A mixture model for signature discovery from sparse mutation data

Itay Sason, Yuexi Chen, Mark D.M. Leiserson, Roded Sharan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Mutational signatures and their exposures are key to understanding the processes that shape cancer genomes with applications to diagnosis and treatment. Yet current signature discovery or refitting approaches are limited to relatively rich mutation data that comes from whole-genome or whole-exome sequencing. Recently, orders of magnitude sparser data sets from gene panel sequencing have become increasingly available in the clinical setting. Such data have typically less than 10 mutations per sample, making them challenging to deal with using current approaches. Here we suggest a novel mixture model for sparse mutation data. In application to synthetic sparse datasets and real gene panel sequences it is shown to outperform current approaches and yield mutational signatures and patient stratifications that are in higher agreement with the literature.

Original languageEnglish
Title of host publicationResearch in Computational Molecular Biology - 24th Annual International Conference, RECOMB 2020, Proceedings
EditorsRussell Schwartz
PublisherSpringer
Pages271-272
Number of pages2
ISBN (Print)9783030452568
DOIs
StatePublished - 2020
Event24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020 - Padua, Italy
Duration: 10 May 202013 May 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12074 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th Annual Conference on Research in Computational Molecular Biology, RECOMB 2020
Country/TerritoryItaly
CityPadua
Period10/05/2013/05/20

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