Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Nucleic acids research
SpliceSelectNet: a hierarchical Transformer-based deep learning model for splice site prediction
Accurate RNA splicing is essential for gene expression and protein function, yet the mechanisms governing splice site recognition remain incompletely understood. Aberrant splicing caused by mutations can lead to severe diseases, including cancer and genetic disorders, underscoring the need for accurate computational tools to predict splice sites and detect disruptions. Existing methods have made significant advances in splice site prediction but are often limited in handling long-range dependencies due to high computational costs, a factor critical to splicing regulation. Moreover, many models lack interpretability, hindering efforts to elucidate the underlying biological mechanisms. Here, we present SpliceSelectNet (SSNet), a hierarchical Transformer-based deep learning model that predicts splice sites from DNA sequences spanning up to 100 kb. By integrating local and global attention mechanisms, SSNet efficiently captures both proximal and distal regulatory signals while maintaining single-nucleotide resolution. Across multiple benchmark datasets, SSNet achieves state-of-the-art performance in splice site prediction and aberrant splicing detection. Systematic in silico mutagenesis demonstrates that attention scores reflect functional sequence importance, supporting their biological relevance. Long-range sequence perturbation experiments further show that SSNet captures distal regulatory effects beyond conventional receptive fields. Together, these results establish SSNet as a biologically interpretable framework for modeling long-range splicing regulation from genomic sequence.
23 June 2026
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Clinical Feasibility of Long-Read WGS for DNA Methylation Signature Analysis
DNA methylation (DNAm) signatures have emerged as valuable diagnostic biomarkers for rare genetic disorders. To date, the most widely used approach for establishing and validating these signatures has relied on array-based technologies. However, in clinical diagnostics, there is a growing shift from short-read sequencing (SRS) toward long-read sequencing (LRS) technologies. Recent advances in platforms such as Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) enable direct assessment of DNAm from native DNA, offering improved resolution and reduced technical bias compared to array-based technologies. In this study, we compared DNAm profiles generated by LRS with those obtained from DNAm arrays. DNAm profiles of two individuals with pathogenic KMT2D variants were analyzed using DNAm arrays, LRS using PacBio and ONT, and ONT multiplexed sequencing with adaptive sampling. A support vector machine (SVM) classifier trained on array data, as well as the public classification platform EpigenCentral, yielded correct predictions for all LRS samples, underscoring the potential of LRS platforms in DNAm-based diagnostics. Our results suggest that DNAm profiles generated by LRS align well with DNAm signatures established using DNAm arrays, supporting their feasibility in clinical and research applications with the added benefit of simultaneous methylation and sequence analysis.
4 Apr 2026
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