Inference of Isoforms from Short Sequence Reads

(Extended Abstract)
  • Jianxing Feng
  • Wei Li
  • Tao Jiang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6044)


Due to alternative splicing events in eukaryotic species, the identification of mRNA isoforms (or splicing variants) is a difficult problem. Traditional experimental methods for this purpose are time consuming and cost ineffective. The emerging RNA-Seq technology provides a possible effective method to address this problem. Although the advantages of RNA-Seq over traditional methods in transcriptome analysis have been confirmed by many studies, the inference of isoforms from millions of short sequence reads (e.g., Illumina/Solexa reads) has remained computationally challenging. In this work, we propose a method to calculate the expression levels of isoforms and infer isoforms from short RNA-Seq reads using exon-intron boundary, transcription start site (TSS) and poly-A site (PAS) information. We first formulate the relationship among exons, isoforms, and single-end reads as a convex quadratic program, and then use an efficient algorithm (called IsoInfer) to search for isoforms. IsoInfer can calculate the expression levels of isoforms accurately if all the isoforms are known and infer novel isoforms from scratch. Our experimental tests on known mouse isoforms with both simulated expression levels and reads demonstrate that IsoInfer is able to calculate the expression levels of isoforms with an accuracy comparable to the state-of-the-art statistical method and a 60 times faster speed. Moreover, our tests on both simulated and real reads show that it achieves a good precision and sensitivity in inferring isoforms when given accurate exon-intron boundary, TSS and PAS information, especially for isoforms whose expression levels are significantly high.


Transcription Start Site Alternative Splice Event Massively Parallel Signature Sequencing Exon Junction Splice Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jianxing Feng
    • 1
  • Wei Li
    • 2
  • Tao Jiang
    • 2
    • 3
  1. 1.State Key Laboratory on Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer ScienceTsinghua Univ.BeijingChina
  2. 2.Department of Computer ScienceUniv. of CaliforniaRiverside
  3. 3.Tsinghua Univ.BeijingChina

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