ArticleBioinformatics (Oxford, England)2020
Improved design and analysis of practical minimizers.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- RSHash: a fast and space-efficient hash table for k-mers.Bioinformatics (Oxford, England) · 2026Article
- Optimizing sparse and skew hashing: faster k-mer dictionaries.Bioinformatics (Oxford, England) · 2026Article
- chewBBACA 3: lowering the barrier for scalable and detailed whole- and core-genome multilocus sequence typing.Genome medicine · 2026Article
- Optimizing sparse and skew hashing: fasterbioRxiv : the preprint server for biology · 2026Article
- Kaminari: a frugal colored index for approximateBioinformatics advances · 2026Article
- Density-reducing Jaccard estimators for sketch-based long read applications.BMC bioinformatics · 2025Article
- GreedyMini: generating low-density DNA minimizers.Bioinformatics (Oxford, England) · 2025Article
- The open-closed mod-minimizer algorithm.Algorithms for molecular biology : AMB · 2025Article
- Fractional hitting sets for efficient multiset sketching.Algorithms for molecular biology : AMB · 2025Article
- Taming large-scale genomic analyses via sparsified genomics.Nature communications · 2025Article
- K2R: Tinted de Bruijn graphs implementation for efficient read extraction from sequencing datasets.Bioinformatics advances · 2025Article
- Construction of edit-distance graphs for large sets of short reads through minimizer-bucketing.Bioinformatics advances · 2025Article
- A near-tight lower bound on the density of forward sampling schemes.Bioinformatics (Oxford, England) · 2024Article
- A near-tight lower bound on the density of forward sampling schemes.bioRxiv : the preprint server for biology · 2024Article
- k-nonical space: sketching with reverse complements.Bioinformatics (Oxford, England) · 2024Article
- When less is more: sketching with minimizers in genomics.Genome biology · 2024Review
- Sketching Methods with Small Window Guarantee Using Minimum Decycling Sets.Journal of computational biology : a journal of computational molecular cell biology · 2024Article
- Conway-Bromage-Lyndon (CBL): an exact, dynamic representation of k-mer sets.Bioinformatics (Oxford, England) · 2024Article
- Highly efficient clustering of long-read transcriptomic data with GeLuster.Bioinformatics (Oxford, England) · 2024Article
- Density and Conservation Optimization of the Generalized Masked-Minimizer Sketching Scheme.Journal of computational biology : a journal of computational molecular cell biology · 2024Article
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3 authors.
Funding
Abstract
motivationMinimizers are methods to sample k-mers from a string, with the guarantee that similar set of k-mers will be chosen on similar strings. It is parameterized by the k-mer length k, a window length w and an order on the k-mers. Minimizers are used in a large number of softwares and pipelines to improve computation efficiency and decrease memory usage. Despite the method's popularity, many theoretical questions regarding its performance remain open. The core metric for measuring performance of a minimizer is the density, which measures the sparsity of sampled k-mers. The theoretical optimal density for a minimizer is 1/w, provably not achievable in general. For given k and w, little is known about asymptotically optimal minimizers, that is minimizers with density O(1/w).
resultsWe derive a necessary and sufficient condition for existence of asymptotically optimal minimizers. We also provide a randomized algorithm, called the Miniception, to design minimizers with the best theoretical guarantee to date on density in practical scenarios. Constructing and using the Miniception is as easy as constructing and using a random minimizer, which allows the design of efficient minimizers that scale to the values of k and w used in current bioinformatics software programs. AVAILABILITY AND IMPLEMENTATION: Reference implementation of the Miniception and the codes for analysis can be found at https://github.com/kingsford-group/miniception. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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