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Generating realistic null hypothesis of cancer mutational landscapes using SigProfilerSimulator
Erik N Bergstrom1,2, Mark Barnes1,2, Iñigo Martincorena3
1Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, 92093, USA.
BMC Bioinformatics
|October 8, 2020
Summary
SigProfilerSimulator rapidly generates realistic null hypotheses for cancer genomics research. This tool aids in discovering biological insights and evaluating bioinformatics tools by simulating somatic mutational landscapes.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Statistical testing in cancer genomics necessitates a robust null hypothesis.
- Generating accurate somatic mutational landscapes is crucial for biological discovery.
Purpose of the Study:
- To introduce SigProfilerSimulator, a tool for simulating cancer genome mutational landscapes.
- To provide a realistic null hypothesis for cancer genomics research.
Main Methods:
- SigProfilerSimulator simulates thousands of cancer genomes at various resolutions rapidly.
- The tool was applied to 2144 whole-genome sequenced cancer samples.
Main Results:
- Most doublet base substitutions appear to be single genomic events, not adjacent substitutions.
- An extended sequencing context (±2 bp) is necessary for comprehensive mutational signature analysis.
- The study provides insights into the false-positive rates of driver gene detection tools.
Conclusions:
- SigProfilerSimulator enables the creation of tailored null hypotheses for bioinformatics tool evaluation and statistical analysis.
- The tool facilitates biological discoveries by providing accurate simulated mutational data.
- SigProfilerSimulator is freely accessible with comprehensive documentation.
