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Brief Bioinform. 2013 Nov;14(6):671-83. doi: 10.1093/bib/bbs046. Epub 2012 Sep 17.

A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis.

Author information

1
Institut Pasteur, PF2 Plate-forme Transcriptome et Epigénome, 28 rue du Dr Roux, Paris CEDEX 15, F-75724 France. Tel.: +33 (0) 145688651; Fax: +33 (0) 145688406; marie-agnes.dillies@pasteur.fr.

Abstract

During the last 3 years, a number of approaches for the normalization of RNA sequencing data have emerged in the literature, differing both in the type of bias adjustment and in the statistical strategy adopted. However, as data continue to accumulate, there has been no clear consensus on the appropriate normalization method to be used or the impact of a chosen method on the downstream analysis. In this work, we focus on a comprehensive comparison of seven recently proposed normalization methods for the differential analysis of RNA-seq data, with an emphasis on the use of varied real and simulated datasets involving different species and experimental designs to represent data characteristics commonly observed in practice. Based on this comparison study, we propose practical recommendations on the appropriate normalization method to be used and its impact on the differential analysis of RNA-seq data.

KEYWORDS:

RNA-seq; differential analysis; high-throughput sequencing; normalization

PMID:
22988256
DOI:
10.1093/bib/bbs046
[Indexed for MEDLINE]

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