DiffSplice: The genome-wide detection of differential splicing events with RNA-seq

Yin Hu, Yan Huang, Ying Du, Christian F. Orellana, Darshan Singh, Amy R. Johnson, Anaïs Monroy, Pei Fen Kuan, Scott M. Hammond, Liza Makowski, Scott H. Randell, Derek Y. Chiang, D. Neil Hayes, Corbin Jones, Yufeng Liu, Jan F. Prins, Jinze Liu

Research output: Contribution to journalArticlepeer-review

109 Scopus citations


The RNA transcriptome varies in response to cellular differentiation as well as environmental factors, and can be characterized by the diversity and abundance of transcript isoforms. Differential transcription analysis, the detection of differences between the transcriptomes of different cells, may improve understanding of cell differentiation and development and enable the identification of biomarkers that classify disease types. The availability of high-throughput short-read RNA sequencing technologies provides in-depth sampling of the transcriptome, making it possible to accurately detect the differences between transcriptomes. In this article, we present a new method for the detection and visualization of differential transcription. Our approach does not depend on transcript or gene annotations. It also circumvents the need for full transcript inference and quantification, which is a challenging problem because of short read lengths, as well as various sampling biases. Instead, our method takes a divide-and-conquer approach to localize the difference between transcriptomes in the form of alternative splicing modules (ASMs), where transcript isoforms diverge. Our approach starts with the identification of ASMs from the splice graph, constructed directly from the exons and introns predicted from RNA-seq read alignments. The abundance of alternative splicing isoforms residing in each ASM is estimated for each sample and is compared across sample groups. A non-parametric statistical test is applied to each ASM to detect significant differential transcription with a controlled false discovery rate. The sensitivity and specificity of the method have been assessed using simulated data sets and compared with other state-of-the-art approaches. Experimental validation using qRT-PCR confirmed a selected set of genes that are differentially expressed in a lung differentiation study and a breast cancer data set, demonstrating the utility of the approach applied on experimental biological data sets. The software of DiffSplice is available at http://www.netlab.uky.edu/p/bioinfo/DiffSplice.

Original languageEnglish
Pages (from-to)e39
JournalNucleic Acids Research
Issue number2
StatePublished - Jan 2013

Bibliographical note

Funding Information:
US National Institutes of Health [R01-HG006272 to J.F.P and J.L.]; US National Science Foundation [EF-0850237 to J.L. and J.F.P.]. Additional support was provided by NSF Career award [IIS-1054631 to J.L.]; National Institutes of Health grants [RC1-HL100108 to D.N.H., S.M.H. and S.H.R., AA017376 to L.M., U24-CA143848 and 3U24-CA143848-02S1 to D.N.H., R01-CA149569-03 to Y.L.]; UNC University Cancer Research Fund (to C.J. and L.M.). Funding for open access charge: NIH [R01-HG006272].

ASJC Scopus subject areas

  • Genetics


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