A survey of statistical software for analysing RNA-seq data

Dexiang Gao; Jihye Kim; Hyunmin Kim; Tzu L. Phang; Heather Selby; Aik Choon Tan; Tiejun Tong

doi:10.1186/1479-7364-5-1-56

A survey of statistical software for analysing RNA-seq data

Dexiang Gao, Jihye Kim, Hyunmin Kim, Tzu L. Phang, Heather Selby, Aik Choon Tan, Tiejun Tong

Department of Computer Science and Engineering

Research output: Contribution to journal › Review article › peer-review

24 Citations (Scopus)

Abstract

High-throughput RNA sequencing is rapidly emerging as a favourite method for gene expression studies. We review three software packages - edgeR, DEGseq and baySeq - from Bioconductor http://bioconductor.org for analysing RNA-sequencing data. We focus on three aspects: normalisation, statistical models and the testing employed on these methods. We also discuss the advantages and limitations of these software packages.

Original language	English
Pages (from-to)	56-60
Number of pages	5
Journal	Human Genomics
Volume	5
Issue number	1
DOIs	https://doi.org/10.1186/1479-7364-5-1-56
Publication status	Published - 2010 Oct 1

Keywords

RNA-sequencing analysis
normalisation
sequencing data
statistical software

ASJC Scopus subject areas

Molecular Medicine
Molecular Biology
Genetics
Drug Discovery

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10.1186/1479-7364-5-1-56

Cite this

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A survey of statistical software for analysing RNA-seq data

Abstract

Keywords

ASJC Scopus subject areas

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