Jaeho Jeong;Hosung Park;Hee-Youl Kwak;Jong-Seon No;Hahyeon Jeon;Jeong Wook Lee;Jae-Won Kim
{"title":"Iterative Soft Decoding Algorithm for DNA Storage Using Quality Score and Redecoding","authors":"Jaeho Jeong;Hosung Park;Hee-Youl Kwak;Jong-Seon No;Hahyeon Jeon;Jeong Wook Lee;Jae-Won Kim","doi":"10.1109/TNB.2023.3284406","DOIUrl":null,"url":null,"abstract":"Ever since deoxyribonucleic acid (DNA) was considered as a next-generation data-storage medium, lots of research efforts have been made to correct errors occurred during the synthesis, storage, and sequencing processes using error correcting codes (ECCs). Previous works on recovering the data from the sequenced DNA pool with errors have utilized hard decoding algorithms based on a majority decision rule. To improve the correction capability of ECCs and robustness of the DNA storage system, we propose a new iterative soft decoding algorithm, where soft information is obtained from FASTQ files and channel statistics. In particular, we propose a new formula for log-likelihood ratio (LLR) calculation using quality scores (Q-scores) and a redecoding method which may be suitable for the error correction and detection in the DNA sequencing area. Based on the widely adopted encoding scheme of the fountain code structure proposed by Erlich et al., we use three different sets of sequenced data to show consistency for the performance evaluation. The proposed soft decoding algorithm gives 2.3%\n<inline-formula> <tex-math>$\\sim $ </tex-math></inline-formula>\n7.0% improvement of the reading number reduction compared to the state-of-the-art decoding method and it is shown that it can deal with erroneous sequenced oligo reads with insertion and deletion errors.","PeriodicalId":13264,"journal":{"name":"IEEE Transactions on NanoBioscience","volume":"23 1","pages":"81-90"},"PeriodicalIF":3.7000,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on NanoBioscience","FirstCategoryId":"99","ListUrlMain":"https://ieeexplore.ieee.org/document/10147330/","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Ever since deoxyribonucleic acid (DNA) was considered as a next-generation data-storage medium, lots of research efforts have been made to correct errors occurred during the synthesis, storage, and sequencing processes using error correcting codes (ECCs). Previous works on recovering the data from the sequenced DNA pool with errors have utilized hard decoding algorithms based on a majority decision rule. To improve the correction capability of ECCs and robustness of the DNA storage system, we propose a new iterative soft decoding algorithm, where soft information is obtained from FASTQ files and channel statistics. In particular, we propose a new formula for log-likelihood ratio (LLR) calculation using quality scores (Q-scores) and a redecoding method which may be suitable for the error correction and detection in the DNA sequencing area. Based on the widely adopted encoding scheme of the fountain code structure proposed by Erlich et al., we use three different sets of sequenced data to show consistency for the performance evaluation. The proposed soft decoding algorithm gives 2.3%
$\sim $
7.0% improvement of the reading number reduction compared to the state-of-the-art decoding method and it is shown that it can deal with erroneous sequenced oligo reads with insertion and deletion errors.
期刊介绍:
The IEEE Transactions on NanoBioscience reports on original, innovative and interdisciplinary work on all aspects of molecular systems, cellular systems, and tissues (including molecular electronics). Topics covered in the journal focus on a broad spectrum of aspects, both on foundations and on applications. Specifically, methods and techniques, experimental aspects, design and implementation, instrumentation and laboratory equipment, clinical aspects, hardware and software data acquisition and analysis and computer based modelling are covered (based on traditional or high performance computing - parallel computers or computer networks).