#Identifying recurrent mutations in cancer
This is a method to identify population-scale recurrent mutations in cancer based on a binomial statisical model that incoporates underlying mutational processes including nucleotide context mutability, gene-specific mutation rates, and major expected patterns of hotspot mutation emergence
Need R Version 3.0.2 or higher Install dependent packages (data.table, IRanges, BSgenome.Hsapiens.UCSC.hg19) as follows:
install.packages("data.table")
source("http://bioconductor.org/biocLite.R")
biocLite("IRanges","BSgenome.Hsapiens.UCSC.hg19")
####Usage:
./hotspot_algo.R
--input-maf=[REQUIRED: mutation file]
--rdata=[REQUIRED: Rdata object with necessary files for algorithm]
--output-file=[REQUIRED: output file to print statistically significant hotspots]
--gene-query=[OPTIONAL (default=all genes in mutation file): List of Hugo Symbol in which to query for hotspots]
--homopolymer=[OPTIONAL (default=TRUE): TRUE|FALSE filter hotspot mutations in homopolymer regions]
--filter-centerbias=[OPTIONAL (default=FALSE): TRUE|FALSE to identify false positive filtering based on mutation calling center bias]
--align100mer=[OPTIONAL: BED file of hg19 UCSC alignability track for 100-mer length sequences for false positive filtering]
--align24mer=[OPTIONAL: BED file of hg19 UCSC alignability track for 24-mer length sequences for false positive filtering]
Command to run hotspot algorithm on genes listed in file genes_of_interest.txt:
./hotspot_algo.R \
--input-maf=pancancer_unfiltered.maf \
--rdata=hotspot_algo.Rdata \
--gene-query=genes_of_interest.txt \
--output-file=sig_hotspots.txt
####Contents:
[ Required ] hotspot_algo.R - R script to execute hotspot detection algorithm
[ Required ] hotspot_algo.Rdata - Rdata object with necessary files for algorithm (mutability, expression filters, etc)
[ Required ] funcs.R - R script of functions necessary for proper execution of hotspot_algo.R
genes_of_interest.txt - Sample list of genes for hotspot detection
minimalist_test_maf.txt - minimalist MAF needed from maf2maf. mskcc/maf2maf
####Notes:
--align100mer and --align24mer are optional filters based on how uniquely k-mer sequences align to a region of the hg19 genome. Note, both filters were used as part of this analysis. See more information at ENCODE Mapability.
The use of these filters will require downloading the 100-mer and 24-mer alignability tracks from UCSC that are not included here: ENCODE CRG Alignability 100-mer ENCODE CRG Alignability 24-mer
Convert these downloaded bigWig to bedgraph format, following instructions here: UCSC BigWig
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