Bream: an open-source deep learning framework for simultaneous base calling and DNA methylation detection on novel nanopore sequencing platforms
Year: 2026
Type: article
Abstract: Nanopore sequencing enables the simultaneous detection of genetic sequences and DNA modifications, yet the development of accurate, open-source computational models for these tasks, particularly for non-ONT platforms, remains challenging. To address this, we developed Bream, an open-source deep learning framework that integrates a convolutional neural network with a reverse long short-term memory network for base calling and a bidirectional LSTM with an attention mechanism for methylation detection. We trained and evaluated Bream on datasets from A. thaliana, O. sativa , and D. melanogaster generated using a novel nanopore sequencing platform (Qitan Technology’s QCell-384) featuring engineered helicase and nanopore proteins. The framework achieved base-calling accuracies between 89.38% and 91.83%, comparable to ONT’s R9.4 platform, and demonstrated high-performance methylation detection, with an AUC-ROC of 0.98 on a D. melanogaster dataset. Furthermore, its estimates of whole-genome CpG methylation frequency showed strong agreement (Pearson’s r ≥ 0.96) with bisulfite sequencing data across species. These results demonstrate Bream as a powerful, transparent, and adaptable tool that facilitates simultaneous base calling and methylation detection on emerging nanopore sequencing platforms, thereby advancing open innovation in the field.
Source: Frontiers in Genetics
Authors: Hui-Cong Yao, Bo Wu, Chen-Liang Ye, Xin Bai, He-Xu Chen, Geng Hu, Chuan-Le Xiao
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7123438323