SOLQC: Synthetic Oligo Library Quality Control tool

Sabary, Omer; Orlev, Yoav; Shafir, Roy; Anavy, Leon; Yaakobi, Eitan; Yakhini, Zohar

doi:10.1093/bioinformatics/btaa740

Cited by 22 publications

(15 citation statements)

References 29 publications

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“…That is, our results include the algorithm from 33 with . Additionally, we also implemented the algorithm from 32 , with parameters of , , and , while for the data of the DNA storage experiments the substitution probability was taken from 29 . For further information about the parameters we used in the algorithms from 32 , 33 , the reader is referred to their original papers.…”

Section: Resultsmentioning

confidence: 99%

“…PacBio and Oxford Nanopore 20 , 23 , 26 , 27 . However, the error rates of these technologies can be higher, with deletions and substitutions as the most dominant errors 28 , 29 . Furthermore, there are emerging synthesis technologies, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…photolithographic light-directed synthesis 28 , 30 , in which the reported error rates are up to 25%-30%. A detailed characterization of the errors in the DNA-storage channel and more statistics about previous DNA-storage experiments can be found in 9 , 29 .…”

Section: Introductionmentioning

confidence: 99%

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Reconstruction algorithms for DNA-storage systems

Sabary,

Yucovich,

Shapira

et al. 2024

Sci Rep

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Motivated by DNA storage systems, this work presents the DNA reconstruction problem, in which a length-n string, is passing through the DNA-storage channel, which introduces deletion, insertion and substitution errors. This channel generates multiple noisy copies of the transmitted string which are called traces. A DNA reconstruction algorithm is a mapping which receives t traces as an input and produces an estimation of the original string. The goal in the DNA reconstruction problem is to minimize the edit distance between the original string and the algorithm’s estimation. In this work, we present several new algorithms for this problem. Our algorithms look globally on the entire sequence of the traces and use dynamic programming algorithms, which are used for the shortest common supersequence and the longest common subsequence problems, in order to decode the original string. Our algorithms do not require any limitations on the input and the number of traces, and more than that, they perform well even for error probabilities as high as 0.27. The algorithms have been tested on simulated data, on data from previous DNA storage experiments, and on a new synthesized dataset, and are shown to outperform previous algorithms in reconstruction accuracy.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reconstruction algorithms for DNA-storage systems

Sabary,

Yucovich,

Shapira

et al. 2024

Sci Rep

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“…The subset size was drawn randomly from N ( μ, σ = 100), where μ is a parameter. Errors in predetermined rates were introduced during the simulation of both DNA synthesis and sequencing, as expected in actual usage [21] (See Section 5.2.3 for details on the simulation runs). Reconstruction rates and Levenshtein distances are calculated throughout the simulation process, as described in Fig.…”

Section: Resultsmentioning

confidence: 99%

Efficient DNA-based data storage using shortmer combinatorial encoding

Preuss

Yakhini

Anavy

2021

Preprint

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Storage needs represent a significant burden on the economy and the environment. Some of this can potentially be offset by improved density molecular storage. The potential of using DNA for storing data is immense. DNA can be harnessed as a high density, durable archiving medium for compressing and storing the exponentially growing quantities of digital data that mankind generates. Several studies have demonstrated the potential of DNA-based data storage systems. These include exploration of different encoding and error correction schemes and the use of different technologies for DNA synthesis and sequencing. Recently, the use of composite DNA letters has been demonstrated to leverage the inherent redundancy in DNA based storage systems to achieve higher logical density, offering a more cost-effective approach. However, the suggested composite DNA approach is still limited due to its sensitivity to the stochastic nature of the process. Combinatorial assembly methods were also suggested to encode information on DNA in high density, while avoding the challenges of the stochastic system. These are based on enzynatic assembly processes for producing the synthetic DNA. In this paper, we propose a novel method to encode information into DNA molecules using combinatorial encoding and shortmer DNA synthesis, in compatibility with current chemical DNA synthesis technologies. Our approach is based on a set of easily distinguishable DNA shortmers serving as building blocks and allowing for near-zero error rates. We construct an extended combinatorial alphabet in which every letter is a subset of the set of building blocks. We suggest different combinatorial encoding schemes and explore their theoretical properties and practical implications in terms of error probabilities and required sequencing depth. To demonstrate the feasibility of our approach, we implemented an end-to-end computer simulation of a DNA-based storage system, using our suggested combinatorial encodings. We use simulations to assess the performance of the system and the effect of different parameters. Our simulations suggest that our combinatorial approach can potentially achieve up to 6.5-fold increase in the logical density over standard DNA based storage systems, with near zero reconstruction error. Implementing our approach at scale to perform actual synthesis, requires minimal alterations to current technologies. Our work thus suggests that the combination of combinatorial encoding with standard DNA chemical synthesis technologies can potentially improve current solutions, achieving scalable, efficient and cost- effective DNA-based storage.

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“…Codes correcting insertions/deletions recently attracted considerable attention due to their relevance to the special error behavior in DNA-based data storage [8], [40], [51], [64], [67], [69], [90], [91]. These codes are relevant for other applications in communications models.…”

Section: Introductionmentioning

confidence: 99%

On The Decoding Error Weight of One or Two Deletion Channels

Sabary¹,

Bar-Lev²,

Gershon³

et al. 2022

Preprint

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This paper tackles two problems that fall under the study of coding for insertions and deletions. These problems are motivated by several applications, among them is reconstructing strands in DNA-based storage systems. Under this paradigm, a word is transmitted over some fixed number of identical independent channels and the goal of the decoder is to output the transmitted word or some close approximation of it. The first part of this paper studies the deletion channel that deletes a symbol with some fixed probability p, while focusing on two instances of this channel. Since operating the maximum likelihood (ML) decoder in this case is computationally unfeasible, we study a slightly degraded version of this decoder for two channels and study its expected normalized distance. We observe that the dominant error patterns are deletions in the same run or errors resulting from alternating sequences. Based on these observations, it is derived that the expected normalized distance of the degraded ML decoder is roughly 3q−1 q−1 p 2 , when the transmitted word is any q-ary sequence and p is the channel's deletion probability. We also study the cases when the transmitted word belongs to the Varshamov Tenengolts (VT) code or the shifted VT code. Additionally, the insertion channel is studied as well as the case of two insertion channels. These theoretical results are verified by corresponding simulations. The second part of the paper studies optimal decoding for a special case of the deletion channel, referred by the k-deletion channel, which deletes exactly k symbols of the transmitted word uniformly at random. In this part, the goal is to understand how an optimal decoder operates in order to minimize the expected normalized distance. A full characterization of an efficient optimal decoder for this setup, reffered to as the maximum likelihood* (ML*) decoder, is given for a channel that deletes one or two symbols. For k = 1 it is shown that when the code is the entire space, the decoder is the lazy decoder which simply returns the channel output. Similarly, for k = 2 it is shown that the decoder acts as the lazy decoder in almost all cases and when the longest run is significantly long (roughly (2 − √ 2)n when n is the word length), it prolongs the longest run by one symbol.

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SOLQC: Synthetic Oligo Library Quality Control tool

Cited by 22 publications

References 29 publications

Reconstruction algorithms for DNA-storage systems

Reconstruction algorithms for DNA-storage systems

Efficient DNA-based data storage using shortmer combinatorial encoding

On The Decoding Error Weight of One or Two Deletion Channels

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