A pan-cancer landscape of somatic mutations in non-unique regions of the human genome.

ABSTRACT: A substantial fraction of the human genome displays high sequence similarity with at least one other genomic sequence, posing a challenge for the identification of somatic mutations from short-read sequencing data. Here we annotate genomic variants in 2,658 cancers from the Pan-Cancer Analysis of Whole Genomes (PCAWG) cohort with links to similar sites across the human genome. We train a machine learning model to use signals distributed over multiple genomic sites to call somatic events in non-unique regions and validate the data against linked-read sequencing in an independent dataset. Using this approach, we uncover previously hidden mutations in ~1,700 coding sequences and in thousands of regulatory elements, including in known cancer genes, immunoglobulins and highly mutated gene famil

SUBMITTER: Tarabichi M 

PROVIDER: S-EPMC7612106 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature