Evidence map›Paper›PMID 40705438›Full record

ArticleBioinformatics (Oxford, England)2025

Efficient seeding for error-prone sequences with SubseqHash2.

Xiang Li, Ke Chen, Mingfu Shao

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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.

2 · The registry

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.

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Sassy: fuzzy searching DNA sequences using SIMD.Bioinformatics (Oxford, England) · 2026
    Article
  3. Article
  4. MELO-ED: learning locality-sensitive multi-embeddings for edit distance.bioRxiv : the preprint server for biology · 2025
    Article
  5. Sequence similarity estimation by random subsequence sketching.bioRxiv : the preprint server for biology · 2025
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Xiang LiDepartment of Computer Science and Engineering, The Pennsylvania State University, Pennsylvania, PA 16802, United States.ORCID 0000-0001-8499-4740
Ke ChenDepartment of Computer Science and Engineering, The Pennsylvania State University, Pennsylvania, PA 16802, United States.ORCID 0000-0001-5470-6621
Mingfu ShaoDepartment of Computer Science and Engineering, The Pennsylvania State University, Pennsylvania, PA 16802, United States.ORCID 0000-0001-6112-5139

Funding

Computational Methods for Assembling Multiple RNA-seq SamplesR01HG011065 · NHGRI · PENNSYLVANIA STATE UNIVERSITY, THE · PI SHAO, MINGFU · 2021 to 2025
$1.8M
NHGRI NIH HHS R01 HG011065US National Institutes of Health R01HG011065US National Science Foundation 2019797US National Science Foundation 2145171
6 · The paper itself

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

AlgorithmsComputational BiologySequence AlignmentSequence Analysis, DNASoftware

Identifiers

PMID40705438
PMCPMC12317743

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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.