Statistical methods on detecting differentially expressed genes for RNA-seq data

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A comparison of statistical methods for detecting differentially expressed genes from RNA-seq data.

RNA-Seq technologies are quickly revolutionizing genomic studies, and statistical methods for RNA-seq data are under continuous development. Timely review and comparison of the most recently proposed statistical methods will provide a useful guide for choosing among them for data analysis. Particular interest surrounds the ability to detect differential expression (DE) in genes. Here we compare...

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RNA-sequencing (RNA-seq) technology has become a major choice in detecting differentially expressed genes across different biological conditions. Although microarray technology is used for the same purpose, statistical methods available for identifying differential expression for microarray data are generally not readily applicable to the analysis of RNA-seq data, as RNA-seq data comprise discr...

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A common challenge in analysis of transcriptomic data is to identify differentially expressed genes, i.e., genes whose mean transcript abundance levels differ across the levels of a factor of scientific interest. Transcript abundance levels can be measured simultaneously for thousands of genes in multiple biological samples using RNA sequencing (RNA-seq) technology. Part of the variation in RNA...

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ژورنال

عنوان ژورنال: BMC Systems Biology

سال: 2011

ISSN: 1752-0509

DOI: 10.1186/1752-0509-5-s3-s1