BaseNet: A transformer-based toolkit for nanopore sequencing signal decoding.

ABSTRACT: Nanopore sequencing provides a rapid, convenient and high-throughput solution for nucleic acid sequencing. Accurate basecalling in nanopore sequencing is crucial for downstream analysis. Traditional approaches such as Hidden Markov Models (HMM), Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN) have improved basecalling accuracy but there is a continuous need for higher accuracy and reliability. In this study, we introduce BaseNet (https://github.com/liqingwen98/BaseNet), an open-source toolkit that utilizes transformer models for advanced signal decoding in nanopore sequencing. BaseNet incorporates both autoregressive and non-autoregressive transformer-based decoding mechanisms, offering state-of-the-art algorithms freely accessible for future improvement. Our resea

SUBMITTER: Li Q 

PROVIDER: S-EPMC11465205 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature