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DeepBAM: a high-accuracy single-molecule CpG methylation detection tool for Oxford nanopore sequencing
Xin Bai1, Hui-Cong Yao2, Bo Wu1
1State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, 7 Jinsui Road, Tianhe District, Guangzhou 510060, China.
Briefings in Bioinformatics
|August 23, 2024
Summary
A new DeepBAM model improves nanopore sequencing for CpG methylation detection. It offers more accurate and stable results than the standard Dorado tool across diverse datasets, enhancing epigenomic research.
Area of Science:
- Genomics
- Epigenetics
- Bioinformatics
Background:
- Nanopore sequencing (R10.4) offers high base calling accuracy for CpG methylation detection.
- The performance of the official Dorado methylation calling model across diverse datasets is not well-established.
Purpose of the Study:
- To evaluate the robustness and universality of the Dorado methylation calling model.
- To develop a more accurate and stable methylation calling model for nanopore sequencing data.
Main Methods:
- Collected heterogeneous human and plant datasets.
- Comprehensively evaluated the performance of the Dorado model.
- Developed a deep neural network model, DeepBAM, with optimized training.
Main Results:
- Dorado showed significant performance variations across datasets.
- DeepBAM outperformed Dorado in accuracy and stability, with higher ROC AUC and F1 scores.
- DeepBAM methylation frequencies correlated highly (>0.95) with BS-seq data, surpassing Dorado's performance.
Conclusions:
- DeepBAM provides superior and consistent CpG methylation calling for nanopore sequencing data.
- The model enables detailed analysis of allele-specific methylation and transposable elements.
- DeepBAM is poised to expand the utility of nanopore sequencing in epigenetics.
