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Assessing predictions of the impact of variants on splicing in CAGI5
Authors:Stephen M Mount   iga Avsec  Liran Carmel  Rita Casadio  Muhammed Hasan elik  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 Wang  Yuedong Yang  Huiying Zhao
Institution: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 Wang,Yuedong Yang,Huiying Zhao
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.
Keywords:CAGI experiment  machine learning  mutation  splicing  variant interpretation
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