Welcome to Vcfparser’s documentation!¶

vcfparser¶

Python parser for parsing the genomics and transcriptomics VCF data.

Free software: MIT license
Documentation: https://vcfparser.readthedocs.io.

Features¶

No external dependency except python
Minimalistic in nature
With lots of control to api users

Installation¶

To install vcfparser, run this command in your terminal:

$ pip install vcfparser

In 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:

for record in records:
    chrom = record.CHROM
    pos = record.POS
    id = record.ID
    ref = record.REF
    alt = record.ALT
    qual = record.QUAL
    filter = record.FILTER
    format_ = record.format_
    infos = record.get_info_dict()
    mapped_sample = record.get_mapped_samples()

Modules¶

vcfparser package¶

Submodules¶

vcfparser.meta_header_parser module¶

class vcfparser.meta_header_parser.MetaDataParser(header_file)[source]¶

Bases: object

Parses a meta lines of the vcf files.

parse_lines()[source]¶: Parse a vcf metadataline

static split_to_dict(string)[source]¶

vcfparser.record_parser module¶

class vcfparser.record_parser.Record(line, header_line)[source]¶

Bases: object

A class for to store and extract the data lines in the vcf file.

__init__(line, header_line)[source]¶

Initializes the class with record lines and header lines.

Parameters:	line (str) – tab separated data lines (records) lines below # CHROM in vcf file header_line (str) – a line in vcf starting with # CHROM

deletion_overlapping_variant()[source]¶

get_info_dict(required_keys=None)[source]¶

Convert Info to dict for required keys

Parameters:	required_keys (list) – Keys of interest (default = all keys will be mapped)
Returns:	key: value pair of only required keys
Return type:	dict

Notes

If ‘=’ isn’t present then it will return its value as ‘.’.

Examples

>>> info_str = 'AC=2,0;AF=1.00;AN=8;BaseQRankSum'
>>> required_keys= ['AC', 'BaseQRankSum']
>>> get_info_dict(self, required_keys)
{'AC':2, 'BaseQRankSum' : '.'}

get_mapped_samples(sample_names=None, formats=None)[source]¶

Parameters:	sample_names (list) – list of sample names that needs to be filtered (default = all samples will be filtered) formats (list) – list of format tags that needs to be filtered (default = all formats will be filtered)
Returns:	dict of filtered sample names along with filtered formats
Return type:	dict

Examples

>>> mapped_sample = {'ms01e': {'GT': './.','PI': '.', 'PC': '.'}, 'MA622': {'GT': '0/0', 'PI': '.', 'PC': '.'}, 'MA611': {'GT': '0/0', 'PI': '.', 'PC': '.'}}
>>> get_mapped_samples(self, sample_names= ['ms01e', 'MA611'], formats= ['GT', 'PC'])
{'ms01e': {'GT': './.', 'PC': '.'}, 'MA611': {'GT': '0/0', 'PC': '.'}}

hasAllele(allele='0', tag='GT', bases='numeric')[source]¶

Parameters:	allele (str) – allele to check if it is present in given samples(default = ‘0’) tag (str) – format tags of interest (default = ‘GT’) bases (str) – iupac or numeric (default = ‘numeric’)
Returns:	dict of sample with values having given allele
Return type:	dict

hasINDEL()[source]¶

hasSNP()[source]¶

hasVAR(genotype='0/0', tag='GT', bases='numeric')[source]¶

Parameters:	genotype (str) – genotype to check if it is present in given samples(default = ‘0/0’) tag (str) – format tags of interest (default = ‘GT’) bases (str) – iupac or numeric (default = ‘numeric’)
Returns:	dict of sample with values having given genotype
Return type:	dict

has_phased(tag='GT', bases='numeric')[source]¶

Parameters:	tag (str) – format tags of interest (default = ‘GT’) bases (str) – iupac or numeric (default = ‘numeric’)
Returns:	dict of sample with values having ‘/’ in samples formats
Return type:	dict

Examples

>>> rec_keys_eg = 'CHROM        POS     ID      REF     ALT     QUAL    FILTER  INFO    FORMAT  ms01e   ms02g   ms03g   ms04h   MA611   MA605   MA622'

