Reconstruction Algorithms for DNA-Storage Systems

Sabary, Omer; Yucovich, Alexander; Shapira, G.; Yaakobi, Eitan

doi:10.1101/2020.09.16.300186

Cited by 13 publications

(16 citation statements)

References 48 publications

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“…and the optimal X is given by the symbolwise MAP estimate. Choosing X to minimize the edit distance has also been considered in [28], [33], [34]. For hard-decision decoding of an outer code defined by M symbols, the expected Hamming error rate is likewise minimized by choosing M to be the symbolwise MAP estimate of M.…”

Section: E Performance Metrics and Information Ratesmentioning

confidence: 99%

Trellis BMA: Coded Trace Reconstruction on IDS Channels for DNA Storage

Srinivasavaradhan¹,

Gopi²,

Pfister³

et al. 2021

Preprint

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Sequencing a DNA strand, as part of the read process in DNA storage, produces multiple noisy copies which can be combined to produce better estimates of the original strand; this is called trace reconstruction. One can reduce the error rate further by introducing redundancy in the write sequence and this is called coded trace reconstruction. In this paper, we model the DNA storage channel as an insertion-deletion-substitution (IDS) channel and design both encoding schemes and low-complexity decoding algorithms for coded trace reconstruction.We introduce Trellis BMA, a new reconstruction algorithm whose complexity is linear in the number of traces, and compare its performance to previous algorithms. Our results show that it reduces the error rate on both simulated and experimental data. The performance comparisons in this paper are based on a new dataset of traces that will be publicly released with the paper. Our hope is that this dataset will enable research progress by allowing objective comparisons between candidate algorithms.

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Section: E Performance Metrics and Information Ratesmentioning

confidence: 99%

Trellis BMA: Coded Trace Reconstruction on IDS Channels for DNA Storage

Srinivasavaradhan¹,

Gopi²,

Pfister³

et al. 2021

Preprint

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“…Among other applications, the input entropy is relevant to DNA reconstruction algorithms, since in several of these algorithms such as [6], [37], [43], there is a limitation on the number of noisy copies that are considered by the algorithm's decoder, due to design restrictions and run-time considerations. Therefore, in case the number of received noisy copies is greater than this limitation, a subset of these copies should be considered.…”

Section: Introductionmentioning

confidence: 99%

The Input and Output Entropies of the $k$-Deletion/Insertion Channel

Shubhransh¹,

Sabary²,

Bar-Lev³

et al. 2022

Preprint

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This paper is eligible for the Jack Keil Wolf ISIT Student Paper Award. The channel output entropy of a transmitted word is the entropy of the possible channel outputs and similarly the input entropy of a received word is the entropy of all possible transmitted words. The goal of this work is to study these entropy values for the k-deletion, k-insertion channel, where exactly k symbols are deleted, inserted in the transmitted word, respectively. If all possible words are transmitted with the same probability then studying the input and output entropies is equivalent. For both the 1-insertion and 1-deletion channels, it is proved that among all words with a fixed number of runs, the input entropy is minimized for words with a skewed distribution of their run lengths and it is maximized for words with a balanced distribution of their run lengths. Among our results, we establish a conjecture by Atashpendar et al. which claims that for the binary 1-deletion channel, the input entropy is maximized for the alternating words.

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“…While these works mainly derive asymptotic results, here we discuss a fixed number of short, finite-length received sequences over a channel that additionally allows insertions and substitutions. More recently, the trace reconstruction problem has also been formulated for a fixed number of sequences with a larger focus on algorithmic aspects [15], [16]. However, these works consider only uncoded sequences, while we are interested in the case of coded transmission.…”

Section: Introductionmentioning

confidence: 99%

Concatenated Codes for Recovery From Multiple Reads of DNA Sequences

Lenz

Maarouf

Welter

et al. 2021

2020 IEEE Information Theory Workshop (ITW)

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Decoding sequences that stem from multiple transmissions of a codeword over an insertion, deletion, and substitution channel is a critical component of efficient deoxyribonucleic acid (DNA) data storage systems. In this paper, we consider a concatenated coding scheme with an outer low-density paritycheck code and either an inner convolutional code or a block code. We propose two new decoding algorithms for inference from multiple received sequences, both combining the inner code and channel to a joint hidden Markov model to infer symbolwise a posteriori probabilities (APPs). The first decoder computes the exact APPs by jointly decoding the received sequences, whereas the second decoder approximates the APPs by combining the results of separately decoded received sequences. Using the proposed algorithms, we evaluate the performance of decoding multiple received sequences by means of achievable information rates and Monte-Carlo simulations. We show significant performance gains compared to a single received sequence.

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Reconstruction Algorithms for DNA-Storage Systems

Cited by 13 publications

References 48 publications

Trellis BMA: Coded Trace Reconstruction on IDS Channels for DNA Storage

Trellis BMA: Coded Trace Reconstruction on IDS Channels for DNA Storage

The Input and Output Entropies of the $k$-Deletion/Insertion Channel

Concatenated Codes for Recovery From Multiple Reads of DNA Sequences

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