Reliable Identification of Genomic Variants from RNA-Seq Data

• Published in: American journal of human genetics Vol. 93; no. 4; pp. 641 - 651
• Main Authors: Piskol, Robert, Ramaswami, Gokul, Li, Jin Billy
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 03-10-2013; Cell Press; Elsevier
• Subjects: Biological variation; Exome; Exons; Genome, Human; Genomics; Genomics - methods; Genotype & phenotype; Humans; Identification; Open Reading Frames; Polymorphism, Single Nucleotide; Ribonucleic acid; RNA; RNA - genetics; Sensitivity and Specificity; Sequence Analysis, RNA - methods
• ISSN: 0002-9297; 1537-6605
• DOI: 10.1016/j.ajhg.2013.08.008

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. Here, we present a highly accurate approach termed SNPiR to identify SNPs in RNA-seq data. We 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 our method’s limitation in detecting variants in expressed regions only, our 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.