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

Abstract

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.

Future Preventive Gene Therapy of Polygenic Diseases from a Population Genetics Perspective

“…Many ethical and regulatory considerations will need to be settled before (or if) such therapies become practicable [15,107]. Deeper scientific knowledge and more advanced techniques are being developed, particularly for personalized determination (with either computational methods or thoroughly verified genomic databases) of the deleterious effects of common [108][109][110] and rare allele variations and exome mutations [43,[111][112][113][114][115][116]. It may be many years (likely decades) until precise knowledge is of sufficient depth for personalized medicine diagnostics to be conducted.…”

Characterization of threeYarrowia lipolyticastrains in respect to different cultivation temperatures and metabolite secretion

“…Interestingly, several deleterious sequence variations were found for 1889 and but none for H222Δ pox1-6 , despite their similar phenotypes. However, genotypic diversity does not mandatory lead to distinct phenotypes (Wagih et al 2018) as well as the prediction accuracy of such tools is limited and dependent on the amount of available homologous sequences (Choi et al 2012) .…”

From prioritisation to understanding: mechanistic predictions of variant effects

“…As a result, our understanding of the mechanisms underlying genetic diseases is lagging behind, despite the large number of existing prediction tools. In their recent work, Wagih et al () present Mutfunc, a new resource that provides mechanistic predictions of mutational impact on function by combining sequence conservation with analysis of mutational effects on protein stability, interaction interfaces, post‐translational modifications and linear motifs. Additionally, Mutfunc predicts disruptions to start and stop codons, and quantifies the impact of non‐coding mutations, by analysing their effects on transcription factor binding sites, allowing predictions of gene expression deregulation.…”

“…Therefore, developing approaches allowing accurate in‐silico prediction of mutation effects is becoming increasingly important. In their recent study, Beltrao and colleagues (Wagih et al , ) describe an integrative approach for determining the effects of mutations from the perspective of protein structure, conservation and transcription factor binding. This allows for predicting the mechanisms underlying the most impactful variants rather than just identifying these variants.…”

“… Developing approaches for in‐silico prediction of mutation effects is important for interpreting sequence variants. In their recent work, Beltrao et al (Wagih et al , ) describe an integrative approach for determining the effects of mutations from the perspective of protein structure, conservation and transcription factor binding.

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