CAGI 5 splicing challenge: Improved exon skipping and intron retention predictions with MMSplice. Issue 9 (29th July 2019)

• Record Type: Journal Article
• Authors: Cheng, Jun; Çelik, Muhammed Hasan; Nguyen, Thi Yen Duong; Avsec, Žiga; Gagneur, Julien
• Editors: Moult, John; Brenner, Steven E.
• Other Names: Karchin Rachel guestEditor.; Pal Lipika R. specialEditor.
• Is Part Of: Human mutation. Volume 40:Issue 9(2019)
• Page Start: 1243
• Page End: 1251
• Publication Date: 2019-07-29
• Subjects: artificial neural network -- splicing -- variant effect -- variant interpretation; Human chromosome abnormalities -- Periodicals; Mutation (Biology) -- Periodicals; 616.04205
• DOI: 10.1002/humu.23788
• Languages: English
• ISSNs: 1059-7794

Abstract

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. Abstract : Pathogenic genetic variants often primarily affect splicing. In 2018, CAGI proposed two splicing prediction challenges based on experimental perturbation assays: Vex‐seq, assessing exon skipping, and MaPSy assessing splicing efficiency. Our modular modeling framework, MMSplice, performed among the best for both challenges.