Software review
A survey of statistical software for analysing RNA-seq data
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
Human Genomics 2010, 5:56-60 doi:10.1186/1479-7364-5-1-56
Published: 1 October 2010Abstract
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.
