Maftools: efficient and comprehensive analysis of somatic variants in cancer

By A. Mayakonda, D. Lin, Yassen Assenov, C. Plass, H. Koeffler. Genome Research, 2018

4,023 citations recorded by the source corpus.

Numerous large-scale genomic studies of matched tumor-normal samples have established the somatic landscapes of most cancer types. However, the downstream analysis of data from somatic mutations entails a number of computational and statistical approaches, requiring usage of independent software and numerous tools. Here, we describe an R Bioconductor package, Maftools, which offers a multitude of analysis and visualization modules that are commonly used in cancer genomic studies, including driver gene identification, pathway, signature, enrichment, and association analyses. Maftools only requires somatic variants in Mutation Annotation Format (MAF) and is independent of larger alignment files. With the implementation of well-established statistical and computational methods, Maftools facilitates data-driven research and comparative analysis to discover novel results from publicly available data sets. In the present study, using three of the well-annotated cohorts from The Cancer Genome Atlas (TCGA), we describe the application of Maftools to reproduce known results. More importantly, we show that Maftools can also be used to uncover novel findings through integrative analysis.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/03adc4840ed3ff3647369c691e2765241dfb5ae1

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In Molecular Biology

Citations in the corpus

IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures (2020, methodology): “Genome data in Mutation Annotation Format (MAF) format (20) downloaded from the University of California, Santa Cruz (UCSC) website, or user-construct mutation matrices are acceptable as an input to find mutations related to specific signatures.”