RNA-Seq: Differential expression analysis for sequence count data.

[arthurmelo]

Hi protist, wanderfull and recent work.
It`s very interesting the SEQanswers people share good publication/papers in NGS approach.

Thanks

[protist]

Brief Bioinform. 2012 Sep 17. [Epub ahead of print]
A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis.
Dillies MA, Rau A, Aubert J, Hennequet-Antier C, Jeanmougin M, Servant N, Keime C, Marot G, Castel D, Estelle J, Guernec G, Jagla B, Jouneau L, Laloë D, Le Gall C, Schaëffer B, Le Crom S, Guedj M, Jaffrézic F; on behalf of The French StatOmique Consortium.
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.

PMID:22988256

[arthurmelo]

See this paper too:

Differential gene and transcript expression analysis of RNA-Seq experiments with TopHat and Cufflinks.

Trapnell et al. (2012) Nature protocols, 7:562-578

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Differential expression analysis for sequence count data.

Genome Biol. 2010 Oct 27;11(10):R106

Authors: Anders S, Huber W

ABSTRACT: High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer diferential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.

PMID: 20979621 [PubMed - as supplied by publisher]



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