ArticleGenome research2023
Deriving confidence intervals for mutation rates across a wide range of evolutionary distances using FracMinHash.
Article in Genome research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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32 citing papers in PubMed, 35 citations in OpenAlex.
- The Oxytetracycline ProducerInternational journal of molecular sciences · 2026Article
- Estimation of substitution and indel rates via k-mer statistics.Algorithms for molecular biology : AMB · 2026Article
- A k-mer-based estimator of the substitution rate between repetitive sequences.Algorithms for molecular biology : AMB · 2026Article
- The gift of novelty: repeat-robust k-mer-based estimators of mutation rates.Bioinformatics (Oxford, England) · 2026Article
- The gift of novelty: repeat-robustbioRxiv : the preprint server for biology · 2026Article
- High-resolution metagenome assembly for modern long reads with myloasm.Nature biotechnology · 2026Article
- krepp: a k-mer-based maximum pseudo-likelihood method for estimating read distances and genome-wide phylogenetic placement.Genome biology · 2026Article
- GenCore: Genomic distance estimation using Locally Consistent Parsing.bioRxiv : the preprint server for biology · 2026Article
- MaxGeomHash: An Algorithm for Variable-Size Random Sampling of Distinct Elements.bioRxiv : the preprint server for biology · 2025Article
- Leveraging FracMinHash Containment for GenomicbioRxiv : the preprint server for biology · 2025Article
- High-resolution metagenome assembly for modern long reads with myloasm.bioRxiv : the preprint server for biology · 2025Article
- Estimation of substitution and indel rates viaAlgorithms in bioinformatics : ... International Workshop, WABI ..., proceedings. WABI (Workshop) · 2025Article
- Rapid species-level metagenome profiling and containment estimation with sylph.Nature biotechnology · 2025Article
- Estimation of substitution and indel rates viabioRxiv : the preprint server for biology · 2025Article
- Starship giant transposable elements cluster by host taxonomy using k-mer-based phylogenetics.G3 (Bethesda, Md.) · 2025Article
- Estimating similarity and distance using FracMinHash.Algorithms for molecular biology : AMB · 2025Article
- A k-mer-Based Estimator of the Substitution Rate Between Repetitive Sequences.Algorithms in bioinformatics : ... International Workshop, WABI ..., proceedings. WABI (Workshop) · 2025Article
- Article
- GSearch: ultra-fast and scalable genome search by combining K-mer hashing with hierarchical navigable small world graphs.Nucleic acids research · 2024Article
- Metagenomic functional profiling: to sketch or not to sketch?Bioinformatics (Oxford, England) · 2024Article
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Authors and funding
3 authors at 2 institutions in 1 country.
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Abstract
Sketching methods offer computational biologists scalable techniques to analyze data sets that continue to grow in size. MinHash is one such technique to estimate set similarity that has enjoyed recent broad application. However, traditional MinHash has previously been shown to perform poorly when applied to sets of very dissimilar sizes. FracMinHash was recently introduced as a modification of MinHash to compensate for this lack of performance when set sizes differ. This approach has been successfully applied to metagenomic taxonomic profiling in the widely used tool sourmash gather. Although experimental evidence has been encouraging, FracMinHash has not yet been analyzed from a theoretical perspective. In this paper, we perform such an analysis to derive various statistics of FracMinHash, and prove that although FracMinHash is not unbiased (in the sense that its expected value is not equal to the quantity it attempts to estimate), this bias is easily corrected for both the containment and Jaccard index versions. Next, we show how FracMinHash can be used to compute point estimates as well as confidence intervals for evolutionary mutation distance between a pair of sequences by assuming a simple mutation model. We also investigate edge cases in which these analyses may fail to effectively warn the users of FracMinHash indicating the likelihood of such cases. Our analyses show that FracMinHash estimates the containment of a genome in a large metagenome more accurately and more precisely compared with traditional MinHash, and the point estimates and confidence intervals perform significantly better in estimating mutation distances.
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