Universal count correction for high-throughput sequencing.
Universal count correction for high-throughput sequencing.
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DOI：
10.1371/journal.pcbi.1003494
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发表时间：
2014-03
影响因子：
4.3
通讯作者：
Gifford DK
中科院分区：
文献类型：
--
作者：
Hashimoto TB;Edwards MD;Gifford DK
我们表明，现有的RNA-SEQ、DNASE-SEQ和CHIP-SEQ数据表现出过度分散的按碱基的读取计数分布，其与现有的计算方法假设不匹配。为了补偿这种过度分散，我们引入了一种非参数且通用的方法来处理每个碱基的测序读取计数数据，称为FIXSEQ。我们证明，与现有替代方案相比，Fixseq显著提高了现有RNA-SEQ、DNase-SEQ和CHIP-SEQ分析工具的性能。高通量DNA测序已被用于测量不同的生物状态信息，包括RNA表达、染色质可获得性和转录因子与基因组的结合。从序列计数中准确地推断生物学机制需要一个序列计数如何分布的模型。我们发现目前使用的顺序计数分布模型通常是不准确的，并提出了一种称为Fixseq的新方法来处理计数，以更接近于现有的计数模型。在典型的数据集上，Fixseq改进了用于RNA-seq、DNase-seq和Chip-seq的现有工具的性能，同时在特定于领域的工具可用的情况下产生了补充的额外收益。
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. High-throughput DNA sequencing has been adapted to measure diverse biological state information including RNA expression, chromatin accessibility, and transcription factor binding to the genome. The accurate inference of biological mechanism from sequence counts requires a model of how sequence counts are distributed. We show that presently used sequence count distribution models are typically inaccurate and present a new method called Fixseq to process counts to more closely follow existing count models. On typical datasets Fixseq improves the performance of existing tools for RNA-seq, DNase-seq, and ChIP-seq, while yielding complementary additional gains in cases where domain-specific tools are available.
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影响因子：
46.9
作者：
Ji, Hongkai;Jiang, Hui;Ma, Wenxiu;Johnson, David S.;Myers, Richard M.;Wong, Wing H.
通讯作者：
Wong, Wing H.
影响因子：
14.9
作者：
Hansen KD;Brenner SE;Dudoit S
通讯作者：
Dudoit S
影响因子：
5.8
作者：
Li, Wei;Jiang, Tao
通讯作者：
Jiang, Tao
DOI：
10.1093/bioinformatics/bts055
发表时间：
2012-04-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Jones DC;Ruzzo WL;Peng X;Katze MG
通讯作者：
Katze MG
影响因子：
4.3
作者：
Guo Y;Mahony S;Gifford DK
通讯作者：
Gifford DK