|Title||Performance of computational methods for the evaluation of Pericentriolar Material 1 missense variants in CAGI-5.|
|Publication Type||Journal Article|
|Year of Publication||2019|
|Authors||Monzon, AMiguel, Carraro, M, Chiricosta, L, Reggiani, F, Han, J, Ozturk, K, Wang, Y, Miller, M, Bromberg, Y, Capriotti, E, Savojardo, C, Babbi, G, Martelli, PLuigi, Casadio, R, Katsonis, P, Lichtarge, O, Carter, H, Kousi, M, Katsanis, N, Andreoletti, G, Moult, J, Brenner, SE, Ferrari, C, Leonardi, E, Tosatto, SCE|
|Date Published||2019 Jul 01|
The CAGI-5 PCM1 challenge aimed to predict the effect of 38 transgenic human missense mutations in the Pericentriolar Material 1 (PCM1) protein implicated in schizophrenia. Participants were provided with 16 benign variants (negative controls), 10 hypomorphic, and 12 loss of function variants. Six groups participated and were asked to predict the probability of effect and standard deviation associated to each mutation. Here, we present the challenge assessment. Prediction performance were evaluated using different measures to conclude in a final ranking which highlights the strengths and weaknesses of each group. The results show a great variety of predictions where some methods performed significantly better than others. Benign variants played an important role as negative controls, highlighting predictors biased to identify disease phenotypes. The best predictor, Bromberg lab used a neural-network based method able to discriminate between neutral and non-neutral single nucleotide polymorphisms. The CAGI-5 PCM1 challenge allowed us to evaluate the state of the art techniques for interpreting the effect of novel variants for a difficult target protein. This article is protected by copyright. All rights reserved.
|Alternate Journal||Hum. Mutat.|