Estimation of data-specific constitutive exons with RNA-Seq data.
BACKGROUND
RNA-Seq has the potential to answer many diverse and interesting questions about the inner workings of cells. Estimating changes in the overall transcription of a gene is not straightforward. Changes in overall gene transcription can easily be confounded with changes in exon usage which alter the lengths of transcripts produced by a gene. Measuring the expression of constitutive exons--xons which are consistently conserved after splicing--ffers an unbiased estimation of the overall transcription of a gene.


RESULTS
We propose a clustering-based method, exClust, for estimating the exons that are consistently conserved after splicing in a given data set. These are considered as the exons which are "constitutive" in this data. The method utilises information from both annotation and the dataset of interest. The method is implemented in an openly available R function package, sydSeq.


CONCLUSIONS
When used on two real datasets exClust includes more than three times as many reads as the standard UI method, and improves concordance with qRT-PCR data. When compared to other methods, our method is shown to produce robust estimates of overall gene transcription.
School of Mathematics and Statistics, University of Sydney, Sydney NSW 2006, Australia.
Ellis
E
Patrick
Michael
M
Buckley
Yee Hwa
YH
Yang
D000465:Algorithms; D017398:Alternative Splicing; D016000:Cluster Analysis; D005091:Exons; D020869:Gene Expression Profiling / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D017423:Sequence Analysis, RNA / Q000379:methods*