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Assessing computational predictions of the phenotypic effect of cystathionine‐beta‐synthase variants
Author(s) -
Kasak Laura,
Bakolitsa Constantina,
Hu Zhiqiang,
Yu Changhua,
Rine Jasper,
DimsterDenk Dago F.,
Pandey Gaurav,
Baets Greet,
Bromberg Yana,
Cao Chen,
Capriotti Emidio,
Casadio Rita,
Durme Joost,
Giollo Manuel,
Karchin Rachel,
Katsonis Panagiotis,
Leonardi Emanuela,
Lichtarge Olivier,
Martelli Pier Luigi,
Masica David,
Mooney Sean D.,
Olatubosun Ayodeji,
Radivojac Predrag,
Rousseau Frederic,
Pal Lipika R.,
Savojardo Castrense,
Schymkowitz Joost,
Thusberg Janita,
Tosatto Silvio C.E.,
Vihinen Mauno,
Väliaho Jouni,
Repo Susanna,
Moult John,
Brenner Steven E.,
Friedberg Iddo
Publication year - 2019
Publication title -
human mutation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.981
H-Index - 162
eISSN - 1098-1004
pISSN - 1059-7794
DOI - 10.1002/humu.23868
Subject(s) - cystathionine beta synthase , biology , homocystinuria , computational biology , phenotype , transsulfuration , homocysteine , genetics , bioinformatics , human genetics , hyperhomocysteinemia , gene , amino acid , biochemistry , methionine
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.

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