>>> rec_valeg = '2      15881018        .       G       A,C     5082.45 PASS    AC=2,0;AF=1.00;AN=8;BaseQRankSum=-7.710e-01;ClippingRankSum=0.00;DP=902;ExcessHet=0.0050;FS=0.000;InbreedingCoeff=0.8004;MLEAC=12,1;MLEAF=0.462,0.038;MQ=60.29;MQRankSum=0.00;QD=33.99;ReadPosRankSum=0.260;SF=0,1,2,3,4,5,6;SOR=0.657;set=HignConfSNPs GT:PI:GQ:PG:PM:PW:AD:PL:DP:PB:PC        ./.:.:.:./.:.:./.:0,0:0,0,0,.,.,.:0:.:. ./.:.:.:./.:.:./.:0,0:0,0,0,.,.,.:0:.:. ./.:.:.:./.:.:./.:0,0:0,0,0,.,.,.:0:.:. 1/1:.:6:1/1:.:1/1:0,2:49,6,0,.,.,.:2:.:.        0/0:.:78:0/0:.:0/0:29,0,0:0,78,1170,78,1170,1170:29:.:. 0/0:.:9:0/0:.:0/0:3,0,0:0,9,112,9,112,112:3:.:. 0/0:.:99:0/0:.:0/0:40,0,0:0,105,1575,105,1575,1575:40:.:.'
>>> from record_parser import Record
>>> rec_obj = Record(rec_valeg, rec_keys_eg)
>>> rec_obj.has_phased(tag="GT", bases="iupac")
{}

has_unphased(tag='GT', bases='numeric')[source]¶

Parameters:	tag (str) – format tags of interest (default = ‘GT’) bases (str) – iupac or numeric (default = ‘numeric’)
Returns:	dict of sample with values having ‘/’ in samples formats
Return type:	dict

Examples

>>> rec_keys_eg = 'CHROM        POS     ID      REF     ALT     QUAL    FILTER  INFO    FORMAT  ms01e   ms02g   ms03g   ms04h   MA611   MA605   MA622'

>>> rec_valeg = '2      15881018        .       G       A,C     5082.45 PASS    AC=2,0;AF=1.00;AN=8;BaseQRankSum=-7.710e-01;ClippingRankSum=0.00;DP=902;ExcessHet=0.0050;FS=0.000;InbreedingCoeff=0.8004;MLEAC=12,1;MLEAF=0.462,0.038;MQ=60.29;MQRankSum=0.00;QD=33.99;ReadPosRankSum=0.260;SF=0,1,2,3,4,5,6;SOR=0.657;set=HignConfSNPs GT:PI:GQ:PG:PM:PW:AD:PL:DP:PB:PC        ./.:.:.:./.:.:./.:0,0:0,0,0,.,.,.:0:.:. ./.:.:.:./.:.:./.:0,0:0,0,0,.,.,.:0:.:. ./.:.:.:./.:.:./.:0,0:0,0,0,.,.,.:0:.:. 1/1:.:6:1/1:.:1/1:0,2:49,6,0,.,.,.:2:.:.        0/0:.:78:0/0:.:0/0:29,0,0:0,78,1170,78,1170,1170:29:.:. 0/0:.:9:0/0:.:0/0:3,0,0:0,9,112,9,112,112:3:.:. 0/0:.:99:0/0:.:0/0:40,0,0:0,105,1575,105,1575,1575:40:.:.'
>>> from record_parser import Record
>>> rec_obj = Record(rec_valeg, rec_keys_eg)
>>> rec_obj.has_unphased(tag="GT", bases="iupac")
{'ms01e': './.', 'ms02g': './.', 'ms03g': './.', 'ms04h': '1/1', 'MA611': '0/0', 'MA605': '0/0', 'MA622': '0/0'}

hasnoVAR(tag='GT')[source]¶: Returns samples with empty genotype

isHETVAR(tag='GT', bases='numeric')[source]¶

Parameters:	tag (str) – format tags of interest (default = ‘GT’) bases (str) – iupac or numeric (default = ‘numeric’)
Returns:	dict of sample with values having homoref
Return type:	dict

isHOMREF(tag='GT', bases='numeric')[source]¶

Parameters:	tag (str) – format tags of interest (default = ‘GT’) bases (str) – iupac or numeric (default = ‘numeric’)
Returns:	dict of sample with values having homoref
Return type:	dict

Examples

>>> rec_keys_eg = 'CHROM        POS     ID      REF     ALT     QUAL    FILTER  INFO    FORMAT  ms01e   ms02g   ms03g   ms04h   MA611   MA605   MA622'

