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Hum Mutat. 2019 Sep;40(9):1530-1545. doi: 10.1002/humu.23868. Epub 2019 Sep 3.

Assessing computational predictions of the phenotypic effect of cystathionine-beta-synthase variants.

Author information

1
Department of Plant and Microbial Biology, University of California, Berkeley, California.
2
Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia.
3
California Institute for Quantitative Biosciences, University of California, Berkeley, California.
4
Switch Laboratory, VIB Center for Brain and Disease Research, Leuven, Belgium.
5
Department of Cellular and Molecular Medicine, KU Leuven, Leuven, Belgium.
6
Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey.
7
Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland.
8
Computational Biology, Bioinformatics and Genomics, Biological Sciences Graduate Program, University of Maryland, College Park, Maryland.
9
Department of Bioengineering, Stanford University, Stanford, California.
10
Biocomputing Group, Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
11
VIB Switch Laboratory, Vrije Universiteit Brussel, Brussels, Belgium.
12
Department of Biomedical Sciences, University of Padua, Padua, Italy.
13
Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland.
14
Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas.
15
Department for Woman and Child Health, University of Padua, Italy.
16
Computational Biology Group, Buck Institute for Research on Aging, Novato, California.
17
Institute of Medical Technology, University of Tampere, Tampere, Finland.
18
School of Informatics and Computing, Indiana University, Bloomington, Indiana.
19
Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts.
20
Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland.
21
Department of Microbiology, Miami University, Oxford, Ohio.
22
Department of Veterinary Microbiology and Preventive Medicine, Iowa State University, Ames, Iowa.

Abstract

Accurate prediction of the impact of genomic variation on phenotype is a major goal of computational biology and an important contributor to personalized medicine. Computational predictions can lead to a better understanding of the mechanisms underlying genetic diseases, including cancer, but their adoption requires thorough and unbiased assessment. Cystathionine-beta-synthase (CBS) is an enzyme that catalyzes the first step of the transsulfuration pathway, from homocysteine to cystathionine, and in which variations are associated with human hyperhomocysteinemia and homocystinuria. We have created a computational challenge under the CAGI framework to evaluate how well different methods can predict the phenotypic effect(s) of CBS single amino acid substitutions using a blinded experimental data set. CAGI participants were asked to predict yeast growth based on the identity of the mutations. The performance of the methods was evaluated using several metrics. The CBS challenge highlighted the difficulty of predicting the phenotype of an ex vivo system in a model organism when classification models were trained on human disease data. We also discuss the variations in difficulty of prediction for known benign and deleterious variants, as well as identify methodological and experimental constraints with lessons to be learned for future challenges.

KEYWORDS:

CAGI challenge; critical assessment; cystathionine-beta-synthase; machine learning; phenotype prediction; single amino acid substitution

PMID:
31301157
DOI:
10.1002/humu.23868

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