ArticleNature biotechnology2019
Best practices for benchmarking germline small-variant calls in human genomes.
Article in Nature biotechnology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 263 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
263 citing papers in PubMed.
- Telomere-to-telomere CHM13 reference reveals missing truth variants and improves deep learning-based variant calling in long-read sequencing data.Quantitative biology (Beijing, China) · 2026Article
- Structural variant detection across sequencing technologies.Nature biotechnology · 2026Article
- SNP calling, haplotype phasing and allele-specific analysis with long RNA-seq reads.Nature methods · 2026Article
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- Structural Variant benchmarking frameworks: Parameterization, matching logic, and evaluation assumptions presented through HG002 and NA12878.PLoS computational biology · 2026Article
- A population-specific genomic reference panel for Taiwan: NHRI-RP-1.Journal of biomedical science · 2026Article
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- ITHindex: An integrated web-based platform for intratumor heterogeneity evaluation.PLOS digital health · 2026Article
- ClairS: a deep-learning method for long-read tumor-normal pair somatic small variant calling.Nature methods · 2026Article
- Benchmarking genomic variant calling tools in inbred mouse strains: recommendations and considerations.Genetics · 2026Article
- VCboost: reducing false positives in long-read variant calling for single-nucleotide polymorphism and indel detection in challenging genomic regions.Briefings in bioinformatics · 2026Article
- MRDsteer: quality-aware AI-driven closed-loop optimization enhances ctDNA-based minimal residual disease detection.Briefings in bioinformatics · 2026Article
- Article
- Short-Read Sequencing Benchmarking with Donor-Specific Assemblies.bioRxiv : the preprint server for biology · 2026Article
- Aardvark: sifting through differences in a mound of variants.Genome biology · 2026Article
- Benchmarking Q40 sequencing for sensitive and efficient detection of rare genomic variants.Genome biology · 2026Article
- Article
- Whole-genome sequencing with AVITI and NovaSeq X Plus reveals comparable performance with contextual biases.NAR genomics and bioinformatics · 2026Article
- A sibling study of variation in parental mutation rates.bioRxiv : the preprint server for biology · 2026Article
203 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
18 authors.
Funding
Abstract
Standardized benchmarking approaches are required to assess the accuracy of variants called from sequence data. Although variant-calling tools and the metrics used to assess their performance continue to improve, important challenges remain. Here, as part of the Global Alliance for Genomics and Health (GA4GH), we present a benchmarking framework for variant calling. We provide guidance on how to match variant calls with different representations, define standard performance metrics, and stratify performance by variant type and genome context. We describe limitations of high-confidence calls and regions that can be used as truth sets (for example, single-nucleotide variant concordance of two methods is 99.7% inside versus 76.5% outside high-confidence regions). Our web-based app enables comparison of variant calls against truth sets to obtain a standardized performance report. Our approach has been piloted in the PrecisionFDA variant-calling challenges to identify the best-in-class variant-calling methods within high-confidence regions. Finally, we recommend a set of best practices for using our tools and evaluating the results.
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Registered trials
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.