Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor.

S M Ashiqul Islam, Marcos Díaz-Gay, Mark Barnes, Raviteja Vangara, Erik N Bergstrom, Yudou He, Mike Vella, Jingwei Wang, Jon W Teague, Peter Clapham, Sarah Moody, Sergey Senkin, Laura Riva, Tongwu Zhang, Andreas J Gruber, Christopher D Steele, Burçak Otlu, Azhar Khandekar, Ammal Abbasi, Laura HumphreysNatalia Syulyukina, Boian S Alexandrov, Nischalan Pillay, Jinghui Zhang, Iñigo Martincorena, David C Wedge, Maria Teresa Landi, Paul Brennan, Michael R Stratton, Steven G Rozen, Ludmil B Alexandrov

Research output: Contribution to journalArticlepeer-review


Mutational signature analysis is commonly performed in cancer genomic studies. Here, we present SigProfilerExtractor, an automated tool for de novo extraction of mutational signatures, and benchmark it against another 13 bioinformatics tools by using 34 scenarios encompassing 2,500 simulated signatures found in 60,000 synthetic genomes and 20,000 synthetic exomes. For simulations with 5% noise, reflecting high-quality datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true-positive signatures while yielding 5-fold less false-positive signatures. Applying SigProfilerExtractor to 4,643 whole-genome- and 19,184 whole-exome-sequenced cancers reveals four novel signatures. Two of the signatures are confirmed in independent cohorts, and one of these signatures is associated with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting signatures, and several novel mutational signatures, including one putatively attributed to direct tobacco smoking mutagenesis in bladder tissues.

Original languageEnglish
Pages (from-to)None
JournalCell genomics
Issue number11
Publication statusPublished - 9 Nov 2022


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