  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. 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. RNA-seq data have been submitted to ENA
archive with project ID PRJEB5348.
