Preferred analysis methods for single genomic regions in RNA sequencing revealed by processing the shape of coverage

Abstract

The informational content of RNA sequencing is currently far from being completely explored. Most of the analyses focus on processing tables of counts or finding isoform deconvolution via exon junctions. This article presents a comparison of several techniques that can be used to estimate differential expression of exons or small genomic regions of expression, based on their coverage function shapes. The problem is defined as finding the differentially expressed exons between two samples using local expression profile normalization and statistical measures to spot the differences between two profile shapes. Initial experiments have been done using synthetic data, and real data modified with synthetically created differential patterns. Then, 160 pipelines (5 types of generator × 4 normalizations × 8 difference measures) are compared. As a result, the best analysis pipelines are selected based on linearity of the differential expression estimation and the area under the ROC curve. These platform-independent techniques have been implemented in the Bioconductor package rnaSeqMap. They point out the exons with differential expression or internal splicing, even if the counts of reads may not show this. The areas of application include significant difference searches, splicing identification algorithms and finding suitable regions for QPCR primers.

Count-based differential expression analysis of RNA sequencing data using R and Bioconductor

“…Recently, a flexible Bayesian framework for the analysis of "random" effects in the context of GLM models and RNA-seq count data was made available in the ShrinkSeq package 31 . As well, count-based methods that operate at the exon level, which share the same statistical framework, as well as flexible coverage-based methods have become available to address the limitations of gene-level analyses 29,32,33 . These methods give a direct readout of differential exons, genes whose exons are used unequally, or non-parallel coverage profiles, all of which reflect a change in isoform usage.…”

Uncovering correlated variability in epigenomic datasets using the Karhunen-Loeve transform

“…We have explored variability decomposed into functional components to reveal differences between chromatin modifications at transcription start sites. The biological interpretation of curve information in NGS is relevant, as we and others have shown recently for ChIP-seq peak calling [ 26 , 54 – 56 ], RNA-seq [ 57 ], and DNA methylation by bisulfite sequencing [ 58 ]. The methodology we have proposed and illustrated here continues the progress in the interpretation of next generation sequencing data.…”