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RNA-seq 数据差异表达分析的缩放标准化方法。

A scaling normalization method for differential expression analysis of RNA-seq data.

机构信息

Bioinformatics Division, Walter and Eliza Hall Institute, 1G Royal Parade, Parkville, Australia.

出版信息

Genome Biol. 2010;11(3):R25. doi: 10.1186/gb-2010-11-3-r25. Epub 2010 Mar 2.

DOI:10.1186/gb-2010-11-3-r25
PMID:20196867
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2864565/
Abstract

The fine detail provided by sequencing-based transcriptome surveys suggests that RNA-seq is likely to become the platform of choice for interrogating steady state RNA. In order to discover biologically important changes in expression, we show that normalization continues to be an essential step in the analysis. We outline a simple and effective method for performing normalization and show dramatically improved results for inferring differential expression in simulated and publicly available data sets.

摘要

基于测序的转录组调查提供的详细信息表明，RNA-seq 可能成为研究稳态 RNA 的首选平台。为了发现表达中具有生物学重要意义的变化，我们表明标准化仍然是分析中的一个重要步骤。我们概述了一种简单有效的标准化方法，并在模拟和公开可用的数据集上展示了显著改进的推断差异表达的结果。

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本文引用的文献

[1]
edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.

Bioinformatics. 2009-11-11

[2]
Methods for analyzing deep sequencing expression data: constructing the human and mouse promoterome with deepCAGE data.

Genome Biol. 2009-7-22

[3]
Transcript length bias in RNA-seq data confounds systems biology.

Biol Direct. 2009-4-16

[4]
PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls.

Nat Biotechnol. 2009-1

[5]
Determination of tag density required for digital transcriptome analysis: application to an androgen-sensitive prostate cancer model.

Proc Natl Acad Sci U S A. 2008-12-23

[6]
Transcriptome-wide identification of novel imprinted genes in neonatal mouse brain.

PLoS One. 2008

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RNA-Seq: a revolutionary tool for transcriptomics.

Nat Rev Genet. 2009-1

[8]
Alternative isoform regulation in human tissue transcriptomes.

Nature. 2008-11-27

[9]
Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab portability over five microarray platforms.

Nucleic Acids Res. 2008-12

[10]
In-depth characterization of the microRNA transcriptome in a leukemia progression model.

Genome Res. 2008-11

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