Decomposition of mutational context signatures using quadratic programming methods [version 1; referees: 1 approved, 1 approved with reservations]
Published version
Peer-reviewed
Repository URI
Repository DOI
Type
Article
Change log
Authors
Lynch, AG
Abstract
Methods:
for inferring signatures of mutational contexts from large cancer sequencing data sets are invaluable for biological research, but impractical for clinical application where we require tools that decompose the context data for an individual into signatures. One such method has recently been published using an iterative linear modelling approach. A natural alternative places the problem within a quadratic programming framework and is presented here, where it is seen to offer advantages of speed and accuracy.Description
Keywords
31 Biological Sciences, 3102 Bioinformatics and Computational Biology, 42 Health Sciences, Human Genome, Genetics, Cancer, Cancer
Journal Title
F1000Research
Conference Name
Journal ISSN
2046-1402
1759-796X
1759-796X
Volume Title
5
Publisher
F1000 Research Ltd
Publisher DOI
Rights
Sponsorship
AGL was supported in this work by a Cancer Research UK programme grant [C14303/A20406] to Simon Tavaré. AGL acknowledges the support of the University of Cambridge, Cancer Research UK and Hutchison Whampoa Limited. Whole-genome sequencing of oesophageal adenocarcinoma was part of the oesophageal International Cancer Genome Consortium (ICGC) project. The oesophageal ICGC project was funded through a programme and infrastructure grant to Rebecca Fitzgerald as part of the OCCAMS collaboration.