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Modelling, Characterization of Data-Dependent and Process-Dependent Errors in DNA Data Storage

delete2023-05-01
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OA
AI
Y
Yixin Wang *
N
Noor-A-Rahim, Md.
E
Erry Gunawan
Y
Yong Liang Guan
C
Chueh Loo Poh
DOI:10.1109/TCBB.2022.3233914delete
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Abstract

Abstract

En 中文
Using DNA as the medium to store information has recently been recognized as a promising solution for long-term data storage. While several system prototypes have been demonstrated, the error characteristics in DNA data storage are discussed with limited content. Due to the data and process variations from experiment to experiment, the error variation and its effect on data recovery remain to be uncovered. To close the gap, we systematically investigate the storage channel, i.e., error characteristics in the storage process. In this work, we first propose a new concept named sequence corruption to unify the error characteristics into the sequence level, easing the channel analysis. Then we derived the formulations of the data imperfection at the decoder including both sequence loss and sequence corruption, revealing the decoding demand and monitoring the data recovery. Furthermore, we extensively explored several data-dependent unevenness observed in the base error patterns and studied a few potential factors and their impacts on the data imperfection at the decoder both theoretically and experimentally. The results presented here introduce a more comprehensive channel model and offer a new angle towards the data recovery issue in DNA data storage by further elucidating the error characteristics of the storage process.
Keywords:
Channel modelling
DNA data storage
error characterization
long-term storage

Journal

I
IEEE-ACM Transactions on Computational Biology and Bioinformatics
IF:
3.4
Papers:
3.3K
Citations:
6.4K

Organization

U
University College Cork
Scholars:
1.5W
Papers: 1.3W
Citations: 1.7W
N
Nanyang Technological University
Scholars:
4.9W
Papers: 4.8W
Citations: 8.1W
N
National University of Singapore
Scholars:
7.5W
Papers: 6.5W
Citations: 11.4W
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