Anchor-Based Correction of Substitutions in Indexed Sets

Lenz, Andreas; Siegel, Paul H.; Wachter-Zeh, Antonia; Yaakobi, Eitan

doi:10.1109/isit.2019.8849523

Cited by 35 publications

(24 citation statements)

References 22 publications

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“…The improved insertion and deletion correction can extend the applicability of the framework to sequencing platforms such as nanopore sequencing [28] which have higher insertion and deletion error rates. Another interesting direction is to incorporate ideas from [18] and [29] to reduce the inefficiency of index error correction.…”

Section: Discussionmentioning

confidence: 99%

Improved read/write cost tradeoff in DNA-based data storage using LDPC codes

Chandak

Tatwawadi

Lau

et al. 2019

Preprint

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With the amount of data being stored increasing rapidly, there is significant interest in exploring alternative storage technologies. In this context, DNA-based storage systems can offer significantly higher storage densities (petabytes/gram) and durability (thousands of years) than current technologies. Specifically, DNA has been found to be stable over extended periods of time which has been demonstrated in the analysis of organisms long since extinct. Recent advances in DNA sequencing and synthesis pipelines have made DNA-based storage a promising candidate for the storage technology of the future.Recently, there have been multiple efforts in this direction, focusing on aspects such as error correction for synthesis/sequencing errors and erasure correction for handling missing sequences. The typical approach is to use separate codes for handling errors and erasures, but there is limited understanding of the efficiency of this framework. Furthermore, the existing techniques use short block-length codes and heavily rely on read consensus, both of which are known to be suboptimal in coding theory.In this work, we study the tradeoff between the writing and reading costs involved in DNA-based storage and propose a practical scheme to achieve an improved tradeoff between these quantities. Our scheme breaks with the traditional separation framework and instead uses a single large block-length LDPC code for both erasure and error correction. We also introduce novel techniques to handle insertion and deletion errors introduced by the synthesis process. For a range of writing costs, the proposed scheme achieves 30-40% lower reading costs than state-of-the-art techniques on experimental data obtained using array synthesis and Illumina sequencing.

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Section: Discussionmentioning

confidence: 99%

Improved read/write cost tradeoff in DNA-based data storage using LDPC codes

Chandak

Tatwawadi

Lau

et al. 2019

Preprint

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“…Recovering the data results in a set of traces arising from these seed strings and involves accurately determining a large fraction of the seed strings. Storing and retrieving a set of strings leads to interesting coding-theoretic problems as well [56], [59], [61], [63], [64], [68], [69].…”

Section: A Reconstruction Of Multiple Seed Stringsmentioning

confidence: 99%

Trace Reconstruction Problems in Computational Biology

Bhardwaj¹,

Pevzner²,

Rashtchian³

et al. 2020

Preprint

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The problem of reconstructing a string from its error-prone copies, the trace reconstruction problem, was introduced by Vladimir Levenshtein two decades ago. While there has been considerable theoretical work on trace reconstruction, practical solutions have only recently started to emerge in the context of two rapidly developing research areas: immunogenomics and DNA data storage. In immunogenomics, traces correspond to mutated copies of genes, with mutations generated naturally by the adaptive immune system. In DNA data storage, traces correspond to noisy copies of DNA molecules that encode digital data, with errors being artifacts of the data retrieval process. In this paper, we introduce several new trace generation models and open questions relevant to trace reconstruction for immunogenomics and DNA data storage, survey theoretical results on trace reconstruction, and highlight their connections to computational biology. Throughout, we discuss the applicability and shortcomings of known solutions and suggest future research directions.

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“…A different approach, potentially more applicable to in-vivo DNA storage, is to add redundancy in the form of constraints on the long information string, such that it can be uniquely reconstructed by knowledge of its substrings of a given length (or range of lengths). The combinatorial problem of recovering a sequence from its substrings has attracted attention in recent years [14]- [20], and coding schemes involving only these substrings (including the incidence frequency of each substring) were studied [10], [11], [21], [22].…”

Section: Introductionmentioning

confidence: 99%

On Codes for the Noisy Substring Channel

Yehezkeally,

Polyanskii

2021

Preprint

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We consider the problem of coding for the substring channel, in which information strings are observed only through their (multisets of) substrings. Because of applications to DNAbased data storage, due to DNA sequencing techniques, interest in this channel has renewed in recent years. In contrast to existing literature, we consider a noisy channel model, where information is subject to noise before its substrings are sampled, motivated by in-vivo storage.We study two separate noise models, substitutions or deletions. In both cases, we examine families of codes which may be utilized for error-correction and present combinatorial bounds. Through a generalization of the concept of repeat-free strings, we show that the added required redundancy due to this imperfect observation assumption is sublinear, either when the fraction of errors in the observed substring length is sufficiently small, or when that length is sufficiently long. This suggests that no asymptotic cost in rate is incurred by this channel model.

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Anchor-Based Correction of Substitutions in Indexed Sets

Cited by 35 publications

References 22 publications

Improved read/write cost tradeoff in DNA-based data storage using LDPC codes

Improved read/write cost tradeoff in DNA-based data storage using LDPC codes

Trace Reconstruction Problems in Computational Biology

On Codes for the Noisy Substring Channel

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