ArticleBioinformatics (Oxford, England)2018
COSSMO: predicting competitive alternative splice site selection using deep learning.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Article
- Improving splice site usage prediction with SPLAIRE.bioRxiv : the preprint server for biology · 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
- Variant-resolved prediction of context-specific isoform variation with a graph-based attention model.Cell genomics · 2026Article
- Generative Design of Cell Type-Specific RNA Splicing Elements for Programmable Gene Regulation.bioRxiv : the preprint server for biology · 2025Article
- Long-read RNA-seq demarcates cis- and trans-directed alternative RNA splicing.Nature communications · 2025Article
- A basic framework to explain splice-site choice in eukaryotes.Nature communications · 2025Article
- From computational models of the splicing code to regulatory mechanisms and therapeutic implications.Nature reviews. Genetics · 2025Review
- Difference Analysis Among Six Kinds of Acceptor Splicing Sequences by the Dispersion Features of 6-mer Subsets in Human Genes.Biology · 2025Article
- Predicting splicing patterns from the transcription factor binding sites in the promoter with deep learning.BMC genomics · 2024Article
- Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences.Genome research · 2024Article
- Big data and deep learning for RNA biology.Experimental & molecular medicine · 2024Review
- Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences.bioRxiv : the preprint server for biology · 2024Article
- Self-supervised learning on millions of primary RNA sequences from 72 vertebrates improves sequence-based RNA splicing prediction.Briefings in bioinformatics · 2024Article
- Improved modeling of RNA-binding protein motifs in an interpretable neural model of RNA splicing.Genome biology · 2024Article
- Regulation of pre-mRNA splicing: roles in physiology and disease, and therapeutic prospects.Nature reviews. Genetics · 2023Review
- Advances in alternative splicing identification: deep learning and pantranscriptome.Frontiers in plant science · 2023Article
- A review of deep learning applications in human genomics using next-generation sequencing data.Human genomics · 2022Review
- DeeReCT-APA: Prediction of Alternative Polyadenylation Site Usage Through Deep Learning.Genomics, proteomics & bioinformatics · 2022Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Motivation: Alternative splice site selection is inherently competitive and the probability of a given splice site to be used also depends on the strength of neighboring sites. Here, we present a new model named the competitive splice site model (COSSMO), which explicitly accounts for these competitive effects and predicts the percent selected index (PSI) distribution over any number of putative splice sites. We model an alternative splicing event as the choice of a 3' acceptor site conditional on a fixed upstream 5' donor site or the choice of a 5' donor site conditional on a fixed 3' acceptor site. We build four different architectures that use convolutional layers, communication layers, long short-term memory and residual networks, respectively, to learn relevant motifs from sequence alone. We also construct a new dataset from genome annotations and RNA-Seq read data that we use to train our model. Results: COSSMO is able to predict the most frequently used splice site with an accuracy of 70% on unseen test data, and achieve an R2 of 0.6 in modeling the PSI distribution. We visualize the motifs that COSSMO learns from sequence and show that COSSMO recognizes the consensus splice site sequences and many known splicing factors with high specificity. Availability and implementation: Model predictions, our training dataset, and code are available from http://cossmo.genes.toronto.edu. Supplementary information: Supplementary data are available at Bioinformatics online.
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