Decomposition of mutational context signatures using quadratic programming methods

Andy G. Lynch

doi:10.12688/F1000RESEARCH.8918.1

Decomposition of mutational context signatures using quadratic programming methods

Andy G. Lynch^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	1253
Journal	F1000Research
Volume	5
DOIs	https://doi.org/10.12688/F1000RESEARCH.8918.1
Publication status	Published - 7 Jun 2016

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10.12688/F1000RESEARCH.8918.1Licence: CC BY

Lynch_2016_FR1000Res_ContextSignatures_CC
© 2016 Lynch AG. This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).
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note = "AGL was supported in this work by a Cancer Research UK programme grant [C14303/A20406] to Simon Tavar{\'e}. 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. ",

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N2 - 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.

AB - 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.

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Decomposition of mutational context signatures using quadratic programming methods

Abstract

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