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dc.contributor.authorAlipoor, B
dc.contributor.authorGhaedi, H
dc.contributor.authorOmrani, MD
dc.contributor.authorBastami, M
dc.contributor.authorMeshkani, R
dc.contributor.authorGolmohammadi, T
dc.date.accessioned2018-08-26T07:29:51Z
dc.date.available2018-08-26T07:29:51Z
dc.date.issued2016
dc.identifier.urihttp://dspace.tbzmed.ac.ir:8080/xmlui/handle/123456789/47093
dc.description.abstractIt has been suggested that single nucleotide polymorphisms (SNPs) in genes involved in Toll-like receptors (TLRs) pathway may exhibit broad effects on function of this network and might contribute to a range of human diseases. However, the extent to which these variations affect TLR signaling is not well understood. In this study, we adopted a bioinformatics approach to predict the consequences of SNPs in TLRs network. The consequences of non-synonymous coding SNPs (nsSNPs) were predicted by SIFT, PolyPhen, PANTHER, SNPs&GO, I-Mutant, ConSurf and NetSurf tools. Structural visualization of wild type and mutant protein was performed using the project HOPE and Swiss PDB viewer. The influence of 5'-UTR and 3'-UTR SNPs were analyzed by appropriate computational approaches. Nineteen nsSNPs in TLRs pathway genes were found to have deleterious consequences as predicted by the combination of different algorithms. Moreover, our results suggested that SNPs located at UTRs of TLRs pathway genes may potentially influence binding of transcription factors or microRNAs. By applying a pathway-based bioinformatics analysis of genetic variations, we provided a prioritized list of potentially deleterious variants. These findings may facilitate the selection of proper variants for future functional and/or association studies.
dc.language.isoEnglish
dc.relation.ispartofINTERNATIONAL JOURNAL OF MOLECULAR AND CELLULAR MEDICINE
dc.subjectBioinformatics
dc.subjectin-silico analysis
dc.subjectsingle nucleotide polymorphisms
dc.subjecttoll-like receptors
dc.titleA Bioinformatics Approach to Prioritize Single Nucleotide Polymorphisms in TLRs Signaling Pathway Genes
dc.typeArticle
dc.citation.volume5
dc.citation.issue2
dc.citation.spage65
dc.citation.epage79
dc.citation.indexWeb of science


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