Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor
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Authors
Islam, S. M. A.Díaz-Gay, M.
Wu, Y.
Barnes, M.
Vangara, R.
Bergstrom, E. N.
He, Y.
Vella, M.
Wang, J.
Teague, J. W.
Clapham, P.
Moody, S.
Senkin, S.
Li, Y. R.
Riva, L.
Zhang, T.
Gruber, Andreas J
Steele, C. D.
Otlu, B.
Khandekar, A.
Abbasi, A.
Humphreys, L.
Syulyukina, N.
Brady, S. W.
Alexandrov, B. S.
Pillay, N.
Zhang, J.
Adams, D. J.
Martincorena, I.
Wedge, David C
Landi, M. T.
Brennan, P.
Stratton, M. R.
Rozen, S. G.
Alexandrov, L. B.
Affiliation
Department of Cellular and Molecular Medicine, UC San Diego, La Jolla, CA 92093, USAIssue Date
2022
Metadata
Show full item recordAbstract
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.Citation
Islam SMA, Díaz-Gay M, Wu Y, Barnes M, Vangara R, Bergstrom EN, et al. Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor. Cell genomics. 2022 Nov 9;2(11):None. PubMed PMID: 36388765. Pubmed Central PMCID: PMC9646490. Epub 2022/11/18. eng.Journal
Cell GenomicsDOI
10.1016/j.xgen.2022.100179PubMed ID
36388765Additional Links
https://dx.doi.org/10.1016/j.xgen.2022.100179Type
ArticleLanguage
enae974a485f413a2113503eed53cd6c53
10.1016/j.xgen.2022.100179