Universal count correction for high-throughput sequencing.

We show that existing RNA-seq, DNase-seq, and ChIP-seq data exhibit overdispersed per-base read count distributions that are not matched to existing computational method assumptions. To compensate for this overdispersion we introduce a nonparametric and universal method for processing per-base seque...

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Autores principales:	Tatsunori B Hashimoto, Matthew D Edwards, David K Gifford
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/aaa72ea7909044b3a8ea6b54b5804812
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spelling	oai:doaj.org-article:aaa72ea7909044b3a8ea6b54b58048122021-11-18T05:53:06ZUniversal count correction for high-throughput sequencing.1553-734X1553-735810.1371/journal.pcbi.1003494https://doaj.org/article/aaa72ea7909044b3a8ea6b54b58048122014-03-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24603409/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358We show that existing RNA-seq, DNase-seq, and ChIP-seq data exhibit overdispersed per-base read count distributions that are not matched to existing computational method assumptions. To compensate for this overdispersion we introduce a nonparametric and universal method for processing per-base sequencing read count data called FIXSEQ. We demonstrate that FIXSEQ substantially improves the performance of existing RNA-seq, DNase-seq, and ChIP-seq analysis tools when compared with existing alternatives.Tatsunori B HashimotoMatthew D EdwardsDavid K GiffordPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 10, Iss 3, p e1003494 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Tatsunori B Hashimoto Matthew D Edwards David K Gifford Universal count correction for high-throughput sequencing.
description	We show that existing RNA-seq, DNase-seq, and ChIP-seq data exhibit overdispersed per-base read count distributions that are not matched to existing computational method assumptions. To compensate for this overdispersion we introduce a nonparametric and universal method for processing per-base sequencing read count data called FIXSEQ. We demonstrate that FIXSEQ substantially improves the performance of existing RNA-seq, DNase-seq, and ChIP-seq analysis tools when compared with existing alternatives.
format	article
author	Tatsunori B Hashimoto Matthew D Edwards David K Gifford
author_facet	Tatsunori B Hashimoto Matthew D Edwards David K Gifford
author_sort	Tatsunori B Hashimoto
title	Universal count correction for high-throughput sequencing.
title_short	Universal count correction for high-throughput sequencing.
title_full	Universal count correction for high-throughput sequencing.
title_fullStr	Universal count correction for high-throughput sequencing.
title_full_unstemmed	Universal count correction for high-throughput sequencing.
title_sort	universal count correction for high-throughput sequencing.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/aaa72ea7909044b3a8ea6b54b5804812
work_keys_str_mv	AT tatsunoribhashimoto universalcountcorrectionforhighthroughputsequencing AT matthewdedwards universalcountcorrectionforhighthroughputsequencing AT davidkgifford universalcountcorrectionforhighthroughputsequencing
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Universal count correction for high-throughput sequencing.

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