MutationalPatterns: the one stop shop for the analysis of mutational processes

Abstract

Background The collective of somatic mutations in a genome represents a record of mutational processes that have been operative in a cell. These processes can be investigated by extracting relevant mutational patterns from sequencing data. Results Here, we present the next version of MutationalPatterns, an R/Bioconductor package, which allows in-depth mutational analysis of catalogues of single and double base substitutions as well as small insertions and deletions. Major features of the package include the possibility to perform regional mutation spectra analyses and the possibility to detect strand asymmetry phenomena, such as lesion segregation. On top of this, the package also contains functions to determine how likely it is that a signature can cause damaging mutations (i.e., mutations that affect protein function). This updated package supports stricter signature refitting on known signatures in order to prevent overfitting. Using simulated mutation matrices containing varied signature contributions, we showed that reliable refitting can be achieved even when only 50 mutations are present per signature. Additionally, we incorporated bootstrapped signature refitting to assess the robustness of the signature analyses. Finally, we applied the package on genome mutation data of cell lines in which we deleted specific DNA repair processes and on large cancer datasets, to show how the package can be used to generate novel biological insights. Conclusions This novel version of MutationalPatterns allows for more comprehensive analyses and visualization of mutational patterns in order to study the underlying processes. Ultimately, in-depth mutational analyses may contribute to improved biological insights in mechanisms of mutation accumulation as well as aid cancer diagnostics. MutationalPatterns is freely available at http://bioconductor.org/packages/MutationalPatterns.

Duplex Sequencing Provides Detailed Characterization of Mutation Frequencies and Spectra in the Bone Marrow of MutaMouse Males Exposed to Procarbazine Hydrochloride

“…Pairwise comparisons as described above were used to determine which mutation types were significantly increased from VC. Subsequently, we generated a 96-trinucleotide mutation spectrum and used cosine similarity to determine the Catalogue of Somatic Mutations in Cancer (COSMIC) single base substitutions (SBS) signatures (Alexandrov et al 2020) that most closely resembled the PRC mutation spectra, using the MutationalPatterns package in R (Manders et al 2022).…”

Spontaneous Mutation Rates and Spectra of Respiratory-Deficient Yeast

“…All SNVs and INDELs that passed these filters were visually checked in the Integrative Genomics Viewer (IGV) [ 21 ] and ambiguous ones were further discarded. The SNV mutational signatures were analyzed and fit to the COSMIC signatures using the R/Bioconductor MutationalPatterns package [ 22 ], in which a strict refitting function “fit_to_signatures_strict” and a cutoff of 0.004 were applied. Of note, we identified a potential cross-contamination between two replicates (replicate 1 and replicate 3) of the C+ strain MALs based on finding a high proportion of shared mutations.…”

Somatic mutations reveal clonal cell populations in atherosclerotic plaques

“…Mutational signatures analysis was performed using the Mutational Patterns R packages (v3.6.0) 22 . Due to the limited number of mutations across all samples ( n = 367), mutations were pooled to increase analytical power.…”