ArticleNature communications2024
SpliceTransformer predicts tissue-specific splicing linked to human diseases.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- ASPIRE: Accurate alternative splicing prediction from limited RNA sequencing data and a minimal gene set.PLoS computational biology · 2026Article
- Advances and challenges of splicing prediction with AI.Nature genetics · 2026Review
- 'Missing' disease-causing variants in Alport syndrome.Nature reviews. Nephrology · 2026Review
- SpliceSelectNet: a hierarchical Transformer-based deep learning model for splice site prediction.Nucleic acids research · 2026Article
- Review
- Predicting human mRNA isoform levels from site-specific splicing kineticsbioRxiv : the preprint server for biology · 2026Article
- Improving splice site usage prediction with SPLAIRE.bioRxiv : the preprint server for biology · 2026Article
- Resolving variants of uncertain significance in neurofibromatosis: An integrated approach combining deep learning and minigene assays.Functional & integrative genomics · 2026Article
- Homozygous SGCB splice-site variant causes isolated dilated cardiomyopathy through sarcoglycan complex destabilization in East Asians.The Journal of clinical investigation · 2026Article
- Application of deep learning in crop research: From genomics to phenomics.The plant genome · 2026Review
- Article
- DeepSAP: improved RNA-seq alignment by integrating transcriptome guidance with transformer-based splice junction scoring.Genome biology · 2026Article
- Comprehensive review and assessment of multi-species splicing variant prediction: task-specific deep learning models and genomic foundation models.Briefings in bioinformatics · 2026Review
- Pervasive non-triplet alternative splicing drives functional isoform diversity.Nature communications · 2026Article
- SASdb: a comprehensive database for sex-biased alternative splicing profiles in human tissues.Biology of sex differences · 2026Article
- Harnessing artificial intelligence for genomic variant prediction: advances, challenges, and future directions.GigaScience · 2026Review
- A comprehensive survey of genome language models in bioinformatics.Briefings in bioinformatics · 2026Review
- Improving spliced alignment by modeling splice sites with deep learning.Algorithms for molecular biology : AMB · 2026Article
- Improvement of Diagnostics in NSCLC Patients withInternational journal of molecular sciences · 2025Article
- Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications.Nature communications · 2025Article
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present SpliceTransformer (SpTransformer), a deep-learning framework that predicts tissue-specific RNA splicing alterations linked to human diseases based on genomic sequence. SpTransformer outperforms all previous methods on splicing prediction. Application to approximately 1.3 million genetic variants in the ClinVar database reveals that splicing alterations account for 60% of intronic and synonymous pathogenic mutations, and occur at different frequencies across tissue types. Importantly, tissue-specific splicing alterations match their clinical manifestations independent of gene expression variation. We validate the enrichment in three brain disease datasets involving over 164,000 individuals. Additionally, we identify single nucleotide variations that cause brain-specific splicing alterations, and find disease-associated genes harboring these single nucleotide variations with distinct expression patterns involved in diverse biological processes. Finally, SpTransformer analysis of whole exon sequencing data from blood samples of patients with diabetic nephropathy predicts kidney-specific RNA splicing alterations with 83% accuracy, demonstrating the potential to infer disease-causing tissue-specific splicing events. SpTransformer provides a powerful tool to guide biological and clinical interpretations of human diseases.
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