Comprehensive benchmark and architectural analysis of deep learning models for nanopore sequencing basecalling.

ABSTRACT:

Background

Nanopore-based DNA sequencing relies on basecalling the electric current signal. Basecalling requires neural networks to achieve competitive accuracies. To improve sequencing accuracy further, new models are continuously proposed with new architectures. However, benchmarking is currently not standardized, and evaluation metrics and datasets used are defined on a per publication basis, impeding progress in the field. This makes it impossible to distinguish data from model driven improvements.

Results

To standardize the process of benchmarking, we unified existing benchmarking datasets and defined a rigorous set of evaluation metrics. We benchmarked the latest seven basecaller models by recreating and analyzing their neural network architectures. Our results show th

SUBMITTER: Pages-Gallego M 

PROVIDER: S-EPMC10088207 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature