ArticleMethods (San Diego, Calif.)2015
Inconsistency and features of single nucleotide variants detected in whole exome sequencing versus transcriptome sequencing: A case study in lung cancer.
Article in Methods (San Diego, Calif.), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed.
- The molecular similarity landscape of preclinical cancer models to patient tumors.JCI insight · 2026Article
- Augmenting precision medicine via targeted RNA-Seq detection of expressed mutations.NPJ precision oncology · 2025Article
- Refined variant calling pipeline on RNA-seq data of breast cancer cell lines without matched-normal samples.BMC research notes · 2025Article
- RNA sequencing enables neoantigen discovery and vaccine validation in breast and lung cancer.Frontiers in immunology · 2025Article
- CanCellVar: A database for single-cell variants map in human cancer.American journal of human genetics · 2024Article
- Identification of gene variation feature for targeted therapy of non-small cell lung cancer through combined method of DNA and RNA sequencing.Discover oncology · 2024Article
- Article
- Improvement in neoantigen prediction via integration of RNA sequencing data for variant calling.Frontiers in immunology · 2023Article
- Transcriptome-based variant calling and aberrant mRNA discovery enhance diagnostic efficiency for neuromuscular diseases.Journal of medical genetics · 2022Article
- Cancer Neoantigens: Challenges and Future Directions for Prediction, Prioritization, and Validation.Frontiers in oncology · 2022Review
- Detecting Medium and Large Insertions and Deletions with transIndel.Methods in molecular biology (Clifton, N.J.) · 2022Article
- RNA-SSNV: A Reliable Somatic Single Nucleotide Variant Identification Framework for Bulk RNA-Seq Data.Frontiers in genetics · 2022Article
- RNA-seq driven expression and enrichment analysis to investigate CVD genes with associated phenotypes among high-risk heart failure patients.Human genomics · 2021Article
- Article
- Limitations of Detecting Genetic Variants from the RNA Sequencing Data in Tissue and Fine-Needle Aspiration Samples.Thyroid : official journal of the American Thyroid Association · 2021Article
- Comparative Transcriptomics and RNA-Seq-Based Bulked Segregant Analysis Reveals Genomic Basis UnderlyingFrontiers in microbiology · 2021Article
- Neoantigen prediction in human breast cancer using RNA sequencing data.Cancer science · 2021Article
- Immuno-genomic landscape of osteosarcoma.Nature communications · 2020Article
- Allelic Imbalance of Recurrently Mutated Genes in Acute Myeloid Leukaemia.Scientific reports · 2019Article
- Single-nucleotide variants in human RNA: RNA editing and beyond.Briefings in functional genomics · 2019Review
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
Whole exome sequencing (WES) and RNA sequencing (RNA-Seq) are two main platforms used for next-generation sequencing (NGS). While WES is primarily for DNA variant discovery and RNA-Seq is mainly for measurement of gene expression, both can be used for detection of genetic variants, especially single nucleotide variants (SNVs). How consistently variants can be detected from WES and RNA-Seq has not been systematically evaluated. In this study, we examined the technical and biological inconsistencies in SNV detection using WES and RNA-Seq data from 27 pairs of tumor and matched normal samples. We analyzed SNVs in three categories: WES unique - those only detected in WES, RNA-Seq unique - those only detected in RNA-Seq, and shared - those detected in both. We found a small overlap (average ∼14%) between the SNVs called in WES and RNA-Seq. The WES unique SNVs were mainly due to low coverage, low expression, or their location on the non-transcribed strand in RNA-Seq data, while the RNA-Seq unique SNVs were primarily due to their location out of the WES-capture boundary regions (accounting ∼71%), as well as low coverage of the regions, low coverage of the mutant alleles or RNA-editing. The shared SNVs had high locus-specific coverage in both WES and RNA-Seq and high gene expression levels. Additionally, WES unique and RNA-Seq unique SNVs showed different nucleotide substitution patterns, e.g., ∼55% of RNA-Seq unique variants were A:T→G:C, a hallmark of RNA editing. This study provides an important evaluation on the inconsistencies of somatic SNVs called in WES and RNA-Seq data.
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Registered trials
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