Comprehensive benchmark and architectural analysis of deep learning models for nanopore sequencing basecalling.
Journal:
Genome biology
Published Date:
Apr 11, 2023
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.