GradHC: highly reliable gradual hash-based clustering for DNA storage systems

Dvir Ben Shabat, Adar Hadad, Avital Boruchovsky, Eitan Yaakobi

Research output: Contribution to journalArticlepeer-review

Abstract

Motivation: As data storage challenges grow and existing technologies approach their limits, synthetic DNA emerges as a promising storage solution due to its remarkable density and durability advantages. While cost remains a concern, emerging sequencing and synthetic technologies aim to mitigate it, yet introduce challenges such as errors in the storage and retrieval process. One crucial task in a DNA storage system is clustering numerous DNA reads into groups that represent the original input strands. Results: In this paper, we review different methods for evaluating clustering algorithms and introduce a novel clustering algorithm for DNA storage systems, named Gradual Hash-based clustering (GradHC). The primary strength of GradHC lies in its capability to cluster with excellent accuracy various types of designs, including varying strand lengths, cluster sizes (including extremely small clusters), and different error ranges. Benchmark analysis demonstrates that GradHC is significantly more stable and robust than other clustering algorithms previously proposed for DNA storage, while also producing highly reliable clustering results. Availability and implementation: https://github.com/bensdvir/GradHC.

Original languageEnglish
Article numberbtae274
JournalBioinformatics
Volume40
Issue number5
DOIs
StatePublished - 1 May 2024

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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