DeepSelectNet: deep neural network based selective sequencing for oxford nanopore sequencing.

ABSTRACT:

Background

Nanopore sequencing allows selective sequencing, the ability to programmatically reject unwanted reads in a sample. Selective sequencing has many present and future applications in genomics research and the classification of species from a pool of species is an example. Existing methods for selective sequencing for species classification are still immature and the accuracy highly varies depending on the datasets. For the five datasets we tested, the accuracy of existing methods varied in the range of [Formula: see text] 77 to 97% (average accuracy < 89%). Here we present DeepSelectNet, an accurate deep-learning-based method that can directly classify nanopore current signals belonging to a particular species. DeepSelectNet utilizes novel data preprocessing techniques and

SUBMITTER: Senanayake A 

PROVIDER: S-EPMC9883605 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature