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Fractional Hitting Sets for Efficient and Lightweight Genomic Data Sketching

Authors Timothé Rouzé , Igor Martayan , Camille Marchet , Antoine Limasset



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Author Details

Timothé Rouzé
  • Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, F-59000 Lille, France
Igor Martayan
  • ENS Rennes, Univ. Rennes, France
Camille Marchet
  • Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, F-59000 Lille, France
Antoine Limasset
  • Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, F-59000 Lille, France

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Timothé Rouzé, Igor Martayan, Camille Marchet, and Antoine Limasset. Fractional Hitting Sets for Efficient and Lightweight Genomic Data Sketching. In 23rd International Workshop on Algorithms in Bioinformatics (WABI 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 273, pp. 15:1-15:27, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.WABI.2023.15

Abstract

The exponential increase in publicly available sequencing data and genomic resources necessitates the development of highly efficient methods for data processing and analysis. Locality-sensitive hashing techniques have successfully transformed large datasets into smaller, more manageable sketches while maintaining comparability using metrics such as Jaccard and containment indices. However, fixed-size sketches encounter difficulties when applied to divergent datasets. Scalable sketching methods, such as Sourmash, provide valuable solutions but still lack resource-efficient, tailored indexing. Our objective is to create lighter sketches with comparable results while enhancing efficiency. We introduce the concept of Fractional Hitting Sets, a generalization of Universal Hitting Sets, which uniformly cover a specified fraction of the k-mer space. In theory and practice, we demonstrate the feasibility of achieving such coverage with simple but highly efficient schemes. By encoding the covered k-mers as super-k-mers, we provide a space-efficient exact representation that also enables optimized comparisons. Our novel tool, SuperSampler, implements this scheme, and experimental results with real bacterial collections closely match our theoretical findings. In comparison to Sourmash, SuperSampler achieves similar outcomes while utilizing an order of magnitude less space and memory and operating several times faster. This highlights the potential of our approach in addressing the challenges presented by the ever-expanding landscape of genomic data.

Subject Classification

ACM Subject Classification
  • Applied computing → Bioinformatics
Keywords
  • k-mer
  • subsampling
  • sketching
  • Jaccard
  • containment
  • metagenomics

