ArticleBioinformatics (Oxford, England)2025
Efficient seeding for error-prone sequences with SubseqHash2.
Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Hash functions in nucleotide sequence analysis.Genome research · 2026Review
- Sassy: fuzzy searching DNA sequences using SIMD.Bioinformatics (Oxford, England) · 2026Article
- Multi-context seeds enable fast and high-accuracy read mapping.Genome biology · 2026Article
- MELO-ED: learning locality-sensitive multi-embeddings for edit distance.bioRxiv : the preprint server for biology · 2025Article
- Sequence similarity estimation by random subsequence sketching.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
- Update of
Authors and funding
3 authors.
Funding
Abstract
motivationSeeding is an essential preparatory step for many fundamental computational tasks that require large-scale sequence comparison. Substring-based seeding methods such as kmers are ideal for sequences with low error rates but struggle to achieve high sensitivity while maintaining a reasonable precision for error-prone long reads. SubseqHash, a novel subsequence-based seeding method we recently developed, achieves superior accuracy to substring-based methods in seeding sequences with high mutation/error rates, while the only drawback is its computation speed.
resultsWe propose SubseqHash2, an improved algorithm that can compute multiple sets of seeds in one run, by defining k orders over all length-k subsequences and finding the optimal subsequence under each of the k orders in a single dynamic programming framework. The algorithm is further accelerated using single instruction, multiple data instructions for parallel computing. The design of SubseqHash2 also allows it to generate the same sets of seeds for a string and its reverse complement by using symmetric random tables. We demonstrate that SubseqHash2 drastically outperforms popular substring-based methods including kmers, minimizers, syncmers, and Strobemers for three fundamental applications. In read mapping, SubseqHash2 can generate adequate seed matches for aligning hard reads that minimap2 fails on. In sequence alignment, SubseqHash2 achieves high coverage of correct seeds and low coverage of incorrect seeds. In overlap detection, seeds produced by SubseqHash2 lead to more correct overlapping pairs at the same false-positive rate. In all experiments, SubseqHash2 achieves a 10-50× speedup over SubseqHash while maintaining nearly identical high accuracy. With all the algorithmic breakthroughs of SubseqHash2, we clear the path for the wide adoption of subsequence-based seeds in long-read analysis. AVAILABILITY AND IMPLEMENTATION: SubseqHash2 is available at https://github.com/Shao-Group/SubseqHash2 and have also been archived on Software Heritage (swh:1:dir:86738fc4b919eb6a9a26f7f533c25eb69f9a96d5).
Indexed as
Identifiers
What OpenQuestion holds
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.