 'PM': '.', 'PW': './.', 'AD': '0,0', 'PL': '0,0,0,.,.,.', 'DP': '0', 'PB': '.', 'PC': '.'}, 'ms02g': {'GT': './.', 'PI': '.', 'GQ': '.', 'PG': './.', 'PM': '.', 'PW': './.', 'AD': '0,0', 'PL': '0,0,0,.,.,.', 'DP': '0', 'PB': '.', 'PC': '.'}, 'ms03g': {'GT': './.', 'PI': '.', 'GQ': '.', 'PG': './.', 'PM': '.', 'PW': './.', 'AD': '0,0', 'PL': '0,0,0,.,.,.', 'DP': '0', 'PB': '.', 'PC': '.'}, 'ms04h': {'GT': '1/1', 'PI': '.', 'GQ': '6', 'PG': '1/1', 'PM': '.', 'PW': '1/1', 'AD': '0,2', 'PL': '49,6,0,.,.,.', 'DP': '2', 'PB': '.', 'PC': '.'}, 'MA611': {'GT': '0/0', 'PI': '.', 'GQ': '78', 'PG': '0/0', 'PM': '.', 'PW': '0/0', 'AD': '29,0,0', 'PL': '0,78,1170,78,1170,1170', 'DP': '29', 'PB': '.', 'PC': '.'}, 'MA605': {'GT': '0/0', 'PI': '.', 'GQ': '9', 'PG': '0/0', 'PM': '.', 'PW': '0/0', 'AD': '3,0,0', 'PL': '0,9,112,9,112,112', 'DP': '3', 'PB': '.', 'PC': '.'}, 'MA622': {'GT': '0/0', 'PI': '.', 'GQ': '99', 'PG': '0/0', 'PM': '.', 'PW': '0/0', 'AD': '40,0,0', 'PL': '0,105,1575,105,1575,1575', 'DP': '40', 'PB': '.', 'PC': '.\n'}}Similarly, we can loop over rest of the records by following for loop:forrecordinrecords:chrom=record.CHROMpos=record.POSid=record.IDref=record.REFalt=record.ALTqual=record.QUALfilter=record.FILTERformat_=record.format_infos=record.get_info_dict()mapped_sample=record.get_mapped_samples()
Package: vcfproj
VCF projection from chromosome to transcriptomeThe program uses ncls, a fast interval tree search program
that considerably speeds up the ''stabbing'' problem of
finding out the variant that overlaps the transcriptome interval.GTF_FILE = 'Mus_musculus.GRCm38.91.gtf'
VCF_FILE = 'mgp_v4_indels_NOD_PWK.vcf'


from vcfproj import vcfproj
vcf_gtf = vcfproj.projection(GTF_FILE, VCF_FILE)vcf_gtfis a dataframe, that would contain the following columns[

'chrom_x', - chromosome of variation 
'start', - start of transcript
'end', - end of transcript
'gene', - gene name
'txome', - transcript name
'vcf_index', - ignore
'relative_pos', - relative position of variation on transcript
'transcript_length', - length of transcript
'chrom_y', - same as chrom_x
'pos', - position of variation
'id', - ID
'ref', - REF
'alt', - ALT
'qual', - QUAL
'filter', - FILTER
'info', - INFO
'format', - FORMAT
'samples' - SAMPLES 

