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This article is part of the supplement: Selected articles from the 2009 IEEE International Conference on Bioinformatics and Biomedicine

Open Access Highly Accessed Proceedings

Towards reliable isoform quantification using RNA-SEQ data

Brian E Howard* and Steffen Heber

Author Affiliations

Bioinformatics Research Center, North Carolina State University, Raleigh, NC, 27606, USA

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BMC Bioinformatics 2010, 11(Suppl 3):S6  doi:10.1186/1471-2105-11-S3-S6

Published: 29 April 2010

Abstract

Background

In eukaryotes, alternative splicing often generates multiple splice variants from a single gene. Here weexplore the use of RNA sequencing (RNA-Seq) datasets to address the isoform quantification problem. Given a set of known splice variants, the goal is to estimate the relative abundance of the individual variants.

Methods

Our method employs a linear models framework to estimate the ratios of known isoforms in a sample. A key feature of our method is that it takes into account the non-uniformity of RNA-Seq read positions along the targeted transcripts.

Results

Preliminary tests indicate that the model performs well on both simulated and real data. In two publicly available RNA-Seq datasets, we identified several alternatively-spliced genes with switch-like, on/off expression properties, as well as a number of other genes that varied more subtly in isoform expression. In many cases, genes exhibiting differential expression of alternatively spliced transcripts were not differentially expressed at the gene level.

Conclusions

Given that changes in isoform expression level frequently involve a continuum of isoform ratios, rather than all-or-nothing expression, and that they are often independent of general gene expression changes, we anticipate that our research will contribute to revealing a so far uninvestigated layer of the transcriptome. We believe that, in the future, researchers will prioritize genes for functional analysis based not only on observed changes in gene expression levels, but also on changes in alternative splicing.