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A resource of variant effect predictions of single nucleotide variants in model organisms
Author(s) -
Wagih Omar,
Galardini Marco,
Busby Bede P,
Memon Danish,
Typas Athanasios,
Beltrao Pedro
Publication year - 2018
Publication title -
molecular systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 8.523
H-Index - 148
ISSN - 1744-4292
DOI - 10.15252/msb.20188430
Subject(s) - biology , computational biology , saccharomyces cerevisiae , genetics , genome , homo sapiens , single nucleotide polymorphism , phenotype , gene , genotype , sociology , anthropology
Abstract The effect of single nucleotide variants ( SNV s) in coding and noncoding regions is of great interest in genetics. Although many computational methods aim to elucidate the effects of SNV s 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.

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