ArticleBriefings in bioinformatics2024
Self-supervised learning on millions of primary RNA sequences from 72 vertebrates improves sequence-based RNA splicing prediction.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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Who cites it
54 citing papers in PubMed, 62 citations in OpenAlex.
- AI foundation models for RNA biology.RNA biology · 2026Review
- Tools and tactics for studying alternative splicing.Nature reviews. Genetics · 2026Review
- ASPIRE: Accurate alternative splicing prediction from limited RNA sequencing data and a minimal gene set.PLoS computational biology · 2026Article
- RNARL: reinforcement learning-driven unified generative framework for multi-objective RNA codon design.Genome biology · 2026Article
- Advancing bioinformatics with language models: components, applications, and perspectives.Briefings in bioinformatics · 2026Review
- Foundation models in omics research: a comprehensive survey.Briefings in bioinformatics · 2026Review
- An overview of self-supervised deep learning applications to molecular data.Briefings in bioinformatics · 2026Review
- Advances and challenges of splicing prediction with AI.Nature genetics · 2026Review
- SpliceSelectNet: a hierarchical Transformer-based deep learning model for splice site prediction.Nucleic acids research · 2026Article
- Review
- Improving splice site usage prediction with SPLAIRE.bioRxiv : the preprint server for biology · 2026Article
- CodonMoE: DNA language models for codon-dependent mRNA prediction.Bioinformatics (Oxford, England) · 2026Article
- Article
- Comprehensive review and assessment of multi-species splicing variant prediction: task-specific deep learning models and genomic foundation models.Briefings in bioinformatics · 2026Review
- MIFNDRA: an innovative knowledge-enhanced multimodal fusion and graph learning framework for predicting drug resistance-related ncRNAs.Briefings in bioinformatics · 2026Article
- Zero-shot benchmarking of RNA language models in structural, functional, and evolutionary learning.Briefings in bioinformatics · 2026Article
- The DNA dialect: a comprehensive guide to pretrained genomic language models.Molecular systems biology · 2026Review
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- CoBRA: compound binding site prediction using RNA language model.Briefings in bioinformatics · 2026Article
- A comprehensive survey of genome language models in bioinformatics.Briefings in bioinformatics · 2026Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Language models pretrained by self-supervised learning (SSL) have been widely utilized to study protein sequences, while few models were developed for genomic sequences and were limited to single species. Due to the lack of genomes from different species, these models cannot effectively leverage evolutionary information. In this study, we have developed SpliceBERT, a language model pretrained on primary ribonucleic acids (RNA) sequences from 72 vertebrates by masked language modeling, and applied it to sequence-based modeling of RNA splicing. Pretraining SpliceBERT on diverse species enables effective identification of evolutionarily conserved elements. Meanwhile, the learned hidden states and attention weights can characterize the biological properties of splice sites. As a result, SpliceBERT was shown effective on several downstream tasks: zero-shot prediction of variant effects on splicing, prediction of branchpoints in humans, and cross-species prediction of splice sites. Our study highlighted the importance of pretraining genomic language models on a diverse range of species and suggested that SSL is a promising approach to enhance our understanding of the regulatory logic underlying genomic sequences.
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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.