]
Package: vcfpy2
vcfpy2Python 3 VCF library, based onvcfpy.This module is only intended to be used byHmtNote; it is an exact clone of thevcfpymodule, where a couple of lines of code were changed to better suit my needs.I found anissuein the original vcfpy module and made a relatedpull requestto fix it. However, it takes some time to accept my changes, while I need them implemented right now for my software to work properly, so I built this parallel module with the edits I need.As soon as the issue gets fixed in the original module, I will delete this one.CreditsThis package was created withCookiecutterand thecc-pypackageproject template. All credits, however, go tobihealththat created the originalvcfpypackage.History0.1.0 (2019-06-24)First release.0.1.1 (2019-06-24)Fix for weird vcf format.0.1.2 (2019-06-25)Fix handling of error with try-except clause (for the moment).
Package: vcfremapper
vcfremapperutility to convert coordinates of variants in VCF files between different genome
builds.InstallInstall via pip:pipinstallvcfremapperUsagevcfremapperPATH_TO_VCFCONVERTED--referencegenome.fa# or pass output to bgzipvcfremapperPATH_TO_VCF--referencegenome.fa|bgzip>CONVERTED# or pipe in gzipped datacatPATH_TO_VCF|vcfremapper--referencegenome.fa|bgzip>CONVERTEDOptions:--reference- path to reference genome, for the build being converted to.--build-in- build to convert from, defaults to hg19--build-out- build to convert to, defaults to hg38--tempdir- folder to save uncompressed temp file in before sorting
Package: vcfsyncer
vcfsyncer: A package to synchronize multiple VCFsThis is a way to transparently use single sample VCFs as if they were a
multi-sample VCF.InstallationThe simplest way to install vcfsyncer is through pip:pipinstallvcfsyncerUsageUse vcfsyncer within a python environmentfromvcfsyncerimportVCFSyncersynced=VCFSyncer(PATH_A,PATH_B,PATH_C)# you can iterate through the synchronized VCFsforvarinsynced:print(var.chrom,var.pos)# alternatively, fetch regions from the synchronized VCFs (if tabix-indexed)forvarinsynced.fetch(chrom='1',start=1000000,end=2000000):print(var.chrom,var.pos)
Package: vcfsynonymous
VCFSYNONYMOUSGiving.gff3and.fastafiles of a genome and.vcfdetected SNPs onto this genome,VCFSYNONYMOUSreturns two.vcffiles. The first one contains synonymous SNPs and the second one contains non-synonymous SNPs.INSTALLATIONpip3 install vcfsynonymousGET STARTEDvcfsynonymous --vcf tests/data/sample.vcf \
 --genome tests/data/genome.fasta \
 --annotation tests/data/genome.gff3python -m vcfsynonymous --vcf tests/data/sample.vcf \
 --genome tests/data/genome.fasta \
 --annotation tests/data/genome.gff3 \
 --output_prefix resupload packagepip3 uninstall vcfsynonymous
rm -rf build/
rm -rf dist/
rm -rf vcfsynonymous.egg-info/
python setup.py sdist bdist_wheel
pip3 install .

