ArticleBioinformatics (Oxford, England)2023
How to optimally sample a sequence for rapid analysis.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- High-resolution metagenome assembly for modern long reads with myloasm.Nature biotechnology · 2026Article
- High-resolution metagenome assembly for modern long reads with myloasm.bioRxiv : the preprint server for biology · 2025Article
- Sequence similarity estimation by random subsequence sketching.bioRxiv : the preprint server for biology · 2025Article
- Creating and Using Minimizer Sketches in Computational Genomics.Journal of computational biology : a journal of computational molecular cell biology · 2023Review
- Proving sequence aligners can guarantee accuracy in almostGenome research · 2023Article
- Entropy predicts sensitivity of pseudorandom seeds.Genome research · 2023Article
- Locality-preserving minimal perfect hashing of k-mers.Bioinformatics (Oxford, England) · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
motivationWe face an increasing flood of genetic sequence data, from diverse sources, requiring rapid computational analysis. Rapid analysis can be achieved by sampling a subset of positions in each sequence. Previous sequence-sampling methods, such as minimizers, syncmers and minimally overlapping words, were developed by heuristic intuition, and are not optimal.
resultsWe present a sequence-sampling approach that provably optimizes sensitivity for a whole class of sequence comparison methods, for randomly evolving sequences. It is likely near-optimal for a wide range of alignment-based and alignment-free analyses. For real biological DNA, it increases specificity by avoiding simple repeats. Our approach generalizes universal hitting sets (which guarantee to sample a sequence at least once) and polar sets (which guarantee to sample a sequence at most once). This helps us understand how to do rapid sequence analysis as accurately as possible. AVAILABILITY AND IMPLEMENTATION: Source code is freely available at https://gitlab.com/mcfrith/noverlap. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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