R&D: GradHC, Highly Reliable Gradual Hash-Based Clustering for DNA Storage Systems
Benchmark analysis demonstrates that GradHC is more stable and robust than other clustering algorithms previously proposed for DNA storage, while also producing highly reliable clustering results.
This is a Press Release edited by StorageNewsletter.com on January 22, 2024 at 2:01 pmbioRxiv has published an article written by Dvir Ben Shabat, Adar Hadad, Avital Boruchovsky, and Eitan Yaakobi, Computer Science Department, Technion, Haifa, 3200003, Israel.
Abstract: “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 in a DNA storage system is clustering numerous DNA reads into groups that represent the original input strands. 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.“
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