Scholarlink

<p>On-demand access to information encoded in nucleotides lies at the heart of DNA/RNA applications. However, contemporary methods for targeted retrieval using PCR amplification or bead-based extraction, rely on Watson-Crick base pairing and pre-defined primers, limiting dynamic decision-making whilst sequencing. We introduce SUSTag-ORCtrL, a nanopore-based system enables real-time, PCR-free random access to DNA-stored data by directly classifying raw ionic current signatures of 96 or 384-plex DNA molecular tags. Our framework combines SUSTag, a Bhattacharyya distance and incremental clustering enhanced molecular tag design (SUSTag) to minimize crosstalk, with an Optional-Reject Cnn-lstm deep learning model inspired by TRansfer-Learning (ORCtrL), designed to enhance adaptability to signal variability. SUSTag-ORCtrL achieves an intra-class weighted F1-score of 99.69% for 96-plex classification and 99.05% for 384-plex classification, surpassing existing molecular tagging systems. Domain adaptation using only120 minutes of new sequencing data ( ~ 200k reads) boosts the model performance from 87% to over 94%, achieving complete recovery of target data with minimal crosstalk in 10 min to 3 h. This system provides a scalable, low-latency solution for versatile DNA data access and holds promise for genomic and transcriptomic disease screening and the DNA-of-things.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Empowering low-crosstalk, dynamic-decision random access of DNA storage via 384-multiplexed nanopore signatures

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月17日

查看全文
  • Junyao Li,
  • Xuyang Zhao,
  • Qingyuan Fan,
  • Yanping Long,
  • Ronghui Liu,
  • Jixian Zhai,
  • Qing Pan,
  • Yi Li

摘要

On-demand access to information encoded in nucleotides lies at the heart of DNA/RNA applications. However, contemporary methods for targeted retrieval using PCR amplification or bead-based extraction, rely on Watson-Crick base pairing and pre-defined primers, limiting dynamic decision-making whilst sequencing. We introduce SUSTag-ORCtrL, a nanopore-based system enables real-time, PCR-free random access to DNA-stored data by directly classifying raw ionic current signatures of 96 or 384-plex DNA molecular tags. Our framework combines SUSTag, a Bhattacharyya distance and incremental clustering enhanced molecular tag design (SUSTag) to minimize crosstalk, with an Optional-Reject Cnn-lstm deep learning model inspired by TRansfer-Learning (ORCtrL), designed to enhance adaptability to signal variability. SUSTag-ORCtrL achieves an intra-class weighted F1-score of 99.69% for 96-plex classification and 99.05% for 384-plex classification, surpassing existing molecular tagging systems. Domain adaptation using only120 minutes of new sequencing data ( ~ 200k reads) boosts the model performance from 87% to over 94%, achieving complete recovery of target data with minimal crosstalk in 10 min to 3 h. This system provides a scalable, low-latency solution for versatile DNA data access and holds promise for genomic and transcriptomic disease screening and the DNA-of-things.