vcfparser

Python (version >=3.6) package for parsing the genomics and transcriptomics VCF data.

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

Features

No external dependency except python (version >=3.6).
Minimalistic in nature.
Provides a lot of features to API users.
Cython compiling is provided to optimize performance.

Installation

Method A:

VCFsimplify uses vcfparser API, so the package is readily available if VCFsimplify is already installed.

This is only preferred while developing/optimizing VcfSimplify along with vcfparser.

Navigate to the VCFsimplify directory -> activate python -> call the 'vcfparser' package.

$ C:\Users\>cd VCF-Simplify
$ C:\Users\>cd VCF-Simplify>dir
  Volume in drive C is StorageDrive
  Volume Serial Number is .........

  Directory of C:\Users\VCF-Simplify

  07/12/2020  10:14 AM    <DIR>          .
  07/12/2020  10:14 AM    <DIR>          ..
  07/12/2020  08:55 AM    <DIR>          .github
  ............................
  ............................
  07/12/2020  10:42 AM    <DIR>          vcfparser
  07/12/2020  08:55 AM             1,494 VcfSimplify.py
          11 File(s)     20,873,992 bytes
          13 Dir(s)  241,211,793,408 bytes free

$ C:\Users\VCF-Simplify>python
Python 3.8.1 (tags/v3.8.1:1b293b6, Dec 18 2019, 22:39:24) [MSC v.1916 (Intel)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> from vcfparser import VcfParser
>>>

Method B (preferred method):

Pip is the preferred method of installing and using vcfparser API if custom python scripts/app are being developed.

$ pip install vcfparser

Method C:

For offline install, or in order to build from the source code, follow :ref:`advance install <advanced-install>`.

Cythonize (optional but helpful)

The installed "vcfparser" package can be cythonized to optimize performance. Cythonizing the package can increase the speed of the parser by about x.x - y.y (?) times.

TODO: Bhuwan - add required cython method in here

Usage

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

Get metadata information from 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 the vcf file

>>> records = vcf_obj.parse_records()
    # Note: Records are returned as 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'}}

TODO: Bhuwan (priority - high) The very last example "first_record.get_mapped_samples()" is returning the value of the last sample/key with "n". i.e: 'PC': '.n' Please fix that issue - strip('n') in the line before parsing.

Alternately, we can loop over each record by using a 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()

For more specific use cases please check the examples in the following section:

For tutorials in metadata, please follow :ref:`Metadata Tutorial <metadata-tutorial>`.

For tutorials in record parser, please follow :ref:`Record Parser Tutorial <record-parser-tutorial>`.

everestial / vcfparser