Clustering-Correcting Codes

Shinkar, Tal; Yaakobi, Eitan; Lenz, Andreas; Wachter-Zeh, Antonia

doi:10.1109/tit.2021.3127174

Cited by 8 publications

(4 citation statements)

References 28 publications

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“…However, Guanjin et al [25] have shown that this algorithm (as well as other density-based clustering algorithms) is impractical for clustering hundreds of thousands of noisy copies due to computational constraints. Shinkar et al [26] proposed an index-based clustering algorithm and introduced a novel coding scheme called clustering-correcting codes, which uses both the data and index fields for the encoding process. Nonetheless, the success of this algorithm is guaranteed only under the strong assumption that the majority of strands in every generated cluster are derived from the same original strand.…”

Section: Dna Clusteringmentioning

confidence: 99%

GradHC: Highly Reliable Gradual Hash-based Clustering for DNA Storage Systems

Shabat,

Hadad,

Boruchovsky

et al. 2023

Preprint

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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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Section: Dna Clusteringmentioning

confidence: 99%

GradHC: Highly Reliable Gradual Hash-based Clustering for DNA Storage Systems

Shabat,

Hadad,

Boruchovsky

et al. 2023

Preprint

Self Cite

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“…The output of this algorithm is a set of clusters, each corresponding to some unknown original oligo. While we use our solution, we would like to mention that any other read clustering solution [42], [43] can also be used, and is orthogonal to the work presented here.…”

Section: B Read Pipelinementioning

confidence: 99%

OligoArchive-DSM: Columnar Design for Error-Tolerant Database Archival using Synthetic DNA

Marinelli

Yan

Magnone

et al. 2022

Preprint

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The surge in demand for cost-effective, durable long-term archival media, coupled with density limitations of contemporary magnetic media, has resulted in synthetic DNA emerging as a promising new alternative. Today, the limiting factor for DNA-based data archival is the cost of writing (synthesis) and reading (sequencing) DNA. Newer techniques that reduce the cost often do so at the expense of reliability, as they introduce complex, technology-specific error patterns. In order to deal with such errors, it is important to design efficient pipelines that can carefully use redundancy to mask errors without amplifying overall cost. In this paper, we present OligoArchive-DSM (OA-DSM), an end-to-end DNA archival pipeline that can provide error-tolerant data storage at low read/write costs. Central to OA-DSM is a database-inspired columnar encoding technique that makes it possible to improve efficiency by enabling integrated decoding and consensus calling during data restoration.

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“…The codes are defined in GF (4) and have a rate of 1 3 . In [17], transmission over a noisy shuffling-sampling channel is considered and the indexes are designed such that if the distance between two particular segments is small, the distance between their corresponding indexes is large. This property enables the receiver to correctly cluster the channel reads, i.e., to correctly identify the noisy segments that correspond to the same original segment.…”

Section: Introductionmentioning

confidence: 99%

A Practical Indexing Scheme for Noisy Shuffling Channels Using Cosets of Polar Codes

Haghighat,

Duman

2024

Preprint

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Clustering-Correcting Codes

Cited by 8 publications

References 28 publications

GradHC: Highly Reliable Gradual Hash-based Clustering for DNA Storage Systems

GradHC: Highly Reliable Gradual Hash-based Clustering for DNA Storage Systems

OligoArchive-DSM: Columnar Design for Error-Tolerant Database Archival using Synthetic DNA

A Practical Indexing Scheme for Noisy Shuffling Channels Using Cosets of Polar Codes

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