Tutorial on record parser¶

Advanced tutorial on vcf parser module showing available methods for parsing records.

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')

We can also pass gzipped vcf file as an argument.

>>> vcf_obj = VcfParser('input_test.vcf.gz')

VcfParser module has two main methods:

parse_metadata: to extract the metadata information from VCF metadata header.
parse_records: to retrieve the record values from the VCF record lines.

Accessing VCF records:¶

Step 01:

>>> # pass the VCF object to the 'parse_records()' function
>>> records = vcf_obj.parse_records()

Step 02:

Yield record values - Method A: using next()

records is an generator object. Therefore, applying next(records) yields the very first record as Record object.

Subsequent next(records) will yield subsequent records after that first record from the VCF.

parse_records() uses the Record class which can be used directly if record_keys and record_vals are handy.

For more info about Record visit Record.

>>> first_record = next(records)
>>> print(first_record)
2       15881224        .       T       G       143.24  PASS    AC=0;AF=0.036;AN=12;BaseQRankSum=1.75;ClippingRankSum=0.00;DP=591;ExcessHet=3.0103;FS=3.522;InbreedingCoeff=-0.1072;MLEAC=1;MLEAF=0.036;MQ=41.48;MQRankSum=0.366;QD=15.92;ReadPosRankSum=0.345;SF=0,1,2,3,4,5,6;SOR=2.712;set=HignConfSNPs   GT:PM:PG:GQ:AD:PW:PI:PL:PC:PB:DP       ./.:.:./.:.:0:./.:.:.,.,.:.:.:0 0/0:.:0/0:3:1:0/0:.:.,.,.:.:.:1        0/0:.:0/0:12:4:0/0:.:.,.,.:.:.:4        0/0:.:0/0:3:4:0/0:.:.,.,.:.:.:4        0/0:.:0/0:30:17,0:0/0:.:0,30,450:.:.:17 0/0:.:0/0:15:7,0:0/0:.:0,15,225:.:.:7  0/0:.:0/0:39:25,0:0/0:.:0,39,585:.:.:25

Yield record values - Method B: using for-loop

Each record in the VCF can also be accessed on a for-loop

>>> for record in records:
...     print(record)
...     record.POS
...     break
...
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        0/1:5:.:0|1:.:./.: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:.:.
'15881018'

Step 03: Extract data using Record object attribute and methods

Record object also has several attributes and methods which allows us to extract the record values as list or dictionary.

>>> " list of available attribute and methods "
>>> dir(first_record)  # or print(dir(record)) on a for loop
['ALT', 'CHROM', 'FILTER', 'ID', 'POS', 'QUAL', 'REF', '__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_map_fmt_to_samples', '_to_iupac', 'deletion_overlapping_variant', 'format_', 'get_info_as_dict', 'get_mapped_samples', 'get_mapped_tag_list', 'hasAllele',
'hasINDEL', 'hasSNP', 'hasVAR', 'has_phased', 'has_unphased', 'hasnoVAR', 'info_str', 'isHETVAR', 'isHOMREF', 'isHOMVAR', 'isMissing', 'iupac_to_numeric', 'map_records_long', 'mapped_format_to_sample', 'rec_line', 'record_keys', 'record_vals', 'ref_alt', 'sample_names', 'sample_vals', 'get_tag_values_from_samples', 'unmap_fmt_samples_dict', 'vTest']

Attributes

>>> # available attributes in the "record" object are:
CHROM, POS, REF, ALT, ref_alt, QUAL, FILTER, info_str, format_, sample_names, sample_vals, mapped_format_to_sample

>>> "Access simple position level attribute values as"
>>> first_record.CHROM
'2'
>>> first_record.POS
'15881018'
>>> first_record.REF, first_record.ALT, first_record.QUAL, first_record.FILTER
('G', ['A', 'C'], '5082.45', ['PASS'])
>>> first_record.ref_alt  # call REF and ALT allele together
['C', 'CA']

>>> # keys represented in the "CHROM" line of the VCF
>>> first_record.record_keys
['CHROM', 'POS', 'ID', 'REF', 'ALT', 'QUAL', 'FILTER', 'INFO', 'FORMAT', 'ms01e', 'ms02g', 'ms03g', 'ms04h', 'MA611', 'MA605', 'MA622']
>>> # Note: "record_keys" available within record object are same as the one from metainfo object.
>>> metainfo.record_keys  # from "parse_metadata()"
['CHROM', 'POS', 'ID', 'REF', 'ALT', 'QUAL', 'FILTER', 'INFO', 'FORMAT', 'ms01e', 'ms02g', 'ms03g', 'ms04h', 'MA611', 'MA605', 'MA622']
>>>
>>> first_record.record_values  # record values as list
['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:.:.']

