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Hum Mutat. 2019 Sep;40(9):1243-1251. doi: 10.1002/humu.23788. Epub 2019 Jul 29.

CAGI 5 splicing challenge: Improved exon skipping and intron retention predictions with MMSplice.

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Department of Informatics, Technical University of Munich, Garching, Germany.
Graduate School of Quantitative Biosciences (QBM), Ludwig-Maximilians-Universität München, Munich, Germany.


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.


artificial neural network; splicing; variant effect; variant interpretation


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