Use of bioinformatics tools for the annotation of disease-associated mutations in animal models.

Single-point mutations are one of the most frequent causes of genetic variability in both human and close species. The recent availability of different bioinformatics tools for annotating human single nucleotide polymorphisms (SNPs) has opened the possibility of using them to score SNPs from species with a biomedical interest, in particular from mice and other models of human disease. Also, this ability to predict pathogenicity of single point mutations in one species, based on data from another species, opens the possibility to predict the pathological character of single point mutations in humans using data from well-characterized model systems of human disease. This could provide a valuable alternative to the more traditional genetic population approaches. However, transferral of prediction tools may be limited by different factors, from a species bias in the training set, to a large sequence divergence between the proteomes of the training and the target species. Here we study the conditions under which prediction tools can be transferred among species, concentrating in the case of mice. We find that for the majority of the human-mouse homolog pairs, the sequence similarity is large enough to preserve the pathological character of mutations among species, in general. We then establish that prediction/annotation tools developed for one organism can be used to predict the neutral/pathological character of mutations/SNPs in the other organism.