twine upload dist/*
Package: vcftoabook
VCF to addressbookA python script for converting .vcf contact files into abook files to be used in the addressbook program for Mutt/Neomutt.Note:This project is in the Alpha stage. Help with testing on various distros/vcf versions would be appreciatedRequirementsPython 3.6+InstallationRunpip install --user vcftoabookUsageSimply runvcftoabook file.vcfto generate an addressbook file. VCFtoAbook also works on directories and will parse all the .vcf files in a given directory and output one addressbook file. You may also specify an output file/path withvcftoabook file.vcf -o <path>/<filename>. Runvcftoabook -hfor help.To update an existing addressbook file, specify the-aflag and point your-oflag at the path to your addressbook file. VCFtoAbook will attempt to merge new incoming contacts with your existing ones andoverwriteyour output addressbook. Backups not included. Example:vcftoabook -ao ~/.abook/addressbook somecontact.vcfLicense:GPLv3
Package: vcftoolz
Tools for working with Variant Call Format files.VCF Toolz was developed by the United States Food
and Drug Administration, Center for Food Safety and Applied Nutrition.Free softwareDocumentation:https://vcftoolz.readthedocs.ioSource Code:https://github.com/CFSAN-Biostatistics/vcftoolzPyPI Distribution:https://pypi.python.org/pypi/vcftoolzFeaturesCompares the snps in two or more VCF files.Lists the snps that are unique to each VCF file with full genotype information per snp.Lists the snps that are missing from each VCF file if present in at least two other VCF files.Generates Venn diagrams of positions and snps in the VCF files.Reports precision, recall, and F1 score when the truth is known.Reports the effectiveness of filtered variants when the truth is known.Reformat the VCF file in a tall-narrow format for easy viewing and diffs.Count samples, positions, calls, snps, indels, other variants, missing calls, and filter reasons.Plot calls along the length of the genome and show the location of filtered calls.Citing VCF ToolzTo cite VCF Toolz, please reference the VCF Toolz paper:https://doi.org/10.21105/joss.01144LicenseSee the LICENSE file included in the VCF Toolz distribution.History1.2.3 (2023-06-02)Fixed imports in our fork of pyvenn1.2.2 (2023-06-01)Fixed broken Pyvenn dependency1.2.1 (2023-05-09)Update PyVCF to PyVCF31.2.0 (2019-04-04)Fix defect in narrow command wrongly printing ALT=. when GT=.Add thecountcommand to count samples, positions, calls, snps, indels,
other variants, filtered calls, missing calls, and filter reasons.Add theplotcommand to plot calls along the length of the genome and show
the location of filtered calls.Change the text of the compare report to refer to “Calls”, not “Sample snps”.Drop support for Python 3.4, which is not supported by matplotlib.Add support for Python 3.7.1.1.1 (2019-03-26)Replace None with ‘.’ when printing call data.Support VCF files with multiple alternate alleles per position.1.1.0 (2019-02-06)Support reading gzip compressed vcf files.1.0.0 (2018-11-20)First public release.
Package: vcfvalidator
vcfvalidatorThis package checks the metadata of VCF files with respect to the following paper:https://f1000research.com/articles/11-231Installing the package:pip install vcfvalidatorHow to use the validator:The package installs a CLI tool that can be used to check the VCF metdata with the following command:$ vcfvalidator validate-metadata PATH_TO_VCF_FILEThe VCF file under PATH_TO_VCF_FILE can be either compressed with GZIP (.vcf.gz) or uncompressed (.vcf).
Package: vcfViewer
# vcfViewer
A toolkit for visualizing variant call format (VCF) file data.Developed by Tom Sasani for MDCRC 6521 at the University of Utah.## AboutvcfVieweris a basic toolkit that allows users to create useful visualizations of VCF metadata in [bokeh](http://bokeh.pydata.org/en/latest/). For example, users can plot quality scores,## UsageTo visually determine which variants in a VCF meet a quality score threshold, simply run:python plot_qual.py [vcf] [threshold]## DependenciesbokehTo install, runconda install bokeh.cyvcf2To install, runpip install cyvcf2.
Package: vcfwiper
vcfwiperVcfWiperis a Python application that fixes VCF sequencing files
d reads from readable FASTQ files.Compatibility: Python <=3.9OS: Windows, Linux, Mac OSContributions:bioinformatics@css-mendel.itPypi:https://pypi.org/project/vcfwiperConda:https://anaconda.org/bfxcss/vcfwiperDocker Hub: available soonBug report:https://github.com/mazzalab/vcfwiper/issuesInstallationVcfWipercan be installed using both Conda and PyPi and runs smoothly on all OS specified above.Anaconda or MinicondaCreate and activate an empty Conda environment, if not already available.$ conda create -n VcfWiper python=3.9
$ conda activate VcfWiperthen$ conda install -y -c bfxcss -c conda-forge vcfwiperPypipip install vcfwiperUsagevcfwiper<options>options:
  --vcf_in TEXT          The input VCF file to be fixed    [required]
  --vcf_out TEXT         The wiped VCF file                [required]It accepts in input*.vcffiles.AuthorTommaso MazzaLaboratory of BioinformaticsFondazione IRCCS Casa Sollievo della SofferenzaViale Regina Margherita 261 - 00198 Roma ITTel: +39 06 44160526 - Fax: +39 06 44160548E-mail:t.mazza@css-mendel.itWeb page:http://www.css-mendel.itWeb page:http://bioinformatics.css-mendel.it
Package: vcfy
VCFYIt generates a VCF file with simulated random variants based on the given probability model.ToolsvcfyUsage: vcfy [OPTIONS] REFERENCE

  Generate VCF file with simulated variants in specified range [low, high)
  for the given region of the reference genome (in FASTA format). In case
  that the region is not specified the first region is used. If no range is
  provided, it is assumed that the variants are scattered throughout the
  region.

  For more information, consult with the README file.

Options:
  -o, --output FILENAME      Write to this file instead of standard output.
  -m, --mutation-rate FLOAT  Base mutation rate.  [required]
  -r, --region TEXT          Region ID (default=first region in the reference)
  -l, --low INTEGER          Range lower bound (default=first locus in the
                             region)
  -h, --high INTEGER         Range upper bound (default=last locus in the
                             region)
  --help                     Show this message and exit.ksnperUsage: ksnper [OPTIONS] [VCF]

  Report the number of SNPs in all k-mers. Specify the k and the VCF file,
  it reports number of SNPS occurred in each k-mer.

Options:
  -o, --output FILENAME           Write to this file instead of standard
                                  output.
  -r, --reference FILENAME        Reference genome FASTA file. It will be
                                  inferred from VCF header, if not specified.
  -k INTEGER                      The value of k.  [required]
  -c                              Set if the input VCF is compressed
  -d, --dialect [unix|excel-tab|excel]
                                  Use this CSV dialect.  [default: unix]
  --help                          Show this message and exit.