title: A survey of statistical software for analysing RNA-seq data.
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.
authors.affiliation: Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO 80045, USA. Dexiang.Gao@UCDenver.edu
authors.forename: Dexiang
authors.initials: D
authors.lastname: Gao
authors.forename: Jihye
authors.initials: J
authors.lastname: Kim
authors.forename: Hyunmin
authors.initials: H
authors.lastname: Kim
authors.forename: Tzu L
authors.initials: TL
authors.lastname: Phang
authors.forename: Heather
authors.initials: H
authors.lastname: Selby
authors.forename: Aik Choon
authors.initials: AC
authors.lastname: Tan
authors.forename: Tiejun
authors.initials: T
authors.lastname: Tong
mesh_terms: D030561:Databases, Nucleic Acid; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D015233:Models, Statistical; D012984:Software