RT Journal Article
SR Electronic
T1 Side-by-side analysis of alternative approaches on multi-level RNA-seq data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 131862
DO 10.1101/131862
A1 Irina Mohorianu
YR 2017
UL http://biorxiv.org/content/early/2017/04/28/131862.abstract
AB Background RNA sequencing (RNA-seq) is widely used for RNA quantification across environmental, biological and medical sciences; it enables the description of genome-wide patterns of expression and the deduction of regulatory interactions and networks. The aim of computational analyses is to achieve an accurate output, i.e. rigorous quantification of genes/transcripts to allow a reliable prediction of differential expression (DE), despite the variable levels of noise and biases present in sequencing data. The evaluation of sequencing quality and normalization are essential components of this process.Results We investigate the discriminative power of existing approaches for the quality checking of mRNA-seq data and also propose additional, quantitative, quality checks. To accommodate the analysis of a nested, multi-level design using data on D. melanogaster, we incorporated the sample layout into the analysis. We describe a “subsampling without replacement”-based normalization and identification of DE that accounts for the experimental design i.e. the hierarchy and amplitude of effect sizes within samples. We also evaluate the differential expression call in comparison to existing approaches. To assess the broader applicability of these methods, we applied this series of steps to a published set of H. sapiens mRNA-seq samples.Conclusions The dataset-tailored methods improved sample comparability and delivered a robust prediction of subtle gene expression changes. Overall, the proposed approach offers the potential to improve key steps in the analysis of RNA-seq data by incorporating the structure and characteristics of biological experiments into the data analysis. 38