title: Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment.
abstract: BACKGROUND
  High-throughput RNA sequencing (RNA-seq) is now the standard method to determine differential gene expression. Identifying differentially expressed genes crucially depends on estimates of read-count variability. These estimates are typically based on statistical models such as the negative binomial distribution, which is employed by the tools edgeR, DESeq and cuffdiff. Until now, the validity of these models has usually been tested on either low-replicate RNA-seq data or simulations.
  
  
  RESULTS
  A 48-replicate RNA-seq experiment in yeast was performed and data tested against theoretical models. The observed gene read counts were consistent with both log-normal and negative binomial distributions, while the mean-variance relation followed the line of constant dispersion parameter of ∼0.01. The high-replicate data also allowed for strict quality control and screening of 'bad' replicates, which can drastically affect the gene read-count distribution.
  
  
  METHODS
  RNA-seq data have been submitted to ENA archive with project ID PRJEB5348.
  
  
  BACKGROUND
  g.j.barton@dundee.ac.uk.
authors.affiliation: Division of Computational Biology and Centre for Gene Regulation and Expression, College of Life Sciences, University of Dundee, Dow Street Dundee, DD1 5EH, UK.
authors.forename: Marek
authors.initials: M
authors.lastname: Gierliński
authors.affiliation: Division of Computational Biology and.
authors.forename: Christian
authors.initials: C
authors.lastname: Cole
authors.affiliation: Division of Computational Biology and Centre for Gene Regulation and Expression, College of Life Sciences, University of Dundee, Dow Street Dundee, DD1 5EH, UK.
authors.forename: Pietà
authors.initials: P
authors.lastname: Schofield
authors.affiliation: Division of Computational Biology and.
authors.forename: Nicholas J
authors.initials: NJ
authors.lastname: Schurch
authors.affiliation: Division of Computational Biology and.
authors.forename: Alexander
authors.initials: A
authors.lastname: Sherstnev
authors.affiliation: Centre for Gene Regulation and Expression, College of Life Sciences, University of Dundee, Dow Street Dundee, DD1 5EH, UK.
authors.forename: Vijender
authors.initials: V
authors.lastname: Singh
authors.affiliation: Edinburgh Genomics and.
authors.forename: Nicola
authors.initials: N
authors.lastname: Wrobel
authors.affiliation: Edinburgh Genomics and Institute of Evolutionary Biology, Ashworth Laboratories, University of Edinburgh, Edinburgh, UK.
authors.forename: Karim
authors.initials: K
authors.lastname: Gharbi
authors.affiliation: Division of Plant Sciences and.
authors.forename: Gordon
authors.initials: G
authors.lastname: Simpson
authors.affiliation: Centre for Gene Regulation and Expression, College of Life Sciences, University of Dundee, Dow Street Dundee, DD1 5EH, UK.
authors.forename: Tom
authors.initials: T
authors.lastname: Owen-Hughes
authors.affiliation: Edinburgh Genomics and Institute of Evolutionary Biology, Ashworth Laboratories, University of Edinburgh, Edinburgh, UK.
authors.forename: Mark
authors.initials: M
authors.lastname: Blaxter
authors.affiliation: Division of Computational Biology and Centre for Gene Regulation and Expression, College of Life Sciences, University of Dundee, Dow Street Dundee, DD1 5EH, UK, Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, Dow Street Dundee, DD1 5EH, UK.
authors.forename: Geoffrey J
authors.initials: GJ
authors.lastname: Barton
mesh_terms: D001483:Base Sequence; D016010:Binomial Distribution; D020869:Gene Expression Profiling; D015233:Models, Statistical; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000235:genetics; D017423:Sequence Analysis, RNA / Q000379:methods*