Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Methodology article

Estimation of data-specific constitutive exons with RNA-Seq data

Ellis Patrick12, Michael Buckley2 and Yee Hwa Yang1*

Author affiliations

1 School of Mathematics and Statistics, University of Sydney, Sydney NSW 2006, Australia

2 CSIRO Mathematical & Information Sciences, Private Bag 33, Clayton South 3168, Australia

For all author emails, please log on.

Citation and License

BMC Bioinformatics 2013, 14:31  doi:10.1186/1471-2105-14-31

Published: 29 January 2013

Abstract

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— exons which are consistently conserved after splicing— offers 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.

Conclusion

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.