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

Jun Cheng, Muhammed Hasan Çelik, Thi Yen Duong Nguyen, Žiga Avsec, Julien Gagneur

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

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.

Original languageEnglish
Pages (from-to)1243-1251
Number of pages9
JournalHuman Mutation
Volume40
Issue number9
DOIs
StatePublished - 1 Sep 2019

Keywords

  • artificial neural network
  • splicing
  • variant effect
  • variant interpretation

Fingerprint

Dive into the research topics of 'CAGI 5 splicing challenge: Improved exon skipping and intron retention predictions with MMSplice'. Together they form a unique fingerprint.

Cite this