Blind prediction of deleterious amino acid variations with SNPs&GO

Abstract

SNPs&GO is a machine learning method for predicting the association of single amino acid variations (SAVs) to disease, considering protein functional annotation. The method is a binary classifier that implements a support vector machine algorithm to discriminate between disease-related and neutral SAVs. SNPs&GO combines information from protein sequence with functional annotation encoded by gene ontology (GO) terms. Tested in sequence mode on more than 38,000 SAVs from the SwissVar dataset, our method reached 81% overall accuracy and an area under the receiving operating characteristic curve of 0.88 with low false-positive rate. In almost all the editions of the Critical Assessment of Genome Interpretation (CAGI) experiments, SNPs&GO ranked among the most accurate algorithms for predicting the effect of SAVs. In this paper, we summarize the best results obtained by SNPs&GO on disease-related variations of four CAGI challenges relative to the following genes: CHEK2 (CAGI 2010), RAD50 (CAGI 2011), p16-INK (CAGI 2013), and NAGLU (CAGI 2016). Result evaluation provides insights about the accuracy of our algorithm and the relevance of GO terms in annotating the effect of the variants. It also helps to define good practices for the detection of deleterious SAVs.

Keywords: disease-related variation; gene ontology; genome interpretation; machine learning; protein function; single amino acid variation; variant annotation.

Figure 1

Comparison between predicted and experimental Relative Proliferation (RelPro) rates for the p16 challenge. Linear regression for SPARK-LAB (A), SNPs&GO¹³ (B) and Dr.Cancer (C) predictions. r and r° are the Pearson’s correlation coefficients with and without the amino acid variation p.Gly23Ala respectively.

Figure 2

Comparison between the binary classification performance of SNPs&GO¹³ (black) and MutPred2* (gray) on the NAGLU dataset.