Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment

Marek Gierlinski, Christian Cole, Pietà Schofield, Nicholas J. Schurch, Alexander Sherstnev, Vijender Singh, Nicola Wrobel, Karim Gharbi, Gordon Simpson, Tom Owen-Hughes, Mark Blaxter, Geoffrey J. Barton

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Abstract

Motivation: 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.
Availability and implementation: RNA-seq data have been submitted to ENA archive with project ID PRJEB5348.
Original languageEnglish
Pages (from-to)3625-3630
Number of pages6
JournalBioinformatics
Volume31
Issue number22
Early online date23 Jul 2015
DOIs
Publication statusPublished - 15 Nov 2015

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