Genome Gerrymandering: optimal division of the genome into regions with cancer type specific differences in mutation rates

The activity of mutational processes differs across the genome, and is influenced by chromatin state and spatial genome organization. At the scale of one megabase-pair (Mb), regional mutation density correlate strongly with chromatin features and mutation density at this scale can be used to accurat...

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Bibliographic Details
Published inPacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Vol. 25; p. 274
Main Authors Young, Adamo, Chmura, Jacob, Park, Yoonsik, Morris, Quaid, Atwal, Gurnit
Format Journal Article
LanguageEnglish
Published United States 2020
Subjects
Computational Biology
Genomics
Humans
Mutation
Mutation Rate
Neoplasms - genetics
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Summary:The activity of mutational processes differs across the genome, and is influenced by chromatin state and spatial genome organization. At the scale of one megabase-pair (Mb), regional mutation density correlate strongly with chromatin features and mutation density at this scale can be used to accurately identify cancer type. Here, we explore the relationship between genomic region and mutation rate by developing an information theory driven, dynamic programming algorithm for dividing the genome into regions with differing relative mutation rates between cancer types. Our algorithm improves mutual information when compared to the naive approach, effectively reducing the average number of mutations required to identify cancer type. Our approach provides an efficient method for associating regional mutation density with mutation labels, and has future applications in exploring the role of somatic mutations in a number of diseases.
ISSN:2335-6936