ReviewComputational and structural biotechnology journal2018
A review of somatic single nucleotide variant calling algorithms for next-generation sequencing data.
Review in Computational and structural biotechnology journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 126 papers.
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Who cites it
126 citing papers in PubMed.
- Somatic mosaicism in the brain: linking development, ageing and neurodegeneration.Nature reviews. Neurology · 2026Review
- Toward Practical Embryo Genomic Selection in Cattle: An Optimized Workflow Enabling High-Accuracy Breeding Value Prediction Without Compromising Embryo Survival.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Article
- Confounding factors in assessing the enriched expression of somatic mutant alleles in bulk tumor samples.Genome research · 2026Article
- Molecular origin, discovery, validation and application of neoantigens.Asian journal of pharmaceutical sciences · 2026Review
- Article
- Synth4bench: generating synthetic data for benchmarking tumor-only somatic variant calling algorithms.Frontiers in bioinformatics · 2026Article
- MutAnt: mutation annotation tool predicts deleteriousness of missense mutations and improves mutation calling from transcriptomics.Human genetics · 2025Article
- MOV&RSim: computational modelling of cancer-specific variants and sequencing reads characteristics for realistic tumoral sample simulation.BMC bioinformatics · 2025Article
- Comprehensive molecular characterization of craniopharyngiomas using whole transcriptome and spatial transcriptomics approaches.Brain tumor pathology · 2025Article
- Computational methods and data resources for predicting tumor neoantigens.Briefings in bioinformatics · 2025Review
- Current Bioinformatics Tools in Precision Oncology.MedComm · 2025Review
- UNISOM: Unified Somatic Calling and Machine Learning-based Classification Enhance the Discovery of CHIP.Genomics, proteomics & bioinformatics · 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
- Review
- Landscape of human protein-coding somatic mutations across tissues and individuals.bioRxiv : the preprint server for biology · 2025Article
- Artificial intelligence in variant calling: a review.Frontiers in bioinformatics · 2025Review
- A benchmarking study of individual somatic variant callers and voting-based ensembles for whole-exome sequencing.Briefings in bioinformatics · 2024Article
- Evaluating Bioinformatics Processing of Somatic Variant Detection in cfDNA Using Targeted Sequencing with UMIs.International journal of molecular sciences · 2024Article
- Benchmarking UMI-aware and standard variant callers for low frequency ctDNA variant detection.BMC genomics · 2024Article
66 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
1 author.
Funding
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Abstract
Detection of somatic mutations holds great potential in cancer treatment and has been a very active research field in the past few years, especially since the breakthrough of the next-generation sequencing technology. A collection of variant calling pipelines have been developed with different underlying models, filters, input data requirements, and targeted applications. This review aims to enumerate these unique features of the state-of-the-art variant callers, in the hope to provide a practical guide for selecting the appropriate pipeline for specific applications. We will focus on the detection of somatic single nucleotide variants, ranging from traditional variant callers based on whole genome or exome sequencing of paired tumor-normal samples to recent low-frequency variant callers designed for targeted sequencing protocols with unique molecular identifiers. The variant callers have been extensively benchmarked with inconsistent performances across these studies. We will review the reference materials, datasets, and performance metrics that have been used in the benchmarking studies. In the end, we will discuss emerging trends and future directions of the variant calling algorithms.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.