The decreasing cost of sequencing has moved the focus of cancer genomics beyond single genome studies to the analysis of tens or hundreds of patients diagnosed with similar cancers. Besides the routine discovery and validation of SNVs, indels, and SVs in individual genomes, it is now paramount to systematically analyze the function and recurrence of mutations across a cohort, and to describe how they interact with one other and with the associated clinical data. To this end we have developed the Mutational Significance In Cancer package (MuSiC). It consists of a suite of downstream analysis tools designed to (1) apply statistical methods to identify significantly mutated genes, (2) highlight significantly altered pathways, (3) investigate the proximity of amino acid mutations in the same gene, (4) search for gene-based or site-based correlations to mutations and relationships between mutations themselves, (5) correlate mutations to clinical features, and (6) cross-reference findings with relevant databases such as Pfam, COSMIC, and OMIM.
