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

^*Corresponding author for this work

Department of Mathematics

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 - 1 Oct 2010

Scopus Subject Areas

Molecular Medicine
Molecular Biology
Genetics
Drug Discovery

User-Defined Keywords

normalisation
RNA-sequencing analysis
sequencing data
statistical software

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10.1186/1479-7364-5-1-56

Cite this

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AB - 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.

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A survey of statistical software for analysing RNA-seq data

Abstract

Scopus Subject Areas

User-Defined Keywords

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