>>> rec_valeg = '2      15881018        .       G       A,C     5082.45 PASS    AC=2,0;AF=1.00;AN=8;BaseQRankSum=-7.710e-01;ClippingRankSum=0.00;DP=902;ExcessHet=0.0050;FS=0.000;InbreedingCoeff=0.8004;MLEAC=12,1;MLEAF=0.462,0.038;MQ=60.29;MQRankSum=0.00;QD=33.99;ReadPosRankSum=0.260;SF=0,1,2,3,4,5,6;SOR=0.657;set=HignConfSNPs GT:PI:GQ:PG:PM:PW:AD:PL:DP:PB:PC        ./.:.:.:./.:.:./.:0,0:0,0,0,.,.,.:0:.:. ./.:.:.:./.:.:./.:0,0:0,0,0,.,.,.:0:.:. ./.:.:.:./.:.:./.:0,0:0,0,0,.,.,.:0:.:. 1/1:.:6:1/1:.:1/1:0,2:49,6,0,.,.,.:2:.:.        0/0:.:78:0/0:.:0/0:29,0,0:0,78,1170,78,1170,1170:29:.:. 0/0:.:9:0/0:.:0/0:3,0,0:0,9,112,9,112,112:3:.:. 0/0:.:99:0/0:.:0/0:40,0,0:0,105,1575,105,1575,1575:40:.:.'
>>> from record_parser import Record
>>> rec_obj = Record(rec_valeg, rec_keys_eg)
>>> rec_obj.isHOMREF(tag="GT", bases="iupac")
{'MA611': 'G/G', 'MA605': 'G/G', 'MA622': 'G/G'}

isHOMVAR(tag='GT', bases='numeric')[source]¶

Parameters:	tag (str) – format tags of interest (default = ‘GT’) bases (str) – iupac or numeric (default = ‘numeric’)
Returns:	dict of sample with values having homoref
Return type:	dict

isMissing(tag='GT')[source]¶

Parameters:	tag (str) – format tags of interest (default = ‘GT’)
Returns:	dict of sample with values having homoref
Return type:	dict

iupac_to_numeric()[source]¶

map_records_long()[source]¶

Maps record values with record keys.

Returns:	dict with key value pair with sample and infos modified
Return type:	dict

static split_tag_from_samples(order_mapped_samples, tag, sample_names)[source]¶

Splits the tags of given samples from order_dict of mapped_samples

Parameters:	order_mapped_samples (OrderedDict) – tag (str) – sample_names (list) –
Returns:	list of list containing splitted tags
Return type:	list of list

Examples

>>> order_mapped_samples = OrderedDict([('ms01e',{'GT': './.', 'PI': '.'), ('MA622', 'GT': '0/0','PI': '.'})])
>>> tag = 'GT'
>>> sample_names = ['ms01e', 'MA622']
>>> split_tag_from_samples(order_mapped_samples, tag, sample_names)
[['.', '.'], ['0', '0']]

unmap_fmt_samples_dict(mapped_dict)[source]¶: Converts mapped dict again into string to write into the file.

vcfparser.vcf_parser module¶

class vcfparser.vcf_parser.VcfParser(filename)[source]¶

Bases: object

Parses a given vcf file into and outputs metainfo and yields records.

parse_metadata()[source]¶

parse_records()[source]¶

__init__(filename)[source]¶

Parameters:	filename (file) – input vcf file that needs to be parsed. bzip files are also supported.

parse_metadata()[source]: initialize variables to store meta infos

parse_records(chrom=None, pos_range=None, no_of_recs=1)[source]

Parse records from file and yield it.

Parameters:	chrom (str) – pos_range (str) – no_of_recs (int) –
Yields:	Record object on which we can perform different operations and extract required values

vcfparser.vcf_writer module¶

class vcfparser.vcf_writer.VCFWriter(filename)[source]¶

Bases: object

A vcf writer to write headerline and datalines into new file

add_contig(id, length, key='contig')[source]¶

add_filter(id, desc='', key='FILTER')[source]¶

add_filter_long(id, num='.', type='.', desc='', key='FILTER')[source]¶

add_format(id, num='.', type='.', desc='', key='FORMAT')[source]¶

add_header_line(record_keys)[source]¶

add_info(id, num='.', type='.', desc='', key='INFO')[source]¶

add_normal_metadata(key, value)[source]¶: This is used to add normal key value metadata like: fileformat, filedate, refrence

add_record_value(preheader, info, format_, sample_str)[source]¶

Module contents¶

Tutorial on record parser¶

Advanced Tutorial on vcf parser module showing most of the functions.

Let’s first import VcfParser module and instantiate an vcf object by passing vcf file as an argument.

