ArticleBMC genomics2017
The discrepancy among single nucleotide variants detected by DNA and RNA high throughput sequencing data.
Article in BMC genomics, 2017. 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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Who cites it
25 citing papers in PubMed.
- Application of RNA markers in forensic body fluid analysis: from specificity and stability to polymorphism.International journal of legal medicine · 2026Review
- Clair3-RNA: a deep learning-based small variant caller for long-read RNA sequencing data.Nature communications · 2025Article
- MutAnt: mutation annotation tool predicts deleteriousness of missense mutations and improves mutation calling from transcriptomics.Human genetics · 2025Article
- Augmenting precision medicine via targeted RNA-Seq detection of expressed mutations.NPJ precision oncology · 2025Article
- Allelic expression patterns of imprinted and non-imprinted genes in cancer cell lines from multiple histologies.Clinical epigenetics · 2025Article
- Article
- New ABL1 Kinase Domain Mutations in BCR::ABL1-Positive Acute Lymphoblastic Leukemia.Cancer medicine · 2024Article
- RNA-DNA differences in variant calls from cattle tissues result in erroneous eQTLs.BMC genomics · 2024Article
- Transcriptome analysis of primary adult B-cell lineage acute lymphoblastic leukemia identifies pathogenic variants and gene fusions, and predicts subtypes for in depth molecular diagnosis.European journal of haematology · 2024Article
- Codon Usage Provide Insights into the Adaptation of Rice Genes under Stress Condition.International journal of molecular sciences · 2023Article
- A deep-learning-based RNA-seq germline variant caller.Bioinformatics advances · 2023Article
- RNA editing in Mycobacterium tuberculosis.Microbiological research · 2022Review
- An advanced molecular medicine case report of a rare human tumor using genomics, pathomics, and radiomics.Frontiers in genetics · 2022Article
- Large-scale transcriptome sequencing in broiler chickens to identify candidate genes for breast muscle weight and intramuscular fat content.Genetics, selection, evolution : GSE · 2021Article
- RNA-Seq Data for Reliable SNP Detection and Genotype Calling: Interest for Coding Variant Characterization andFrontiers in genetics · 2021Article
- Adaptive Differences in Gene Expression in Farm-Impacted Seedbeds of the Native Blue MusselFrontiers in genetics · 2021Article
- The mutational landscape of the SCAN-B real-world primary breast cancer transcriptome.EMBO molecular medicine · 2020Article
- Genome-Wide Development and Validation of Cost-Effective KASP Marker Assays for Genetic Dissection of Heat Stress Tolerance in Maize.International journal of molecular sciences · 2020Article
- Cancer-specific expression quantitative loci are affected by expression dysregulation.Briefings in bioinformatics · 2020Article
- Sex-Dependent RNA Editing andFrontiers in cell and developmental biology · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundHigh throughput sequencing technology enables the both the human genome and transcriptome to be screened at the single nucleotide resolution. Tools have been developed to infer single nucleotide variants (SNVs) from both DNA and RNA sequencing data. To evaluate how much difference can be expected between DNA and RNA sequencing data, and among tissue sources, we designed a study to examine the single nucleotide difference among five sources of high throughput sequencing data generated from the same individual, including exome sequencing from blood, tumor and adjacent normal tissue, and RNAseq from tumor and adjacent normal tissue.
resultsThrough careful quality control and analysis of the SNVs, we found little difference between DNA-DNA pairs (1%-2%). However, between DNA-RNA pairs, SNV differences ranged anywhere from 10% to 20%.
conclusionsOnly a small portion of these differences can be explained by RNA editing. Instead, the majority of the DNA-RNA differences should be attributed to technical errors from sequencing and post-processing of RNAseq data. Our analysis results suggest that SNV detection using RNAseq is subject to high false positive rates.
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