Identifying genomic variation is a crucial step for unraveling the relationship between genotype and phenotype and can yield important insights into human diseases. Prevailing methods rely on cost-intensive whole-genome sequencing (WGS) or whole-exome sequencing (WES) approaches while the identification of genomic variants from often existing RNA sequencing (RNA-seq) data remains a challenge because of the intrinsic complexity in the transcriptome.
Now, researchers at Stanford University have developed a highly accurate approach termed SNPiR to identify SNPs in RNA-seq data. They applied SNPiR to RNA-seq data of samples for which WGS and WES data are also available and achieved high specificity and sensitivity. Of the SNPs called from the RNA-seq data, >98% were also identified by WGS or WES. Over 70% of all expressed coding variants were identified from RNA-seq, and comparable numbers of exonic variants were identified in RNA-seq and WES. Despite their method’s limitation in detecting variants in expressed regions only, these results demonstrate that SNPiR outperforms current state-of-the-art approaches for variant detection from RNA-seq data and offers a cost-effective and reliable alternative for SNP discovery.
Availability – http://lilab.stanford.edu/SNPiR/
- Piskol R, Ramaswami G, Li JB. (2013) Reliable Identification of Genomic Variants from RNA-Seq Data. Am J Hum Genet [Epub ahead of print]. [abstract]