Initial setup:¶

>>> from vcfparser import VcfParser
>>> vcf_obj = VcfParser('input_test.vcf')

VcfParser module has two main methods: - parse_metadata: It contains information related the header lines - parse_records: It contains methods to retrieve record values from the vcf file.

>>> metainfo = vcf_obj.parse_metadata()
>>> records = vcf_obj.parse_records()

Here, records is an generator object and applying next(records) yields the first record and we can access methods of Record class.

>>> first_record = next(records)

Methods on record object¶

You can filter infos you want from vcf. By default, all info will be returned as dictionary.

>>> first_record.get_info_dict()
{'AC': '2,0', 'AF': '1.00', 'AN': '8', 'BaseQRankSum': '-7.710e-01', 'ClippingRankSum': '0.00', 'DP': '902', 'ExcessHet': '0.0050', 'FS': '0.000', 'InbreedingCoeff': '0.8004', 'MLEAC': '12,1', 'MLEAF': '0.462,0.038', 'MQ': '60.29', 'MQRankSum': '0.00', 'QD': '33.99', 'ReadPosRankSum': '0.260', 'SF': '0,1,2,3,4,5,6', 'SOR': '0.657', 'set': 'HignConfSNPs'}
>>> first_record.get_info_dict(required_keys= ['AC', 'AF'])
{'AC': '2,0', 'AF': '1.00'}

Similarly, you can also filter formats and samples of interest from records. This allows you to retrieve the fields that you need.

>>> first_record.get_mapped_samples(sample_names= ['ms01e', 'MA611'], formats= ['GT','PG', 'PC', 'PI'])
{'ms01e': {'GT': './.', 'PG': './.', 'PC': '.', 'PI': '.'}, 'MA611': {'GT': '0/0', 'PG': '0/0', 'PC': '.', 'PI': '.'}}
>>> first_record.get_mapped_samples( formats= ['GT','PG'])
{'ms01e': {'GT': './.', 'PG': './.'}, 'ms02g': {'GT': './.', 'PG': './.'}, 'ms03g': {'GT': './.', 'PG': './.'}, 'ms04h': {'GT': '1/1', 'PG': '1/1'}, 'MA611': {'GT': '0/0', 'PG': '0/0'}, 'MA605': {'GT': '0/0', 'PG': '0/0'}, 'MA622': {'GT': '0/0', 'PG': '0/0'}}
>>> first_record.get_mapped_samples(sample_names =['ms02g'])
{'ms02g': {'GT': './.', 'PI': '.', 'GQ': '.', 'PG': './.', 'PM': '.', 'PW': './.', 'AD': '0,0', 'PL': '0,0,0,.,.,.', 'DP': '0', 'PB': '.', 'PC': '.'}}

You can also check if any sample have alleles of your interest.

>>> first_record.hasAllele(allele='1', tag= 'GT', bases = 'iupac')
{'ms04h': 'A/A'}
>>> first_record.hasAllele(allele='1', tag= 'GT', bases = 'numeric')
{'ms04h': '1/1'}
>>> first_record.hasAllele(allele='1', tag= 'PG', bases = 'numeric')
{'ms04h': '1/1'}
>>> first_record.hasAllele(allele='0', tag= 'PG', bases = 'numeric')
{'MA611': '0/0', 'MA605': '0/0', 'MA622': '0/0'}
>>> first_record.hasAllele(allele='0', tag= 'PG', bases = 'iupac')
{'MA611': 'G/G', 'MA605': 'G/G', 'MA622': 'G/G'}

To check if samples mapped with format tags contains genotype specified :

>>> first_record.hasVAR(genotype='0/0', tag= 'PG', bases = 'numeric')
{'MA611': '0/0', 'MA605': '0/0', 'MA622': '0/0'}
>>> first_record.hasVAR(genotype='G/G', tag= 'PG', bases = 'iupac')
{'MA611': 'G/G', 'MA605': 'G/G', 'MA622': 'G/G'}
>>> first_record.hasVAR(genotype='1/1', tag= 'PG', bases = 'numeric')
{'ms04h': '1/1'}
>>> first_record.hasVAR(genotype='A/A', tag= 'PG', bases = 'iupac')
{'ms04h': 'A/A'}

To check if samples have phased genotype or unphased genotype:

>>> first_record.has_phased()
{}
>>> first_record.has_unphased()
{'ms01e': './.', 'ms02g': './.', 'ms03g': './.', 'ms04h': '1/1', 'MA611': '0/0', 'MA605': '0/0', 'MA622': '0/0'}
>>> first_record.has_unphased(tag= 'PG')
{'ms01e': './.', 'ms02g': './.', 'ms03g': './.', 'ms04h': '1/1', 'MA611': '0/0', 'MA605': '0/0', 'MA622': '0/0'}
>>> first_record.has_unphased(tag= 'PG', bases = 'iupac')
{'ms01e': './.', 'ms02g': './.', 'ms03g': './.', 'ms04h': 'A/A', 'MA611': 'G/G', 'MA605': 'G/G', 'MA622': 'G/G'}

This returns samples with no variants (i.e. contains ‘./.’, ‘.|.’, ‘.’)

