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Description:

ICGCdataparser 0.2.2

What is the ICGC-data-parser?

A library to ease the parsing of data from the International Cancer Genome
Consortium data releases, in particular, the simple somatic mutation
aggregates.

Tutorial

Installation
Install via PyPI:
$ pip install ICGC_data_parser

Data download
The base data for the scripts is the ICGC’s aggregated of the simple
somatic mutation data. Which can be downloded using
wget https://dcc.icgc.org/api/v1/download?fn=/current/Summary/simple_somatic_mutation.aggregated.vcf.gz
To know more about this file, please read About the ICGC’s simple
somatic mutations
file
WARNING: The current release of the data contains a malformed
header that causes the library to crash with an IndexError:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
~/.local/lib/python3.6/site-packages/vcf/parser.py in _parse_info(self, info_str)
389 try:
...
...
...
362 def _parse_info(self, info_str):

ValueError: could not convert string to float: 'PCAWG'
This is caused by a bad type specification in the header of the
VCF file. To solve it, use the lollowing line after creating the
SSM_Reader object (asuming the reader is in the reader
variable)
# Fix weird bug due to malformed description headers
reader.infos['studies'] = reader.infos['studies']._replace(type='String')
In the future this will be solved in a more elegant way, but for
now this is what we’ve got.

Usage
The main class in the project is the SSM_Reader.
It allows to read easily the ICGC mutations file:
>>> from ICGC_data_parser import SSM_Reader

# Reads also compressed files!
>>> reader = SSM_Reader(open('data/simple_somatic_mutations.aggregated.vcf.gz'))

# or...
>>> reader = SSM_Reader(filename='data/simple_somatic_mutations.aggregated.vcf.gz')
# ^^^^^^^^
# The filename keyord argument is important, else we get an IndexError
The SSM_Reader.parse
method allows to iterate through the records of the file and access the parts
of the record. You can also specify regular expressions to filter only the
lines you want:
# Print only the mutations that are in the
# European Union Breast Cancer project (BRCA-EU).

>>> for record in reader.parse(filters=['BRCA-EU']):
... print(record.ID, record.CHROM, record.POS)

MU66865518 1 100141201
MU65487875 1 100160548
MU66281118 1 100638179
MU66254120 1 101352655
...
The INFO field is special in the sense that it contains several
subfields, AND those subfields may be list-like entries with more
subfields themselves (in particular the CONSEQUENCE and OCCURRENCE
subfields):
# The subfields of the INFO field:
>>> next(reader).INFO

{'CONSEQUENCE': [
'||||||intergenic_region||',
'CD1A|ENSG00000158477|+|CD1A-001|ENST00000289429||upstream_gene_variant||'
],
'OCCURRENCE': [
'ESAD-UK|1|301|0.00332',
'EOPC-DE|1|202|0.00495',
'BRCA-EU|1|569|0.00176'
],
'affected_donors': 3,
'mutation': 'T>A',
'project_count': 3,
'studies': None,
'tested_donors': 12068}
# The description of the CONSEQUENCE subfield
>>> print(reader.infos['CONSEQUENCE'].desc)

Mutation consequence predictions annotated by SnpEff
(subfields: gene_symbol|gene_affected|gene_strand|transcript_name|transcript_affected|protein_affected|consequence_type|cds_mutation|aa_mutation)
# The description of the OCCURRENCE subfield
>>> print(reader.infos['OCCURRENCE'].desc)

Mutation occurrence counts broken down by project
(subfields: project_code|affected_donors|tested_donors|frequency)
Sometimes we want to also parse the information in those subfields. For
this purpose, the SSM_Reader.subfield_parser factory method is
useful. This method creates a parser of the specified subfield that
allows easy access to the data:
# Create the subfield parser for the CONSEQUENCE subfield
>>> consequences = reader.subfield_parser('CONSEQUENCE')

>>> for record in reader.parse():
... # Which genes are affected?
... genes_affected = {c.gene_symbol
... for c in consequences(record)
... if c.gene_affected}
...
... print(f'Mutation: {record.ID}')
... print('\t', ", ".join(genes_affected))

Mutation: MU93246178
TPM3
Mutation: MU66962994
RP11-350G8.9, SHE
Mutation: MU93246498
DCST1, ADAM15, RP11-307C12.11
Mutation: MU66377106
EFNA3, ADAM15, EFNA4
...
The library also contains some helper scripts to manipulate VCF files
(like the ICGC mutations file):

vcf_map_assembly.py: Creates a new VCF with the positions mapped to
another genome assembly. This is useful because currently the positions
reported by ICGC are in the human genome assembly GRCh37, while the most recent
(and the one the rest of the world uses) is the GRCh38 assembly.
vcf_sample.py: Creates a new VCF with a fraction of the mutations in the
original. The mutations are randomly sampled but maintain the order they had in
the original file. This is useful when one wants to make small test analysis on
the data, but still wants the results to be representative of all the
mutations.
vcf_split.py: Splits the input VCF into several (also valid VCFs),
this is useful in case one wants to split the analyses into processes
that receive one file each.

The specific documentation of the scripts can be obtained by executing:
$ python3 <script name>.py --help
Also, the library is shipped with some Jupyter Notebooks that elaborate
on the examples. Besides, in the notebooks are demonstrated ways
to manage common parsing errors that have to do with malformed input
files.

Meta
Author:
Ad115 -
Github –
a.garcia230395@gmail.com
Project pages:
Docs - @GitHub - @PyPI
Distributed under the MIT license. See
LICENSE for
more information.

Contributing

Check for open issues or open a fresh issue to start a discussion
around a feature idea or a bug.
Fork the repository
on GitHub to start making your changes to a feature branch, derived
from the master branch.
Write a test which shows that the bug was fixed or that the feature
works as expected.
Send a pull request and bug the maintainer until it gets merged and
published.