TY - JOUR
T1 - Assessing predictions of the impact of variants on splicing in CAGI5
AU - Mount, Stephen M.
AU - Avsec, Žiga
AU - Carmel, Liran
AU - Casadio, Rita
AU - Çelik, Muhammed Hasan
AU - Chen, Ken
AU - Cheng, Jun
AU - Cohen, Noa E.
AU - Fairbrother, William G.
AU - Fenesh, Tzila
AU - Gagneur, Julien
AU - Gotea, Valer
AU - Holzer, Tamar
AU - Lin, Chiao Feng
AU - Martelli, Pier Luigi
AU - Naito, Tatsuhiko
AU - Nguyen, Thi Yen Duong
AU - Savojardo, Castrense
AU - Unger, Ron
AU - Wang, Robert
AU - Yang, Yuedong
AU - Zhao, Huiying
N1 - Publisher Copyright:
© 2019 Wiley Periodicals, Inc.
PY - 2019/9/1
Y1 - 2019/9/1
N2 - 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.
AB - 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.
KW - CAGI experiment
KW - machine learning
KW - mutation
KW - splicing
KW - variant interpretation
UR - http://www.scopus.com/inward/record.url?scp=85070809093&partnerID=8YFLogxK
U2 - 10.1002/humu.23869
DO - 10.1002/humu.23869
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C2 - 31301154
AN - SCOPUS:85070809093
SN - 1059-7794
VL - 40
SP - 1215
EP - 1224
JO - Human Mutation
JF - Human Mutation
IS - 9
ER -