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References

  1. Clément Agret, Bastien Cazaux, and Antoine Limasset. Toward optimal fingerprint indexing for large scale genomics. In 22nd International Workshop on Algorithms in Bioinformatics, 2022. Google Scholar
  2. Daniel N Baker and Ben Langmead. Dashing: fast and accurate genomic distances with hyperloglog. Genome biology, 20(1):1-12, 2019. Google Scholar
  3. Daniel N Baker and Ben Langmead. Dashing 2: genomic sketching with multiplicities and locality-sensitive hashing. In RECOMB, 2023. Google Scholar
  4. Gaëtan Benoit, Pierre Peterlongo, Mahendra Mariadassou, Erwan Drezen, Sophie Schbath, Dominique Lavenier, and Claire Lemaitre. Multiple comparative metagenomics using multiset k-mer counting. PeerJ Computer Science, 2:e94, 2016. Google Scholar
  5. Grace A Blackwell, Martin Hunt, Kerri M Malone, Leandro Lima, Gal Horesh, Blaise TF Alako, Nicholas R Thomson, and Zamin Iqbal. Exploring bacterial diversity via a curated and searchable snapshot of archived dna sequences. PLoS biology, 19(11):e3001421, 2021. Google Scholar
  6. Andrei Z Broder. On the resemblance and containment of documents. In Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No. 97TB100171), pages 21-29. IEEE, 1997. Google Scholar
  7. Philippe Flajolet, Éric Fusy, Olivier Gandouet, and Frédéric Meunier. Hyperloglog: the analysis of a near-optimal cardinality estimation algorithm. In Discrete Mathematics and Theoretical Computer Science, pages 137-156. Discrete Mathematics and Theoretical Computer Science, 2007. Google Scholar
  8. Mahmudur Rahman Hera, N Tessa Pierce-Ward, and David Koslicki. Debiasing fracminhash and deriving confidence intervals for mutation rates across a wide range of evolutionary distances. bioRxiv, 2022. Google Scholar
  9. Guillaume Holley and Páll Melsted. Bifrost: highly parallel construction and indexing of colored and compacted de bruijn graphs. Genome biology, 21(1):1-20, 2020. Google Scholar
  10. Luiz Carlos Irber, Phillip T Brooks, Taylor E Reiter, N Tessa Pierce-Ward, Mahmudur Rahman Hera, David Koslicki, and C Titus Brown. Lightweight compositional analysis of metagenomes with fracminhash and minimum metagenome covers. bioRxiv, 2022. Google Scholar
  11. Heng Li. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics, 34(18):3094-3100, 2018. Google Scholar
  12. Yang Li et al. Mspkmercounter: a fast and memory efficient approach for k-mer counting. arXiv preprint, 2015. URL: https://arxiv.org/abs/1505.06550.
  13. Shaopeng Liu and David Koslicki. Cmash: fast, multi-resolution estimation of k-mer-based jaccard and containment indices. Bioinformatics, 38(Supplement_1):i28-i35, 2022. Google Scholar
  14. Camille Marchet, Mael Kerbiriou, and Antoine Limasset. Blight: efficient exact associative structure for k-mers. Bioinformatics, 37(18):2858-2865, 2021. Google Scholar
  15. Camille Marchet and Antoine Limasset. Scalable sequence database search using partitioned aggregated bloom comb-trees. In ISMB, 2023. URL: https://doi.org/10.1101/2022.02.11.480089.
  16. Frédéric Meunier, Olivier Gandouet, Éric Fusy, and Philippe Flajolet. Hyperloglog: the analysis of a near-optimal cardinality estimation algorithm. Discrete Mathematics & Theoretical Computer Science, 2007. Google Scholar
  17. Brian D Ondov, Gabriel J Starrett, Anna Sappington, Aleksandra Kostic, Sergey Koren, Christopher B Buck, and Adam M Phillippy. Mash screen: high-throughput sequence containment estimation for genome discovery. Genome biology, 20(1):1-13, 2019. Google Scholar
  18. Brian D Ondov, Todd J Treangen, Páll Melsted, Adam B Mallonee, Nicholas H Bergman, Sergey Koren, and Adam M Phillippy. Mash: fast genome and metagenome distance estimation using minhash. Genome biology, 17(1):1-14, 2016. Google Scholar
  19. Yaron Orenstein, David Pellow, Guillaume Marçais, Ron Shamir, and Carl Kingsford. Designing small universal k-mer hitting sets for improved analysis of high-throughput sequencing. PLoS computational biology, 13(10):e1005777, 2017. Google Scholar
  20. David Pellow, Lianrong Pu, Baris Ekim, Lior Kotlar, Bonnie Berger, Ron Shamir, and Yaron Orenstein. Efficient minimizer orders for large values of k using minimum decycling sets. bioRxiv, pages 2022-10, 2022. Google Scholar
  21. Giulio Ermanno Pibiri. Sparse and skew hashing of K-mers. Bioinformatics, 38(Supplement_1):i185-i194, June 2022. URL: https://doi.org/10.1093/bioinformatics/btac245.
  22. Giulio Ermanno Pibiri, Yoshihiro Shibuya, and Antoine Limasset. Locality-preserving minimal perfect hashing of k-mers. arXiv preprint, 2022. URL: https://arxiv.org/abs/2210.13097.
  23. N Tessa Pierce, Luiz Irber, Taylor Reiter, Phillip Brooks, and C Titus Brown. Large-scale sequence comparisons with sourmash. F1000Research, 8, 2019. Google Scholar
  24. Mihai Pǎtraşcu and Mikkel Thorup. The power of simple tabulation hashing. Journal of the ACM (JACM), 59(3):1-50, 2012. Google Scholar
  25. Amatur Rahman and Paul Medvedev. Representation of k-mer sets using spectrum-preserving string sets. In International Conference on Research in Computational Molecular Biology, pages 152-168. Springer, 2020. Google Scholar
  26. Michael Roberts, Wayne Hayes, Brian R Hunt, Stephen M Mount, and James A Yorke. Reducing storage requirements for biological sequence comparison. Bioinformatics, 20(18):3363-3369, 2004. Google Scholar
  27. Kristoffer Sahlin. Faster short-read mapping with strobemer seeds in syncmer space. bioRxiv, 2021. Google Scholar
  28. Saul Schleimer, Daniel S Wilkerson, and Alex Aiken. Winnowing: local algorithms for document fingerprinting. In Proceedings of the 2003 ACM SIGMOD international conference on Management of data, pages 76-85, 2003. Google Scholar
  29. Yun William Yu and Griffin M Weber. Hyperminhash: Minhash in loglog space. IEEE Transactions on Knowledge and Data Engineering, 34(1):328-339, 2020. Google Scholar
  30. XiaoFei Zhao. Bindash, software for fast genome distance estimation on a typical personal laptop. Bioinformatics, 35(4):671-673, 2019. Google Scholar
  31. Hongyu Zheng, Carl Kingsford, and Guillaume Marçais. Improved design and analysis of practical minimizers. Bioinformatics, 36(Supplement_1):i119-i127, 2020. Google Scholar
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