ArticleScientific reports2023
Evaluating the performance of low-frequency variant calling tools for the detection of variants from short-read deep sequencing data.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Resolving variants of uncertain significance in neurofibromatosis: An integrated approach combining deep learning and minigene assays.Functional & integrative genomics · 2026Article
- Next-generation sequencing: Driving a paradigm shift from pathogen detection to systems-level analysis.Infectious medicine · 2026Article
- Characterization of subclonal variants in HG002 Genome in a Bottle reference material as a resource for benchmarking variant callers.Cell genomics · 2026Article
- Technical feasibility of a long read, fourth generation sequencing platform in diagnostic profiling of clinical routine samples: a proof-of-concept study.Pathologica · 2026Article
- Screening for heterogeneous drug resistance in tuberculosis and its impact on clinical prognosis: A comprehensive review.iScience · 2026Review
- DoBSeqWF: a framework for sensitive detection of individual genetic variation in pooled sequencing data.NAR genomics and bioinformatics · 2026Article
- bronko: ultrafast, alignment-free detection of viral genome variation.bioRxiv : the preprint server for biology · 2026Article
- Synth4bench: generating synthetic data for benchmarking tumor-only somatic variant calling algorithms.Frontiers in bioinformatics · 2026Article
- Article
- Benchmarking UMI clustering tools for accurate detection of low-frequency variants from deep sequencing.Scientific reports · 2025Article
- Accurate detections of the heterozygous SNPs with rice genomic data and prediction of de novo spontaneous mutation rate.Plant methods · 2025Article
- Identifying genetic errors of immunity due to mosaicism.The Journal of experimental medicine · 2025Review
- Inferring active mutational processes in cancer using single cell sequencing and evolutionary constraints.bioRxiv : the preprint server for biology · 2025Article
- A robust benchmark for detecting low-frequency variants in the HG002 Genome In A Bottle NIST reference material.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Detection of low-frequency variants with high accuracy plays an important role in biomedical research and clinical practice. However, it is challenging to do so with next-generation sequencing (NGS) approaches due to the high error rates of NGS. To accurately distinguish low-level true variants from these errors, many statistical variants calling tools for calling low-frequency variants have been proposed, but a systematic performance comparison of these tools has not yet been performed. Here, we evaluated four raw-reads-based variant callers (SiNVICT, outLyzer, Pisces, and LoFreq) and four UMI-based variant callers (DeepSNVMiner, MAGERI, smCounter2, and UMI-VarCal) considering their capability to call single nucleotide variants (SNVs) with allelic frequency as low as 0.025% in deep sequencing data. We analyzed a total of 54 simulated data with various sequencing depths and variant allele frequencies (VAFs), two reference data, and Horizon Tru-Q sample data. The results showed that the UMI-based callers, except smCounter2, outperformed the raw-reads-based callers regarding detection limit. Sequencing depth had almost no effect on the UMI-based callers but significantly influenced on the raw-reads-based callers. Regardless of the sequencing depth, MAGERI showed the fastest analysis, while smCounter2 consistently took the longest to finish the variant calling process. Overall, DeepSNVMiner and UMI-VarCal performed the best with considerably good sensitivity and precision of 88%, 100%, and 84%, 100%, respectively. In conclusion, the UMI-based callers, except smCounter2, outperformed the raw-reads-based callers in terms of sensitivity and precision. We recommend using DeepSNVMiner and UMI-VarCal for low-frequency variant detection. The results provide important information regarding future directions for reliable low-frequency variant detection and algorithm development, which is critical in genetics-based medical research and clinical applications.
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