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dc.contributor.authorLynch, Andy G.
dc.date.accessioned2017-08-15T14:30:08Z
dc.date.available2017-08-15T14:30:08Z
dc.date.issued2016-06-07
dc.identifier.citationLynch , A G 2016 , ' Decomposition of mutational context signatures using quadratic programming methods ' F1000Research , vol. 5 , 1253 . https://doi.org/10.12688/F1000RESEARCH.8918.1en
dc.identifier.issn2046-1402
dc.identifier.otherPURE: 250827830
dc.identifier.otherPURE UUID: d8f75d2b-a599-4d7b-a78e-af93232b95ba
dc.identifier.otherScopus: 85011295562
dc.identifier.otherORCID: /0000-0002-7876-7338/work/36106406
dc.identifier.urihttp://hdl.handle.net/10023/11479
dc.descriptionAGL 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.en
dc.description.abstractMethods 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.
dc.language.isoeng
dc.relation.ispartofF1000Researchen
dc.rights© 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).en
dc.subjectRC0254 Neoplasms. Tumors. Oncology (including Cancer)en
dc.subjectQH426 Geneticsen
dc.subjectMedicine(all)en
dc.subjectImmunology and Microbiology(all)en
dc.subjectBiochemistry, Genetics and Molecular Biology(all)en
dc.subjectPharmacology, Toxicology and Pharmaceutics(all)en
dc.subjectDASen
dc.subject.lccRC0254en
dc.subject.lccQH426en
dc.titleDecomposition of mutational context signatures using quadratic programming methodsen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Medicineen
dc.contributor.institutionUniversity of St Andrews.Statisticsen
dc.identifier.doihttps://doi.org/10.12688/F1000RESEARCH.8918.1
dc.description.statusPeer revieweden


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