Generating realistic null hypothesis of cancer mutational landscapes using SigProfilerSimulator

被引：23

作者：

Bergstrom, Erik N. ^{[1
,2
]}

Barnes, Mark ^{[1
,2
]}

Martincorena, Inigo ^{[3
]}

Alexandrov, Ludmil B. ^{[1
,2
]}

机构：

[1] Univ Calif San Diego, Dept Cellular & Mol Med, La Jolla, CA 92093 USA

[2] Univ Calif San Diego, Dept Bioengn, La Jolla, CA 92093 USA

[3] Wellcome Sanger Inst, Canc Ageing & Somat Mutat, Hinxton CB10 1SA, Cambs, England

来源：

BMC BIOINFORMATICS | 2020年 / 21卷 / 01期

关键词：

Somatic mutations; Mutational patterns; Mutational signatures; SOMATIC MUTATIONS; SIGNATURES; PATTERNS;

D O I：

10.1186/s12859-020-03772-3

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

BackgroundPerforming a statistical test requires a null hypothesis. In cancer genomics, a key challenge is the fast generation of accurate somatic mutational landscapes that can be used as a realistic null hypothesis for making biological discoveries. ResultsHere we present SigProfilerSimulator, a powerful tool that is capable of simulating the mutational landscapes of thousands of cancer genomes at different resolutions within seconds. Applying SigProfilerSimulator to 2144 whole-genome sequenced cancers reveals: (i) that most doublet base substitutions are not due to two adjacent single base substitutions but likely occur as single genomic events; (ii) that an extended sequencing context of 2 bp is required to more completely capture the patterns of substitution mutational signatures in human cancer; (iii) information on false-positive discovery rate of commonly used bioinformatics tools for detecting driver genes.Conclusions SigProfilerSimulator's breadth of features allows one to construct a tailored null hypothesis and use it for evaluating the accuracy of other bioinformatics tools or for downstream statistical analysis for biological discoveries. SigProfilerSimulator is freely available at https://github.com/AlexandrovLab/SigProfilerSimulator with an extensive documentation at https://osf.io/usxjz/wiki/home/.

引用

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