ISOWN: accurate somatic mutation identification in the absence of normal tissue controls

• Published in: Genome medicine Vol. 9; no. 1; p. 59
• Main Authors: Kalatskaya, Irina, Trinh, Quang M, Spears, Melanie, McPherson, John D, Bartlett, John M S, Stein, Lincoln
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 29.06.2017; BioMed Central; BMC
• Subjects: Artificial intelligence; Breast cancer; Cancer; Clinical trials; Datasets; DNA Mutational Analysis - methods; Formaldehyde; Gene mutations; Genetic aspects; Genetic polymorphisms; Genomes; High-Throughput Nucleotide Sequencing - methods; Humans; Learning algorithms; Matching normal tissue; Mutation; Neoplasms - genetics; Next-generation sequencing; Nucleotide sequence; Paraffin; Software; Somatic mutation; Studies; Supervised Machine Learning; Tumors; Variant classification; Somatic mutation; Variant classification; Matching normal tissue; Next-generation sequencing

A key step in cancer genome analysis is the identification of somatic mutations in the tumor. This is typically done by comparing the genome of the tumor to the reference genome sequence derived from a normal tissue taken from the same donor. However, there are a variety of common scenarios in which...