Improved transcriptome quantification and reconstruction from RNA-Seq reads using partial annotations

Serghei Mangul*, Adrian Caciula, Olga Glebova, Ion Mandoiu, Alex Zelikovsky

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

The paper addresses the problem of how to use RNA-Seq data for transcriptome reconstruction and quantification, as well as novel transcript discovery in partially annotated genomes. We present a novel annotation-guided general framework for transcriptome discovery, reconstruction and quantification in partially annotated genomes and compare it with existing annotation-guided and genome-guided transcriptome assembly methods. Our method, referred as Discovery and Reconstruction of Unannotated Transcripts (DRUT), can be used to enhance existing transcriptome assemblers, such as Cufflinks [3], as well as to accurately estimate the transcript frequencies. Empirical analysis on synthetic datasets confirms that Cufflinks enhanced by DRUT has superior quality of reconstruction and frequency estimation of transcripts.

Original languageEnglish (US)
Pages (from-to)251-261
Number of pages11
JournalIn Silico Biology
Volume11
Issue number5
DOIs
StatePublished - 2012

Keywords

  • Expectation maximization
  • Next generation sequencing
  • RNA-Seq
  • Transcriptome reconstruction and quantification

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics
  • Computational Mathematics
  • Computational Theory and Mathematics

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