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bioRxiv · 10.1101/2020.02.13.948422

Generating realistic null hypothesis of cancer mutational landscapes using SigProfilerSimulator

Bergstrom, E. N.·Barnes, M.·Martincorena, I.·Alexandrov, L. B.
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Abstract

Performing 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. Here 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 2,144 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 +/-2bp 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. SigProfilerSimulators 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.

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Bergstrom, E. N., Barnes, M., Martincorena, I., Alexandrov, L. B.. 2020-02-14. Generating realistic null hypothesis of cancer mutational landscapes using SigProfilerSimulator. https://doi.org/10.1101/2020.02.13.948422

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