SOLQC : Synthetic Oligo Library Quality Control Tool

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

doi:10.1101/840231

Cited by 4 publications

(8 citation statements)

References 29 publications

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“…In this section we present the results of the tested algorithms on data from previous DNA storage experiments [20,24,40]. The clustering of these data sets was made by the SOLQC tool [45]. We performed (a) k 1 succ values by the error probability for t = 10.…”

Section: Results On Data From Dna Storage Experimentsmentioning

confidence: 99%

“…PacBio and Oxford Nanopore [10,36,40,53]. However, the error rates of these technologies can be significantly higher and can grow up to 30%, with deletions and substitutions as the most dominant errors [45].…”

Section: Dna Storage and Dna Errorsmentioning

confidence: 99%

“…A detailed characterization of the errors in the DNA-storage channel and more statistics about previous DNA-storage experiments can be found in [25,45].…”

Section: Dna Storage and Dna Errorsmentioning

confidence: 99%

“…The results of the hybrid algorithm on the simulated data from the experiment A had an edit error rate of 0.02 and in 330 seconds for the first experiment of the simulated data. Since in data from previous DNA storage experiments the variance in the cluster size and in the error rates can be really high [45], we added an additional condition to the hybrid algorithm, so it invokes the algorithm from [23] if the cluster is of size 20 or larger, or if the absolute distance of the difference between the average length of the traces in the cluster and the design length is larger than 10% of the design length. The hybrid algorithm reconstructed the full data set of [24] (experiment B) in 37 seconds and presented error rate of 0.000676.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…The parameters we used for the window size w of the algorithm from[23] were 2 w 4, and we present the results for all of them 5. The parameters we used for the algorithm from[52] were = 5, δ = (1 + ps)/2, r = 2 and γ = 3/4, while for the data of the DNA storage experiments the substitution probability ps was taken from[45].…”

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confidence: 99%

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Reconstruction Algorithms for DNA-Storage Systems

Sabary

Yucovich

Shapira

et al. 2020

Preprint

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In the trace reconstruction problem a length-n string x yields a collection of noisy copies, called traces, y1, …, yt where each yi is independently obtained from x by passing through a deletion channel, which deletes every symbol with some fixed probability. The main goal under this paradigm is to determine the required minimum number of i.i.d traces in order to reconstruct x with high probability. The trace reconstruction problem can be extended to the model where each trace is a result of x passing through a deletion-insertion-substitution channel, which introduces also insertions and substitutions. Motivated by the storage channel of DNA, this work is focused on another variation of the trace reconstruction problem, which is referred by the DNA reconstruction problem. A DNA reconstruction algorithm is a mapping which receives t traces y1, …, yt as an input and produces , an estimation of x. The goal in the DNA reconstruction problem is to minimize the edit distance between the original string and the algorithm’s estimation. For the deletion channel case, the problem is referred by the deletion DNA reconstruction problem and the goal is to minimize the Levenshtein distance .In this work, we present several new algorithms for these reconstruction problems. 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 sequence. 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 as well as on data from previous DNA experiments and are shown to outperform all previous algorithms.

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Section: Results On Data From Dna Storage Experimentsmentioning

confidence: 99%

Section: Dna Storage and Dna Errorsmentioning

confidence: 99%

“…A detailed characterization of the errors in the DNA-storage channel and more statistics about previous DNA-storage experiments can be found in [25,45].…”

Section: Dna Storage and Dna Errorsmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

mentioning

confidence: 99%

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Reconstruction Algorithms for DNA-Storage Systems

Sabary

Yucovich

Shapira

et al. 2020

Preprint

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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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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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SOLQC : Synthetic Oligo Library Quality Control Tool

Cited by 4 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

Trace Reconstruction Problems in Computational Biology

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