README

The primary results from the paper can be reproduced with pipe.sh

We have also included a script, get_alts_stats.py, that can be used to generate percentile statistics for cells, describing where they lie on the normal cell putative driver alteration distributions.

get_alts_stats.py takes a directory, containing VCF files as input, an OncoKB API key file, a file containing normal cell counts (given by normal_counts_vec), and optionally an output file path:

python3 get_alts_stats.py -h
usage: get_alts_stats.py [-h] -i INPUT [-o OUTPUT] [-k KEY] [-t FLAG_THRESHOLD] -n NORMAL_COUNTS

options:
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        Directory containing SnpEff-annotated VCFs to be processed
  -o OUTPUT, --output OUTPUT
                        Output file name
  -k KEY, --key KEY     OncoKB API key: a file with a single line containing the key
  -t FLAG_THRESHOLD, --flag-threshold FLAG_THRESHOLD
                        Flag cells that lie greater than this percentile on the normal cell counts
                        distribution
  -n NORMAL_COUNTS, --normal-counts NORMAL_COUNTS
                        Path to normal counts file

The OncoKB API key should be present in a single line file just containing the key:

$ cat oncokb_key 
abcdefgh-1234-4321

The output file is a tab-delimited file with two columns, one for sample UID (the stem of the corresponding VCF), and one for the percentile statistic.

The normal counts vector was generated according to the methodology used in the paper, by randomly selecting cells from GSE130473, GSE81547, GSE109822, and SRP271375.

Data availability

Raw values used to create figures are given in the figure_data directory. Additional processed data is available in the raw_data.tar.gz file, and the raw data used in this work is available from GEO under the accessions note in the manuscript.

File descriptions

• annotate_prot_variants.py - Annotates SnpEff output VCFs with OncoKB data
• codon_map.py - Just the codon map, separate file for readability of the others
• covmachine.py - Pipe genome pileups into this to track coverage at bases of interest
• get_alts_stats.py - Generate statistics for cells from SnpEff-annotated VCFs
• gtf_parser.py - Parses a GTF to extract bases corresponding to residues of interest
• pipe.sh - pipeline to reproduce TNBC dataset results
• samples.tsv - TNBC dataset SRA UIDs and sample UIDs

OncoKB API key requirement

Reproducing this work or running the annotation script will requires an OncoKB API key, which can be obtained at OncoKB.

annotate_prot_variants.py will by look for this in a single-line text file located at ./oncokb_key.

Additional requirements

Requires snpsubtract and a VCF file with common variants, obtainable from dbSNP.