>>> first_record.hasnoVAR()
{'ms01e': './.', 'ms02g': './.', 'ms03g': './.'}
>>> first_record.hasnoVAR(tag= 'PG')
{'ms01e': './.', 'ms02g': './.', 'ms03g': './.'}

Samples with homozygous reference genotypes can be retrieved by:

>>> first_record.isHOMREF()
{'MA611': '0/0', 'MA605': '0/0', 'MA622': '0/0'}
>>> first_record.isHOMREF(tag= 'PG', bases= 'iupac')
{'MA611': 'G/G', 'MA605': 'G/G', 'MA622': 'G/G'}

Similarly, samples with homozygous variant genotypes can be retrieved by:

>>> first_record.isHOMVAR()
{'ms04h': '1/1'}
>>> first_record.isHOMVAR(tag= 'PG', bases= 'iupac')
{'ms04h': 'A/A'}

Samples with heterozygous variant genotypes in given record”

>>> first_record.isHETVAR()
{}

This returns samples with missing variants (i.e. contains ‘./.’, ‘.|.’, ‘.’)

>>> first_record.isMissing()
{'ms01e': './.', 'ms02g': './.', 'ms03g': './.'}
>>> first_record.isMissing(tag = 'PI')
{'ms01e': '.', 'ms02g': '.', 'ms03g': '.', 'ms04h': '.', 'MA611': '.', 'MA605': '.', 'MA622': '.'}

map_records_long method maps all the record with the header lines. This also maps format with samples and info fields into dictionary.

>>> first_record.map_records_long()
{'CHROM': '2', 'POS': '15881018', 'ID': '.', 'REF': 'G', 'ALT': 'A,C', 'QUAL': '5082.45', 'FILTER': 'PASS', 'INFO': {'AC': '2,0', 'AF': '1.00', 'AN': '8', 'BaseQRankSum': '-7.710e-01', 'ClippingRankSum': '0.00', 'DP': '902', 'ExcessHet': '0.0050', 'FS': '0.000', 'InbreedingCoeff': '0.8004', 'MLEAC': '12,1', 'MLEAF': '0.462,0.038', 'MQ': '60.29', 'MQRankSum': '0.00', 'QD': '33.99', 'ReadPosRankSum': '0.260', 'SF': '0,1,2,3,4,5,6', 'SOR': '0.657', 'set': 'HignConfSNPs'}, 'FORMAT': 'GT:PI:GQ:PG:PM:PW:AD:PL:DP:PB:PC', 'ms01e': './.:.:.:./.:.:./.:0,0:0,0,0,.,.,.:0:.:.', 'ms02g': './.:.:.:./.:.:./.:0,0:0,0,0,.,.,.:0:.:.', 'ms03g': './.:.:.:./.:.:./.:0,0:0,0,0,.,.,.:0:.:.', 'ms04h': '1/1:.:6:1/1:.:1/1:0,2:49,6,0,.,.,.:2:.:.', 'MA611': '0/0:.:78:0/0:.:0/0:29,0,0:0,78,1170,78,1170,1170:29:.:.', 'MA605': '0/0:.:9:0/0:.:0/0:3,0,0:0,9,112,9,112,112:3:.:.', 'MA622': '0/0:.:99:0/0:.:0/0:40,0,0:0,105,1575,105,1575,1575:40:.:.', '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': '.'}}}

Welcome to Vcfparser’s documentation!¶

vcfparser¶

Features¶

Installation¶

Usage¶

Get meta information about the vcf file¶

Get Records from vcf file¶

Modules¶

vcfparser package¶

Submodules¶

vcfparser.meta_header_parser module¶

vcfparser.record_parser module¶

vcfparser.vcf_parser module¶

vcfparser.vcf_writer module¶

Module contents¶

Tutorial on record parser¶

Initial setup:¶

Methods on record object¶

Credits¶

Development Lead¶

Contributors¶

Indices and tables¶

Vcfparser

Navigation

Related Topics