ArticleAmerican journal of human genetics2013
Reliable identification of genomic variants from RNA-seq data.
Article in American journal of human genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 224 papers.
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Who cites it
224 citing papers in PubMed.
- Small variant calling in the era of long-read sequencing.Nature reviews. Genetics · 2026Review
- NanoTS: a deep learning tool for accurate SNP calling in nanopore long-read transcriptome data.Nature methods · 2026Article
- Identification of Novel Transcriptional Alleles in Primary Prostate Cancer Cells and Cancer Stem Cells by Bulk RNA-Seq and Single-Cell RNA-Seq Analyses.Biomolecules · 2026Article
- Identification of variants from gene expression data of opioid-addicted patients.Journal of applied genetics · 2026Article
- Article
- Genetic Relatedness and Heterotic Grouping in MRIZP Elite Maize Inbred Lines Using SNP Markers from 25k SNP Array and RNA-seq Data.Current issues in molecular biology · 2026Article
- Fifteen Years of the Genome Analysis Toolkit as the De Facto Standard in Short-Read Variant Calling.International journal of molecular sciences · 2026Review
- R-loop editing by DNA cytosine deaminase APOBEC3B modulates the activity of oestrogen receptor enhancers.Nature communications · 2026Article
- Small Nucleotide Variant Analysis Using RNA Fusion Panel (SMURF): Making the Most of RNAseq Data in Solid Tumours.Journal of clinical laboratory analysis · 2026Article
- A Global Assessment of the Transcription-Dependent Single Nucleotide Variants Relies on the Characteristics of RNA-Sequencing Technologies.Biomolecules · 2026Article
- Whole-transcriptome identification of deleterious variants in candidate genes linked to bovine paratuberculosis.Scientific reports · 2026Article
- Sheep Genetic Resistance to Gastrointestinal Nematode Infections: Current Insights from Transcriptomics and Other OMICs Technologies-A Review.Pathogens (Basel, Switzerland) · 2026Review
- Clair3-RNA: a deep learning-based small variant caller for long-read RNA sequencing data.Nature communications · 2025Article
- Antagonistic regulation of LINE-1/Alu elements and their repressor APOBEC3B in cellular senescence.Mobile DNA · 2025Article
- Efficient and effective identification of cancer neoantigens from tumor only RNA-seq.NAR genomics and bioinformatics · 2025Article
- Landscape transcriptomics as a tool for addressing global change effects across diverse species.Molecular ecology resources · 2025Review
- Precise detection of differential RNA editing sites across varied biological conditions using the CADRES pipeline.Scientific reports · 2025Article
- Dual STAT3/STAT5 inhibition as a novel treatment strategy in T-prolymphocytic leukemia.Leukemia · 2025Article
- Application of RNA-seq for single nucleotide variation identification in a cohort of patients with hypertrophic cardiomyopathy.Scientific reports · 2025Article
- Variant calling from RNA-Seq data reveals allele-specific differential expression of pathogenic cancer variants.Communications medicine · 2025Article
164 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
Abstract
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.
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