Maftools: Efficient and Comprehensive Analysis of Somatic Variants in Cancer

Overview

Journal Genome Res

Specialty Genetics

Date 2018 Oct 21

PMID 30341162

Citations 2356

Authors

Anand Mayakonda

De-Chen Lin

Yassen Assenov

Christoph Plass

H Phillip Koeffler

Affiliations

Soon will be listed here.

Abstract

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.

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