GC-content normalization for RNA-Seq data.
GC-content normalization for RNA-Seq data.
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DOI：
10.1186/1471-2105-12-480
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发表时间：
2011-12-17
影响因子：
3
通讯作者：
Dudoit S
中科院分区：
文献类型：
--
作者：
Risso D;Schwartz K;Sherlock G;Dudoit S
转录组测序 (RNA-Seq) 已成为基因表达高通量研究的首选检测方法。然而，与微阵列的情况一样，主要的技术相关伪影和偏差会影响最终的表达测量。因此，标准化对于确保准确推断表达水平及其后续分析至关重要。我们重点关注与 GC 含量相关的偏差，并证明样本特异性 GC 含量对 RNA-Seq 读数计数存在强烈影响，这可能会严重影响差异表达分析。我们提出了三种简单的泳道内基因级 GC 含量标准化方法，并评估了它们在涉及不同物种和实验设计的两个不同 RNA-Seq 数据集上的性能。我们的方法与最先进的标准化程序在表达倍数变化估计的偏差和均方误差以及差异表达测试的 I 型误差和 p 值分布方面进行了比较。本文提出的探索性数据分析和标准化方法在开源 Bioconductor R 包 EDASeq 中实现。我们的泳道内标准化程序以及泳道间标准化可减少 GC 含量偏差，从而更准确地估计表达倍数变化和差异表达测试。这些结果对于 RNA-Seq 实验的生物学解释至关重要，其中下游分析可能对提供的基因列表敏感。
Transcriptome sequencing (RNA-Seq) has become the assay of choice for high-throughput studies of gene expression. However, as is the case with microarrays, major technology-related artifacts and biases affect the resulting expression measures. Normalization is therefore essential to ensure accurate inference of expression levels and subsequent analyses thereof. We focus on biases related to GC-content and demonstrate the existence of strong sample-specific GC-content effects on RNA-Seq read counts, which can substantially bias differential expression analysis. We propose three simple within-lane gene-level GC-content normalization approaches and assess their performance on two different RNA-Seq datasets, involving different species and experimental designs. Our methods are compared to state-of-the-art normalization procedures in terms of bias and mean squared error for expression fold-change estimation and in terms of Type I error and p-value distributions for tests of differential expression. The exploratory data analysis and normalization methods proposed in this article are implemented in the open-source Bioconductor R package EDASeq. Our within-lane normalization procedures, followed by between-lane normalization, reduce GC-content bias and lead to more accurate estimates of expression fold-changes and tests of differential expression. Such results are crucial for the biological interpretation of RNA-Seq experiments, where downstream analyses can be sensitive to the supplied lists of genes.
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影响因子：
9.2
作者：
Maniar, Jay M.;Fire, Andrew Z.
通讯作者：
Fire, Andrew Z.
影响因子：
14.9
作者：
Hansen KD;Brenner SE;Dudoit S
通讯作者：
Dudoit S
影响因子：
12.3
作者：
Robinson MD;Oshlack A
通讯作者：
Oshlack A
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
3.7
作者：
CLEVELAND, WS;DEVLIN, SJ
通讯作者：
DEVLIN, SJ