ArticleGenome biology2019
MMSplice: modular modeling improves the predictions of genetic variant effects on splicing.
Article in Genome biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 147 papers.
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Who cites it
147 citing papers in PubMed.
- Leveraging genomic deep learning models for the prediction of noncoding variant effects.Nature genetics · 2026Review
- Splice-site variants in neurology: from molecular mechanisms to clinical interpretation - a focused review.Neurogenetics · 2026Review
- From conservation scores to language models: a feature engineering perspective on machine learning for genetic variant interpretation.Briefings in bioinformatics · 2026Review
- ASPIRE: Accurate alternative splicing prediction from limited RNA sequencing data and a minimal gene set.PLoS computational biology · 2026Article
- Integrating single-cell sQTL mapping with deep-learning splicing prediction identifies causal variants under influenza infection.Genome biology · 2026Article
- Advances and challenges of splicing prediction with AI.Nature genetics · 2026Review
- Clinical and genetic characteristics of a large cohort of children with Alagille syndrome: identification of 57 new variants in the JAG1 gene.Journal of human genetics · 2026Article
- Improving splice site usage prediction with SPLAIRE.bioRxiv : the preprint server for biology · 2026Article
- Annotating genomes at increased scale and resolution.Nature reviews. Genetics · 2026Review
- 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
- Pathogenicity prediction for noncanonical splice-altering variants based on multimodal feature fusion.Briefings in bioinformatics · 2026Article
- NCBoost v2: a classifier for non-coding single-nucleotide variants in Mendelian diseases.Bioinformatics (Oxford, England) · 2026Article
- Variant-resolved prediction of context-specific isoform variation with a graph-based attention model.Cell genomics · 2026Article
- Harnessing artificial intelligence for genomic variant prediction: advances, challenges, and future directions.GigaScience · 2026Review
- Causal splicing variants revealed by deep-learning integration of single-cell sQTL mapping under influenza infection.Research square · 2026Article
- Assessment of In-Frame Indel Variants in an Unsolved Cohort of Inherited Retinal Diseases Using Machine Learning.Human mutation · 2026Article
- Evolving computational paradigms for noncoding variant pathogenicity prediction.Frontiers in molecular biosciences · 2026Review
- Deep learning for regulatory genomics: a survey of models, challenges, and applications.Bioinformatics advances · 2026Review
- Clinical Variant Databases and Machine Learning Prediction Supporting Genomic Medicine.Methods in molecular biology (Clifton, N.J.) · 2026Article
87 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
Abstract
Predicting the effects of genetic variants on splicing is highly relevant for human genetics. We describe the framework MMSplice (modular modeling of splicing) with which we built the winning model of the CAGI5 exon skipping prediction challenge. The MMSplice modules are neural networks scoring exon, intron, and splice sites, trained on distinct large-scale genomics datasets. These modules are combined to predict effects of variants on exon skipping, splice site choice, splicing efficiency, and pathogenicity, with matched or higher performance than state-of-the-art. Our models, available in the repository Kipoi, apply to variants including indels directly from VCF files.
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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.