Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor
S M Ashiqul Islam,
Erik N. Bergstrom,
Jon W Teague,
Yun Rose Li,
Andreas J. Gruber,
Christopher D Steele,
Samuel W Brady,
Boian Stoianov Alexandrov,
David J. Adams,
David C Wedge,
Maria Teresa Landi,
Michael R. Stratton,
Steven G Rozen,
Ludmil B. Alexandrov
Posted 13 Dec 2020
bioRxiv DOI: 10.1101/2020.12.13.422570
Posted 13 Dec 2020
Mutational signature analysis is commonly performed in genomic studies surveying cancer and normal somatic tissues. Here we present SigProfilerExtractor, an automated tool for accurate de novo extraction of mutational signatures for all types of somatic mutations. Benchmarking with a total of 33 distinct scenarios encompassing 1,106 simulated signatures operative in more than 200,000 synthetic genomes demonstrates that SigProfilerExtractor outperforms ten other tools across all datasets with and without noise. For simulations with 5% noise, reflecting high-quality genomic datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true positive signatures while yielding more than 5-fold less false positive signatures. Applying SigProfilerExtractor to 2,778 whole-genome sequenced cancers reveals three previously missed mutational signatures. Two of the signatures are confirmed in independent cohorts with one of these signatures associating with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting mutational signatures, and several novel mutational signatures including a signature putatively attributed to direct tobacco smoking mutagenesis in bladder cancer and in normal bladder epithelium.
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