Assessing predictions of the impact of variants on splicing in CAGI5

Stephen M. Mount, Žiga Avsec, Liran Carmel, Rita Casadio, Muhammed Hasan Çelik, Ken Chen, Jun Cheng, Noa E. Cohen, William G. Fairbrother, Tzila Fenesh, Julien Gagneur, Valer Gotea, Tamar Holzer, Chiao Feng Lin, Pier Luigi Martelli, Tatsuhiko Naito, Thi Yen Duong Nguyen, Castrense Savojardo, Ron Unger, Robert WangYuedong Yang, Huiying Zhao

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Precision medicine and sequence-based clinical diagnostics seek to predict disease risk or to identify causative variants from sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. In the past, few CAGI challenges have addressed the impact of sequence variants on splicing. In CAGI5, two challenges (Vex-seq and MaPSY) involved prediction of the effect of variants, primarily single-nucleotide changes, on splicing. Although there are significant differences between these two challenges, both involved prediction of results from high-throughput exon inclusion assays. Here, we discuss the methods used to predict the impact of these variants on splicing, their performance, strengths, and weaknesses, and prospects for predicting the impact of sequence variation on splicing and disease phenotypes.

Original languageEnglish
Pages (from-to)1215-1224
Number of pages10
JournalHuman Mutation
Volume40
Issue number9
DOIs
StatePublished - 1 Sep 2019

Bibliographical note

Publisher Copyright:
© 2019 Wiley Periodicals, Inc.

Keywords

  • CAGI experiment
  • machine learning
  • mutation
  • splicing
  • variant interpretation

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