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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Detalles Bibliográficos
Autores principales: Tatsunori B Hashimoto, Matthew D Edwards, David K Gifford
Formato: article
Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2014
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Acceso en línea:https://doaj.org/article/aaa72ea7909044b3a8ea6b54b5804812
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Sumario: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.