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MMSplice: modular modeling improves the predictions of genetic variant effects on splicing

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Cheng, Jun, Nguyen, Thi Yen Duong, Cygan, Kamil J., Çelik, Muhammed Hasan, Fairbrother, William G., Avsec, žiga, Gagneur, Julien·2019·Genome Biology
DOI 10.1186/s13059-019-1653-zPMC PMC6396468

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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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  • Sequence-based models of post-transcriptional regulation rely on genome annotations and transformed measurements extracted from RNA-seq to isolate each regulatory mechanism.

    “Similarly, sequence-based models of post-transcriptional regulation rely on genome annotations and transformed measurements extracted from RNA-seq to isolate each regulatory mechanism (for example, percent spliced-in for splicing)14–23.”

    From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [14]

    The Background (page unavailable) describes percent spliced-in as an RNA-seq-derived measure used by sequence-based splicing models. The Results, “Modular modeling strategy” (page unavailable), says MMSplice trains separate splicing modules using genome annotations and measured splicing outcomes. These passages support the splicing example, but not the broader claim that models of post-transcriptional regulation generally use those inputs to isolate each regulatory mechanism. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Cheng, J., Nguyen, T. Y. D., Cygan, K. J., Çelik, M. H., Fairbrother, W. G., Avsec, Ž., & Gagneur, J.
Affiliations
  • Technical University of Munich (DE)
  • Brown University (US)
Corresponding author
Gagneur, Julien
ORCID
Venue
Genome Biology
Publisher
Springer Science and Business Media LLC
Published
2019-03-01
Volume
20
Issue
1
Pages
48-48
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
DOI
10.1186/s13059-019-1653-z
PMC
PMC6396468
OpenAlex
W2918497364
Cited by
235
References
4
Citations by year
2026: 24 · 2025: 31 · 2024: 50 · 2023: 37 · 2022: 37 · 2021: 33 · 2020: 11 · 2019: 11 · 2013: 1
FWCI
7.61
Type
journal article
Language
en
Open access
Licence
Locations
  • Genome biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Nvidia
  • Deutsche Forschungsgemeinschaft
  • National Institute of General Medical Sciences (R01GM095612, R01GM105681, R01GM127472)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords
splicing efficiency, variant pathogenicity prediction, Kipoi, indels
MeSH terms
Models, Genetic, Genetic Variation, Neural Networks, Computer, Alternative Splicing, Genetic Diseases, Inborn

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