ArticleCommunications biology2024
Transformers significantly improve splice site prediction.
Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review on the generative AI applications in human medical genetics.Frontiers in genetics · 2025Pooled it
- SpliceSelectNet: a hierarchical Transformer-based deep learning model for splice site prediction.Nucleic acids research · 2026Article
- Application of deep learning in crop research: From genomics to phenomics.The plant genome · 2026Review
- DeepSAP: improved RNA-seq alignment by integrating transcriptome guidance with transformer-based splice junction scoring.Genome biology · 2026Article
- Utility of Targeted RNA Analysis in Neurogenetic Disorders.Neurology. Genetics · 2026Article
- A comprehensive survey of genome language models in bioinformatics.Briefings in bioinformatics · 2026Review
- Alternative Splicing: Molecular Mechanisms, Biological Functions, Diseases, and Potential Therapeutic Targets.MedComm · 2025Review
- Genome-wide functional annotation and interpretation of splicing variants: toward RNA-targeted therapies.Journal of human genetics · 2025Review
- Detection of mRNA Transcript Variants.Genes · 2025Review
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mutations that affect RNA splicing significantly impact human diversity and disease. Here we present a method using transformers, a type of machine learning model, to detect splicing from raw 45,000-nucleotide sequences. We generate embeddings with residual neural networks and apply hard attention to select splice site candidates, enabling efficient training on long sequences. Our method surpasses the leading tool, SpliceAI, in detecting splice sites in GENCODE and ENSEMBL annotations. Using extensive RNA sequencing data from an Icelandic cohort of 17,848 individuals and the Genotype-Tissue Expression (GTEx) project, our method demonstrates superior performance in detecting splice junctions compared to SpliceAI-10k (PR-AUC = 0.834 vs. PR-AUC = 0.820) and is more effective at identifying disease-related splice variants in ClinVar (PR-AUC = 0.997 vs. PR-AUC = 0.996). These advancements hold promise for improving genetic research and clinical diagnostics, potentially leading to better understanding and treatment of splicing-related diseases.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.