Special CMX Seminar

Whole-genome sequencing of a large number of individuals generates hundreds of terabytes of data, requiring efficient computational methods for in-depth analysis. Mutational signature analysis is a recent computational approach for interpreting somatic mutations identified from sequencing data. To discover "signatures" (a 96-dim vector representing possible mutation types and their nucleotide context), a conventional approach utilizes non-negative matrix factorization; to match a signature to a known catalog of signatures, a non-negative least squares is typically used. I will describe some of the shortcomings of the current approaches and our solutions. Applications include detection of homologous recombination deficiency in tumor samples and prediction of response to immunotherapy.   Note: This talk will be geared toward non-biologists.