ArticleBMC bioinformatics2018
TNER: a novel background error suppression method for mutation detection in circulating tumor DNA.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Tumor-naïve ctDNA detection with deep learning-enhanced error suppression for sensitive mutation calling.Genome medicine · 2026Article
- MitoDelta: identifying mitochondrial DNA deletions at cell-type resolution from single-cell RNA sequencing data.BMC genomics · 2025Article
- ERIC recommendations for TP53 mutation analysis in chronic lymphocytic leukemia-2024 update.Leukemia · 2024Review
- Review
- Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp.iMeta · 2023Article
- Review
- A novel virtual barcode strategy for accurate panel-wide variant calling in circulating tumor DNA.BMC bioinformatics · 2020Article
- Applications and analysis of targeted genomic sequencing in cancer studies.Computational and structural biotechnology journal · 2019Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundUltra-deep next-generation sequencing of circulating tumor DNA (ctDNA) holds great promise as a tool for the early detection of cancer and for monitoring disease progression and therapeutic responses. However, the low abundance of ctDNA in the bloodstream coupled with technical errors introduced during library construction and sequencing complicates mutation detection.
resultsTo achieve high accuracy of variant calling via better distinguishing low-frequency ctDNA mutations from background errors, we introduce TNER (Tri-Nucleotide Error Reducer), a novel background error suppression method that provides a robust estimation of background noise to reduce sequencing errors. The results on both simulated data and real data from healthy subjects demonstrate that the proposed algorithm consistently outperforms a current, state-of-the-art, position-specific error polishing model, particularly when the sample size of healthy subjects is small.
conclusionsTNER significantly enhances the specificity of downstream ctDNA mutation detection without sacrificing sensitivity. The tool is publicly available at https://github.com/ctDNA/TNER .
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