A Simple Model-Based Approach to Inferring and Visualizing Cancer Mutation Signatures.

ABSTRACT: Recent advances in sequencing technologies have enabled the production of massive amounts of data on somatic mutations from cancer genomes. These data have led to the detection of characteristic patterns of somatic mutations or "mutation signatures" at an unprecedented resolution, with the potential for new insights into the causes and mechanisms of tumorigenesis. Here we present new methods for modelling, identifying and visualizing such mutation signatures. Our methods greatly simplify mutation signature models compared with existing approaches, reducing the number of parameters by orders of magnitude even while increasing the contextual factors (e.g. the number of flanking bases) that are accounted for. This improves both sensitivity and robustness of inferred signatures. We also provid

SUBMITTER: Shiraishi Y 

PROVIDER: S-EPMC4667891 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature