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 language | English |
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Pages (from-to) | 1215-1224 |
Number of pages | 10 |
Journal | Human Mutation |
Volume | 40 |
Issue number | 9 |
DOIs | |
State | Published - 1 Sep 2019 |
Bibliographical note
Publisher Copyright:© 2019 Wiley Periodicals, Inc.
Funding
This study was partially supported by NSF grant ABI 1564785 to SMM. The CAGI experiment coordination is supported by NIH U41 HG007346 and the CAGI conference by NIH R13 HG006650. We thank CAGI organizers and data providers for making this challenge happen, especially Steven Brenner, John Moult, Lipika Ray, and Brent Graveley. This study was partially supported by NSF grant ABI 1564785 to SMM. The CAGI experiment coordination is supported by NIH U41 HG007346 and the CAGI conference by NIH R13 HG006650. We thank CAGI organizers and data providers for making this challenge happen, especially Steven Brenner, John Moult, Lipika Ray, and Brent Graveley.
Funders | Funder number |
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CAGI | |
National Science Foundation | ABI 1564785 |
National Institutes of Health | R13 HG006650 |
National Human Genome Research Institute | U41HG007346 |
Keywords
- CAGI experiment
- machine learning
- mutation
- splicing
- variant interpretation