Using sigLASSO to optimize cancer mutation signatures jointly with sampling likelihood

The next generation sequencing has provided the opportunity to look for signatures of carcinogenesis on a genome wide scale. Here, the authors develop the algorithm, sigLASSO, that provides confidence in assigning mutational signatures when the mutation count is low and the samples used are variable...

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Detalles Bibliográficos
Autores principales:	Shantao Li, Forrest W. Crawford, Mark B. Gerstein
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/d51810b840e24277acd8a315057999a1
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spelling	oai:doaj.org-article:d51810b840e24277acd8a315057999a12021-12-02T15:33:17ZUsing sigLASSO to optimize cancer mutation signatures jointly with sampling likelihood10.1038/s41467-020-17388-x2041-1723https://doaj.org/article/d51810b840e24277acd8a315057999a12020-07-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-17388-xhttps://doaj.org/toc/2041-1723The next generation sequencing has provided the opportunity to look for signatures of carcinogenesis on a genome wide scale. Here, the authors develop the algorithm, sigLASSO, that provides confidence in assigning mutational signatures when the mutation count is low and the samples used are variable.Shantao LiForrest W. CrawfordMark B. GersteinNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Shantao Li Forrest W. Crawford Mark B. Gerstein Using sigLASSO to optimize cancer mutation signatures jointly with sampling likelihood
description	The next generation sequencing has provided the opportunity to look for signatures of carcinogenesis on a genome wide scale. Here, the authors develop the algorithm, sigLASSO, that provides confidence in assigning mutational signatures when the mutation count is low and the samples used are variable.
format	article
author	Shantao Li Forrest W. Crawford Mark B. Gerstein
author_facet	Shantao Li Forrest W. Crawford Mark B. Gerstein
author_sort	Shantao Li
title	Using sigLASSO to optimize cancer mutation signatures jointly with sampling likelihood
title_short	Using sigLASSO to optimize cancer mutation signatures jointly with sampling likelihood
title_full	Using sigLASSO to optimize cancer mutation signatures jointly with sampling likelihood
title_fullStr	Using sigLASSO to optimize cancer mutation signatures jointly with sampling likelihood
title_full_unstemmed	Using sigLASSO to optimize cancer mutation signatures jointly with sampling likelihood
title_sort	using siglasso to optimize cancer mutation signatures jointly with sampling likelihood
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/d51810b840e24277acd8a315057999a1
work_keys_str_mv	AT shantaoli usingsiglassotooptimizecancermutationsignaturesjointlywithsamplinglikelihood AT forrestwcrawford usingsiglassotooptimizecancermutationsignaturesjointlywithsamplinglikelihood AT markbgerstein usingsiglassotooptimizecancermutationsignaturesjointlywithsamplinglikelihood
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Using sigLASSO to optimize cancer mutation signatures jointly with sampling likelihood

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