SNVQ – A new bayesian method for SNV discovery and genotyping based on quality scores

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.

Presented here is 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. The authors 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 for implementing these methods available at:

  • Duitama J, Srivastava PK. Măndoiu II, (2012) Towards accurate detection and genotyping of expressed variants from whole transcriptome sequencing data. BMC Genomics 13(Suppl 2), S6  [article]