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

作者：Yusen Lin, Yongjun Zhang, Hang Sun, Hang Jiang, Zhao Xing, Xiaojuan Teng, Jingxia Lin, Bowen Shu, Hao Sun, Yuhui Liao, Jiajian Zhou · 发表于：Briefings in Bioinformatics · 年份：2023 · DOI：10.1093/bib/bbad499 · 被引用次数：13 · 研究领域：Genomics and Phylogenetic Studies、Nanopore and Nanochannel Transport Studies、Domain Adaptation and Few-Shot Learning

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.