Reliable identification of genomic variants from RNA-seq data.
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.
Department of Genetics, Stanford University, Stanford, CA 94305, USA.
Robert
R
Piskol
Gokul
G
Ramaswami
Jin Billy
JB
Li
D059472:Exome; D005091:Exons; D015894:Genome, Human; D023281:Genomics / Q000379:methods*; D006801:Humans; D016366:Open Reading Frames; D020641:Polymorphism, Single Nucleotide; D012313:RNA / Q000235:genetics*; D012680:Sensitivity and Specificity; D017423:Sequence Analysis, RNA / Q000379:methods*
D012313:RNA