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This article is part of the supplement: Selected articles from the First IEEE International Conference on Computational Advances in Bio and medical Sciences (ICCABS 2011): Genomics

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Towards accurate detection and genotyping of expressed variants from whole transcriptome sequencing data

Jorge Duitama12*, Pramod K Srivastava3 and Ion I Măndoiu1*

Author Affiliations

1 Department of Computer Science & Engineering, University of Connecticut, 371 Fairfield Rd, Unit 2155, Storrs, CT, 06269-2155, USA

2 Centre of Microbial and Plant Genetics, Katholieke Universiteit Leuven, Gaston Geenslaan 1 - Box 2471, 3001 Heverlee, Belgium

3 Department of Immunology and the Center for Immunotherapy of Cancer and Infectious Diseases, University of Connecticut Health Center, 263 Farmington Avenue, Farmington, CT 06030-1601, USA

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BMC Genomics 2012, 13(Suppl 2):S6  doi:10.1186/1471-2164-13-S2-S6

Published: 12 April 2012



Massively parallel transcriptome sequencing (RNA-Seq) is becoming the method of choice for studying functional effects of genetic variability and establishing causal relationships between genetic variants and disease. However, RNA-Seq poses new technical and computational challenges compared to genome sequencing. In particular, mapping transcriptome reads onto the genome is more challenging than mapping genomic reads due to splicing. Furthermore, detection and genotyping of single nucleotide variants (SNVs) requires statistical models that are robust to variability in read coverage due to unequal transcript expression levels.


In this paper we present a strategy to more reliably map transcriptome reads by taking advantage of the availability of both the genome reference sequence and transcript databases such as CCDS. We also present a novel Bayesian model for SNV discovery and genotyping based on quality scores.


Experimental results on RNA-Seq data generated from blood cell tissue of three Hapmap individuals show that our methods yield increased accuracy compared to several widely used methods. The open source code implementing our methods, released under the GNU General Public License, is available at webcite.