Preferred analysis methods for single genomic regions in RNA sequencing revealed by processing the shape of coverage.
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.
Functional Genomics Center Zurich, UNI ETH Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland. michal@fgcz.ethz.ch
Michal J
MJ
Okoniewski
Anna
A
Leśniewska
Alicja
A
Szabelska
Joanna
J
Zyprych-Walczak
Martin
M
Ryan
Marco
M
Wachtel
Tadeusz
T
Morzy
Beat
B
Schäfer
Ralph
R
Schlapbach
D005091:Exons; D020869:Gene Expression Profiling; D023281:Genomics / Q000379:methods; D012372:ROC Curve; D017423:Sequence Analysis, RNA