The CAGI-5 pericentriolar material 1 (PCM1) challenge aimed to predict the effect of 38 transgenic human missense mutations in the 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 was 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.

Performance of computational methods for the evaluation of pericentriolar material 1 missense variants in CAGI-5

Capriotti E.;Savojardo C.;Babbi G.;Martelli P. L.;Casadio R.;
2019

Abstract

The CAGI-5 pericentriolar material 1 (PCM1) challenge aimed to predict the effect of 38 transgenic human missense mutations in the 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 was 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.
Monzon A.M.; Carraro M.; Chiricosta L.; Reggiani F.; Han J.; Ozturk K.; Wang Y.; Miller M.; Bromberg Y.; Capriotti E.; Savojardo C.; Babbi G.; Martelli P.L.; Casadio R.; Katsonis P.; Lichtarge O.; Carter H.; Kousi M.; Katsanis N.; Andreoletti G.; Moult J.; Brenner S.E.; Ferrari C.; Leonardi E.; Tosatto S.C.E.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/715312
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