| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 388.77 KB | Adobe PDF |
Advisor(s)
Abstract(s)
Variations in the gene encoding uridine diphosphate glucuronosyltransferase 1A1 (UGT1A1) are particularly important because they have been associated with hyperbilirubinemia in Gilbert’s and Crigler-Najjar syndromes as well as with changes in drug metabolism. Several variants associated with these phenotypes are non-synonymous single nucleotide polymorphisms (nsSNPs). Bioinformatics approaches have gained increasing importance in predicting the functional significance of these variants. This study was focused on the predictive ability of bioinformatics approaches to determine the pathogenicity of human UGT1A1 nsSNPs, which were previously characterized at the protein level by in vivo and in vitro studies. Using sixteen web algorithms, we evaluated 48 nsSNPs described in the literature and databases. Eight of these algorithms reached or exceeded 90% sensitivity and six presented a Matthews correlation coefficient above 0.46. The best performing method was MutPred, followed by SIFT. The prediction measures varied significantly when predictors such us SIFT, Polyphen-2 and PMUT were run with their native alignment generated by the tool, or with an input alignment that was strictly built with UGT1A1 orthologs and manually curated. The capacity of the SNP prediction methods can vary according to the available structural information and the MSA employed. We verified that methods primarily based on protein structural information such as SNPeffect failed to give reliable information for the UGT1A1 variants. There are numerous available evolutionary tools that do not allow the user to select the alignments. One of the concepts that our data suggest is that the selection of user inputs improves the performance of these methods. Our data also suggest that whenever possible, the user should consider optimizing the sequence alignment employed. The performance study of SNP predictors using a set of functionally well-characterized variants is essential to help redirect the in silico analysis of a particular gene.
Description
Keywords
Citation
Rodrigues, Carina; Santos-Silva, Alice; Costa, Elísio; Bronze-da-Rocha, Elsa (2016). Desempenho de ferramentas in silico: avaliação de variantes de mutações missense do gene UGT1A1. In 6ª Edição Workshop de Bioinformática. Bragança
Publisher
Escola Superior Agrária de Bragança