CAGI 5 splicing challenge: Improved exon skipping and intron retention predictions with MMSplice
Cheng J, Celik MH, Thi Yen Duong Nguyen , Avsec Z, Gagneur J (2019)
Publication Type: Journal article
Publication year: 2019
Journal
Book Volume: 40
Pages Range: 1243-1251
Journal Issue: 9
DOI: 10.1002/humu.23788
Abstract
Pathogenic genetic variants often primarily affect splicing. However, it remains difficult to quantitatively predict whether and how genetic variants affect splicing. In 2018, the fifth edition of the Critical Assessment of Genome Interpretation proposed two splicing prediction challenges based on experimental perturbation assays: Vex-seq, assessing exon skipping, and MaPSy, assessing splicing efficiency. We developed a modular modeling framework, MMSplice, the performance of which was among the best on both challenges. Here we provide insights into the modeling assumptions of MMSplice and its individual modules. We furthermore illustrate how MMSplice can be applied in practice for individual genome interpretation, using the MMSplice VEP plugin and the Kipoi variant interpretation plugin, which are directly applicable to VCF files.
Involved external institutions
Germany (DE)How to cite
APA:
Cheng, J., Celik, M.H., Thi Yen Duong Nguyen, ., Avsec, Z., & Gagneur, J. (2019). CAGI 5 splicing challenge: Improved exon skipping and intron retention predictions with MMSplice. Human Mutation, 40(9), 1243-1251. https://dx.doi.org/10.1002/humu.23788
MLA:
Cheng, Jun, et al. "CAGI 5 splicing challenge: Improved exon skipping and intron retention predictions with MMSplice." Human Mutation 40.9 (2019): 1243-1251.
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