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Cited by 365

The DOI matches its Crossref record; 318 of the 318 references it cites were found in a registry or an index.

Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor

Open access
Islam, S.M. Ashiqul, Díaz-Gay, Marcos, Wu, Yang, Barnes, Mark, Vangara, Raviteja, Bergstrom, Erik N., He, Yudou, Vella, Mike, Wang, Jingwei, Teague, Jon W., Clapham, Peter, Moody, Sarah, Senkin, Sergey, Li, Yun Rose, Riva, Laura, Zhang, Tongwu, Gruber, Andreas J., Steele, Christopher D., Otlu, Burçak, Khandekar, Azhar, Abbasi, Ammal, Humphreys, Laura, Syulyukina, Natalia, Brady, Samuel W., Alexandrov, Boian S., Pillay, Nischalan, Zhang, Jinghui, Adams, David J., Martincorena, Iñigo, Wedge, David C., Landi, Maria Teresa, Brennan, Paul, Stratton, Michael R., Rozen, Steven G., Alexandrov, Ludmil B.·2022·Cell Genomics
DOI 10.1016/j.xgen.2022.100179

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Abstract

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.

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Research works printed but not linked: 10

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Metadata

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, A. 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, D. C., Landi, M. T., Brennan, P., Stratton, M. R., Rozen, S. G., & Alexandrov, L. B.
Venue
Cell Genomics
Publisher
Elsevier BV
Published
2022-11
Volume
2
Issue
11
Pages
100179
ISSN
2666-979X (ISSN-L)
DOI
10.1016/j.xgen.2022.100179
OpenAlex
W4296819410
Cited by
365
References
318
FWCI
25.19
Type
journal article
Language
en
Open access
gold
Licence
Funders
  • Helsingin Yliopisto
  • Los Alamos National Laboratory (20190020DR, 89233218CNA000001)
  • Wellcome Trust (206194)
  • Laboratory Directed Research and Development
  • Nvidia (63/269,033, 63/289,601, 63/366,392, 63/367,846)
  • CRUK (C57387/A21777, C98/A24032, P2BSP3_178591)
  • National Cancer Institute
  • Alfred P. Sloan Foundation
  • National Nuclear Security Administration
  • Cancer Research UK
  • National Medical Research Council (MOH-000032/MOHCIRG18may-0004, NMRC/CIRG/1422/2015)
  • National Institutes of Health (R01CA269919, R01ES030993-01A1, R01ES032547, R01MH116281-01A1)
  • Ministry of Health -Singapore
  • U.S. Department of Energy

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CoChat. (2026). Citation check: Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor. https://app.cochat.ai/papers/10.1016/j.xgen.2022.100179
The paper
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, A. J., Steele, C. D., Otlu, B., . . . Alexandrov, L. B. (2022). Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor. Cell Genomics. https://doi.org/10.1016/j.xgen.2022.100179

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.