Chapter

Algorithms in Bioinformatics

Volume 7534 of the series Lecture Notes in Computer Science pp 178-189

CLIIQ: Accurate Comparative Detection and Quantification of Expressed Isoforms in a Population

  • Yen-Yi LinAffiliated withSchool of Computing Science, Simon Fraser University
  • , Phuong DaoAffiliated withSchool of Computing Science, Simon Fraser University
  • , Faraz HachAffiliated withSchool of Computing Science, Simon Fraser University
  • , Marzieh BakhshiAffiliated withSchool of Computing Science, Simon Fraser University
  • , Fan MoAffiliated withVancouver Prostate Centre & Department of Urologic Sciences, University of British Columbia
  • , Anna LapukAffiliated withVancouver Prostate Centre & Department of Urologic Sciences, University of British Columbia
  • , Colin CollinsAffiliated withVancouver Prostate Centre & Department of Urologic Sciences, University of British Columbia
  • , S. Cenk SahinalpAffiliated withSchool of Computing Science, Simon Fraser University

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Abstract

The recently developed RNA-Seq technology provides a high-throughput and reasonably accurate way to analyze the transcriptomic landscape of a tissue. Unfortunately, from a computational perspective, identification and quantification of a gene’s isoforms from RNA-Seq data remains to be a non-trivial problem. We propose CLIIQ, a novel computational method for identification and quantification of expressed isoforms from multiple samples in a population. Motivated by ideas from compressed sensing literature, CLIIQ is based on an integer linear programming formulation for identifying and quantifying ”the most parsimonious” set of isoforms. We show through simulations that, on a single sample, CLIIQ provides better results in isoform identification and quantification to alternative popular tools. More importantly, CLIIQ has an option to jointly analyze multiple samples, which significantly outperforms other tools in both isoform identification and quantification.

Keywords

Isoform Identification Isoform Quantification RNA-Seq Transcriptomics Integer Linear Programming