Estimation of alternative splicing isoform frequencies from RNA-seq data

Marius Nicolae*, Serghei Mangul, Ion Mǎndoiu, Alex Zelikovsky

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

20 Scopus citations

Abstract

In this paper we present a novel expectation-maximization algorithm for inference of alternative splicing isoform frequencies from high-throughput transcriptome sequencing (RNA-Seq) data. Our algorithm exploits disambiguation information provided by the distribution of insert sizes generated during sequencing library preparation, and takes advantage of base quality scores, strand and read pairing information if available. Empirical experiments on synthetic datasets show that the algorithm significantly outperforms existing methods of isoform and gene expression level estimation from RNA-Seq data. The Java implementation of IsoEM is available at http://dna.engr.uconn.edu/software/ IsoEM/.

Original languageEnglish (US)
Title of host publicationAlgorithms in Bioinformatics - 10th International Workshop, WABI 2010, Proceedings
Pages202-214
Number of pages13
DOIs
StatePublished - Nov 10 2010
Event10th International Workshop on Algorithms in Bioinformatics, WABI 2010 - Liverpool, United Kingdom
Duration: Sep 6 2010Sep 8 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6293 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Workshop on Algorithms in Bioinformatics, WABI 2010
CountryUnited Kingdom
CityLiverpool
Period9/6/109/8/10

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Nicolae, M., Mangul, S., Mǎndoiu, I., & Zelikovsky, A. (2010). Estimation of alternative splicing isoform frequencies from RNA-seq data. In Algorithms in Bioinformatics - 10th International Workshop, WABI 2010, Proceedings (pp. 202-214). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6293 LNBI). https://doi.org/10.1007/978-3-642-15294-8_17