>>> "Population level information is provided by the INFO key"
>>> # accessed using 'info_str'
>>> first_record.info_str   # info values as string
'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'

>>> "Sample level infomation are extracted by matching the FORMAT tags with their corresponding values in the SAMPLE"
>>> first_record.format_  # available tags in FORMAT
['GT', 'PI', 'GQ', 'PG', 'PM', 'PW', 'AD', 'PL', 'DP', 'PB', 'PC']

>>> first_record.sample_names  # sample names
['ms01e', 'ms02g', 'ms03g', 'ms04h', 'MA611', 'MA605', 'MA622']

>>> first_record.sample_vals  # sample values as list
['./.:.:.:./.:.:./.: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:.:.']

>>> # a default full map of the FORMAT tags to SAMPLE values
>>> first_record.mapped_format_to_sample
OrderedDict([('ms01e', {'GT': '.', 'AD': '.', 'PI': '.', 'PW': '.', 'PG': '.',
'PM': '.', 'GQ': '.', 'DP': '.', 'PB': '.', 'PC': '.', 'PL': '.'}), ('ms02g', {'GT': '.', 'AD': '.', 'PI': '.', 'PW': '.', 'PG': '.', 'PM': '.', 'GQ': '.', 'DP': '.', 'PB': '.', 'PC': '.', 'PL': '.'}), ('ms03g', {'GT': '.', 'AD': '.', 'PI': '.', 'PW': '.', 'PG': '.', 'PM': '.', 'GQ': '.', 'DP': '.', 'PB': '.', 'PC': '.', 'PL': '.'}), ('ms04h', {'GT': '.', 'AD': '.', 'PI': '.', 'PW': '.', 'PG': '.', 'PM': '.', 'GQ': '.', 'DP': '.', 'PB': '.', 'PC': '.', 'PL': '.'}), ('MA611', {'GT': '0/0', 'AD': '20,0', 'PI': '.', 'PW': '0/0', 'PG': '0/0', 'PM': '.', 'GQ': '54', 'DP': '20', 'PB': '.', 'PC': '.', 'PL': '0,54,810'}), ('MA605',
{'GT': '0/0', 'AD': '6,0', 'PI': '.', 'PW': '0/0', 'PG': '0/0', 'PM': '.', 'GQ': '18', 'DP': '6', 'PB': '.', 'PC': '.', 'PL': '0,18,206'}), ('MA622', {'GT': '0/0', 'AD': '27,0', 'PI': '.', 'PW': '0/0', 'PG': '0/0', 'PM': '.', 'GQ': '72', 'DP': '27', 'PB': '.', 'PC': '.', 'PL': '0,72,1080'})])

Methods on record object

Very specific parsing of the record object can be done using the provided methods. These methods take several args and kwargs to narrow down the information available in the Record object.

>>> "Parse the INFO string data using get_info_as_dict()"
>>> first_record.info_str   # the original info values as string
'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_as_dict() # info values as dictionary
{'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'}

>>> # info_keys can be provided extract specific keys:value
>>> first_record.get_info_as_dict(info_keys= ['AC', 'AF'])
{'AC': '2,0', 'AF': '1.00'}

>>> "More controlled FORMAT tag to SAMPLE value mapping can be done using get_format_to_sample_map()"
>>> # it helps to extract specific FORMAT tag values from specific SAMPLE
>>> first_record.get_format_to_sample_map(sample_names= ['ms01e', 'MA611'], formats= ['GT', 'PC'])
{'ms01e': {'GT': './.', 'PC': '.'}, 'MA611': {'GT': '0/0', 'PC': '.'}}

