Decomposition of mutational context signatures using quadratic programming methods [version 1; referees: awaiting peer review]
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Lynch, A. (2016). Decomposition of mutational context signatures using quadratic programming methods [version 1; referees: awaiting peer review]. F1000Research, 5 (1253)https://doi.org/10.12688/f1000research.8918.1
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.
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.
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External DOI: https://doi.org/10.12688/f1000research.8918.1
This record's URL: https://www.repository.cam.ac.uk/handle/1810/257266
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Licence URL: http://creativecommons.org/publicdomain/zero/1.0/
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