GitHub - PoisonAlien/maftools: Summarize, Analyze and Visualize MAF files from TCGA or in-house studies. (original) (raw)

maftools - An R package to summarize, analyze and visualize MAF files

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Introduction

maftools is a comprehensive toolkit for processing somatic variants from cohort-based cancer genomic studies. maftools offers over 80 functions to perform the most commonly required tasks in cancer genomics, using MAF as the only input file type.

Installation

#Install from Bioconductor repository
BiocManager::install("maftools")

#Install from GitHub repository
BiocManager::install("PoisonAlien/maftools")

Getting started: Vignette and a case study

A complete documentation of maftools using TCGA LAML as a case study can be found here.

Primary applications

maftools is extremely easy to use, starting with importing an MAF file along with the associated clinical data. Once the data is successfully imported, the resulting MAF object can be passed to various functions. Key applications include:

Besides the MAF files, maftools can handle sequencing alignment BAM files, copy number output from GISTIC and mosdepth. Please refer to the package documentation sections below to learn more.

Moreover, analyzing all 33 TCGA cohorts along with the harmonized clinical data is a breeze.

Citation

Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP. 2018. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Research. PMID: 30341162

File Fomats Data portals Annotation tools
Mutation Annotation Format TCGA vcf2maf - for converting your VCF files to MAF
Variant Call Format ICGC annovar2maf - for converting annovar output files to MAF
ICGC Simple Somatic Mutation Format Broad Firehose bcftools csq - Rapid annotations of VCF files with variant consequences
cBioPortal Annovar
PeCan Funcotator
CIViC - Clinical interpretation of variants in cancer
DGIdb - Information on drug-gene interactions and the druggable genome

Useful packages/tools

Below are some more useful software packages for somatic variant analysis


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