Differential expression in RNA-seq: a matter of depth.

TitleDifferential expression in RNA-seq: a matter of depth.
Publication TypeJournal Article
Year of Publication2011
AuthorsTarazona, S, García-Alcalde, F, Dopazo, J, Ferrer, A, Conesa, A
JournalGenome Res
Volume21
Issue12
Pagination2213-23
Date Published2011 Dec
ISSN1549-5469
KeywordsAlgorithms; Expressed Sequence Tags; Gene Expression Profiling; Gene Expression Regulation; Humans; Models, Genetic; Oligonucleotide Array Sequence Analysis
Abstract

Next-generation sequencing (NGS) technologies are revolutionizing genome research, and in particular, their application to transcriptomics (RNA-seq) is increasingly being used for gene expression profiling as a replacement for microarrays. However, the properties of RNA-seq data have not been yet fully established, and additional research is needed for understanding how these data respond to differential expression analysis. In this work, we set out to gain insights into the characteristics of RNA-seq data analysis by studying an important parameter of this technology: the sequencing depth. We have analyzed how sequencing depth affects the detection of transcripts and their identification as differentially expressed, looking at aspects such as transcript biotype, length, expression level, and fold-change. We have evaluated different algorithms available for the analysis of RNA-seq and proposed a novel approach--NOISeq--that differs from existing methods in that it is data-adaptive and nonparametric. Our results reveal that most existing methodologies suffer from a strong dependency on sequencing depth for their differential expression calls and that this results in a considerable number of false positives that increases as the number of reads grows. In contrast, our proposed method models the noise distribution from the actual data, can therefore better adapt to the size of the data set, and is more effective in controlling the rate of false discoveries. This work discusses the true potential of RNA-seq for studying regulation at low expression ranges, the noise within RNA-seq data, and the issue of replication.

DOI10.1101/gr.124321.111
Alternate JournalGenome Res
PubMed ID21903743
PubMed Central IDPMC3227109