>>> "the mapped genotype values can be converted to IUPAC bases using the convert_to_iupac flag"
>>> first_record.get_format_to_sample_map(sample_names= ['ms01e', 'MA611'], formats= ['GT', 'PC'], convert_to_iupac=['GT'])
{'ms01e': {'GT': './.', 'PC': '.', 'GT_iupac': './.'}, 'MA611': {'GT': '0/0', 'PC': '.', 'GT_iupac': 'G/G'}}
>>> first_record.get_format_to_sample_map(sample_names= ['ms01e', 'MA611'], formats= ['GT', 'PC'], convert_to_iupac=['GT', 'PG'])
{'ms01e': {'GT': './.', 'PC': '.', 'GT_iupac': './.', 'PG_iupac': './.'}, 'MA611': {'GT': '0/0', 'PC': '.', 'GT_iupac': 'G/G', 'PG_iupac': 'G/G'}}

>>> # get a full mapping for all the record_keys and FORMAT within SAMPLE
>>> # Note: This mapping is only activated when called with lazy instantiation

>>> first_record.get_full_record_map()
{'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': '.'}}}

>>> # full mapping has the option to convert genotype bases to IUPAC
>>> first_record.get_full_record_map(convert_to_iupac= ['GT'])
{'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': '.', 'GT_iupac': './.'}, 'ms02g': {'GT': './.', 'PI': '.', 'GQ': '.', 'PG': './.', 'PM': '.', 'PW': './.', 'AD': '0,0', 'PL': '0,0,0,.,.,.', 'DP': '0', 'PB': '.', 'PC': '.', 'GT_iupac': './.'}, 'ms03g': {'GT': './.', 'PI': '.', 'GQ': '.', 'PG': './.', 'PM': '.', 'PW': './.', 'AD': '0,0', 'PL': '0,0,0,.,.,.', 'DP': '0', 'PB': '.', 'PC': '.', 'GT_iupac': './.'}, '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': '.', 'GT_iupac': 'A/A'}, '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': '.', 'GT_iupac': 'G/G'}, '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': '.', 'GT_iupac': 'G/G'}, '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': '.', 'GT_iupac': 'G/G'}}}
>>> # Note: "convert_to_iupac" will add the genotype tag with suffix "_iupac" to show the genotype in IUPAC bases.

Genotype parsing

Genotype checks and parsing are one of most important use case of VCF data. VcfParser provides several methods to do those checks and extract data.

Check samples that 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'}

Check samples with specific genotype. Both numeric and iupac checks are available.

>>> 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'}

>>> # genotypes can be checked in phased state
>>> first_record.hasVAR(genotype='0|0', tag='GT', bases='numeric')
{}

Check phased vs unphased genotype. Specific genotype tag can be checked; default is ‘GT’.

>>> 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='numeric')
{'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'}

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

>>> first_record.hasnoVAR()
{'ms01e': './.', 'ms02g': './.', 'ms03g': './.'}
>>> first_record.hasnoVAR(tag='GT')
{'ms01e': '.', 'ms02g': '.', 'ms03g': '.', 'ms04h': '.'}
>>> first_record.hasnoVAR(tag= 'PG')
{'ms01e': './.', 'ms02g': './.', 'ms03g': './.'}

Samples with homozygous reference genotypes can be retrieved as.

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

>>> #if another FORMAT tag also represents a genotype, specific the FORMAT tag
>>> first_record.isHOMREF(tag='PG', bases='numeric')
{'MA611': '0/0', 'MA605': '0/0', 'MA622': '0/0'}
>>> first_record.isHOMREF(tag='PG', bases='iupac')
{'MA611': 'C/C', 'MA605': 'C/C', 'MA622': 'C/C'}

Similarly, samples with homozygous variant genotypes can also be retrieved.

>>> 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 for certain FORMAT tags(i.e. contains ‘./.’, ‘.|.’, ‘.’). Currently we used ‘GT’ tag as default.

>>> first_record.isMissing()
{'ms01e': './.', 'ms02g': './.', 'ms03g': './.'}

>>> # missing checks can be applied to other FORMAT tags too.
>>> first_record.isMissing(tag = 'PI')
{'ms01e': '.', 'ms02g': '.', 'ms03g': '.', 'ms04h': '.', 'MA611': '.', 'MA605': '.', 'MA622': '.'}

>>> first_record.isMissing(tag='GQ')
{'ms01e': '.', 'ms02g': '.', 'ms03g': '.', 'ms04h': '.'}

Tutorial on record parser¶

Initial setup:¶

Accessing VCF records:¶

Vcfparser

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