In-silico study of tools that predict SNPs impact in protein functionality

MURILLO JAVIER; OLIVE CAILLOUX; KRSTICEVIC FLAVIA; SPETALE FLAVIO EZEQUIEL; TAPIA ELIZABETH; SÉBASTIEN DESTERCKE; GUSTAVO VAZQUEZ; TAMARA ERNANDEZ; BULACIO PILAR

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Conferencia; V International Society for Computational Biology Latin America, SOIBIO and EMBnet Joint Bioinformatics Conference 2018; 2018

A single nucleotide polymorphism (SNP) is a variation in a single nucleotide that occurs at a specific position in a genome. When a SNP occurs in a coding region, this change can or cannot affect the protein functionality; in this case the SNP is called non-synonymous mutation or missense, respectively. Non-synonymous SNPs are especially relevant since they may change the phenotype, e.g., outcome as diseases. Many in-silico tools have been developed to predict the effect of SNPs to avoid in-vivo assays which are very expensive in money and time. Nowadays, these tools are used to advice of disease likelihood, but their great abundance and the output heterogeneity makes them difficult to select and even harder to compare. In this work we present a study of six tools that predict the impact of SNPs on gene functionality through the use of different background information. The tools were tested with all possible mutations in two genes: one belonging to chikungunya virus and one to drosophila melanogaster. To compare their outputs we propose two indexes which turn their numerical output to a commensurable one. Both, consistency among them and consistency with literature results are analyzed, and two indices are proposed.