ArticleScientific reports2024
Reconstruction algorithms for DNA-storage systems.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Polus: a context-aware enhancement framework for DNA storage via transformer-based soft-decision decoding.Bioinformatics (Oxford, England) · 2026Article
- Highly biased DNA sequence reconstruction in DNA storage with multi-scale attention mechanism and contrast learning.Synthetic and systems biotechnology · 2026Article
- DVOUG enables robust DNA sequence assembly and reconstruction with a dynamic, variable-order graph.Cell reports methods · 2025Article
- Efficient trace reconstruction in DNA storage systems using bidirectional beam search.iScience · 2025Article
- Dna-storalator: a computational simulator for DNA data storage.BMC bioinformatics · 2025Article
- High-performance protocol for ultra-short DNA sequencing using Oxford Nanopore Technology (ONT).PloS one · 2025Article
- Robust multi-read reconstruction from noisy clusters using deep neural network for DNA storage.Computational and structural biotechnology journal · 2024Article
- GradHC: highly reliable gradual hash-based clustering for DNA storage systems.Bioinformatics (Oxford, England) · 2024Article
- DNAsmart: Multiple attribute ranking tool for DNA data storage systems.Computational and structural biotechnology journal · 2023Article
- Robust data storage in DNA by de Bruijn graph-based de novo strand assembly.Nature communications · 2022Article
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Authors and funding
4 authors.
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Abstract
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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