Email updates

Keep up to date with the latest news and content from Human Genomics and BioMed Central.

Open Access Software review

A survey of statistical software for analysing RNA-seq data

Dexiang Gao15*, Jihye Kim2, Hyunmin Kim4, Tzu L Phang3, Heather Selby2, Aik Choon Tan25 and Tiejun Tong6*

Author Affiliations

1 Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO 80045, USA

2 Division of Medical Oncology, University of Colorado School of Medicine, Aurora, CO 80045, USA

3 Division of Critical Care and Pulmonary Medicine, University of Colorado School of Medicine, Aurora, CO 80045, USA

4 Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, CO 80045, USA

5 Department of Biostatistics and Informatics, University of Colorado School of Public Health, Aurora, CO 80045, USA

6 Department of Applied Mathematics, University of Colorado, Boulder, CO 80309, USA

For all author emails, please log on.

Human Genomics 2010, 5:56-60  doi:10.1186/1479-7364-5-1-56

Published: 1 October 2010

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 webcite 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.

Keywords:
statistical software; RNA-sequencing analysis; normalisation; sequencing data