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

ABSTRACT: 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 se

SUBMITTER: Lin Y 

PROVIDER: S-EPMC10772945 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature