A resource of variant effect predictions of single nucleotide variants in model organisms.

The effect of single nucleotide variants (SNVs) in coding and noncoding regions is of great interest in genetics. Although many computational methods aim to elucidate the effects of SNVs on cellular mechanisms, it is not straightforward to comprehensively cover different molecular effects. To address this, we compiled and benchmarked sequence and structure‐based variant effect predictors and we computed the impact of nearly all possible amino acid and nucleotide variants in the reference genomes of Homo sapiens, Saccharomyces cerevisiae and Escherichia coli. Studied mechanisms include protein stability, interaction interfaces, post‐translational modifications and transcription factor binding sites. We apply this resource to the study of natural and disease coding variants. We also show how variant effects can be aggregated to generate protein complex burden scores that uncover protein complex to phenotype associations based on a set of newly generated growth profiles of 93 sequenced S. cerevisiae strains in 43 conditions. This resource is available through mutfunc (www.mutfunc.com), a tool by which users can query precomputed predictions by providing amino acid or nucleotide‐level variants. Synopsis: This study presents mutfunc, a resource that includes the pre‐computed impact of genetic variants in three model organisms (human, yeast and E. coli). These predictions can be used to prioritize genetic variants and compute gene burden scores. Mutfunc is a resource that includes predictions on the impact of single nucleotide substitution across three organisms.Genetic variants can be prioritized by their likely impact on various molecular processes.A gene/complex burden score can be computed from these predictions and used to associate genotype with phenotype. This study presents mutfunc, a resource that includes the pre‐computed impact of genetic variants in three model organisms (human, yeast and E. coli). These predictions can be used to prioritize genetic variants and compute gene burden scores. [ABSTRACT FROM AUTHOR]