NanoDeep: a deep learning framework for nanopore adaptive sampling on microbial sequencing

Nanopore sequencers can enrich or deplete the targeted DNA molecules in a library by reversing the voltage across individual nanopores. However, it requires substantial computational resources to achieve rapid operations in parallel at read-time sequencing. We present a deep learning framework, Nano...

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Bibliographic Details
Published in:Briefings in bioinformatics 2023-11, Vol.25 (1)
Main Authors: Lin, Yusen, Zhang, Yongjun, Sun, Hang, Jiang, Hang, Zhao, Xing, Teng, Xiaojuan, Lin, Jingxia, Shu, Bowen, Sun, Hao, Liao, Yuhui, Zhou, Jiajian
Format: Article
Language:English
Subjects:
Deep Learning
Gene Library
Genome, Bacterial
Humans
Nanopore Sequencing
Nanopores
Problem Solving Protocol
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Summary:Nanopore sequencers can enrich or deplete the targeted DNA molecules in a library by reversing the voltage across individual nanopores. However, it requires substantial computational resources to achieve rapid operations in parallel at read-time sequencing. We present a deep learning framework, NanoDeep, to overcome these limitations by incorporating convolutional neural network and squeeze and excitation. We first showed that the raw squiggle derived from native DNA sequences determines the origin of microbial and human genomes. Then, we demonstrated that NanoDeep successfully classified bacterial reads from the pooled library with human sequence and showed enrichment for bacterial sequence compared with routine nanopore sequencing setting. Further, we showed that NanoDeep improves the sequencing efficiency and preserves the fidelity of bacterial genomes in the mock sample. In addition, NanoDeep performs well in the enrichment of metagenome sequences of gut samples, showing its potential applications in the enrichment of unknown microbiota. Our toolkit is available at https://github.com/lysovosyl/NanoDeep.
ISSN:1467-5463
1477-4054
DOI:10.1093/bib/bbad499