Literature DB >> 33264727

Refinement of coding SNPs in the human aryl hydrocarbon receptor gene using ISNPranker: An integrative-SNP ranking web-tool.

Younes Aftabi1, Saleh Rafei2, Habib Zarredar3, Amir Amiri-Sadeghan3, Mohsen Akbari-Shahpar4, Zahra Khoshkam5, Ensiyeh Seyedrezazadeh3, Majid Khalili3, Faramarz Mehrnejad6, Sasan Fereidouni7, B Paige Lawrence8.   

Abstract

Different bioinformatic methods apply various approaches to predict how much the effect of a SNP could be deleterious and therefore their results may differ significantly. However, variation studies often need to consider an integrated prediction result to analyze the effect of SNPs. To address this problem, we used an algorithm to map ordinal predictions to a numeral space and averaging them, and based on it we developed the ISNPranker web-tool (http://isnpranker.semilab.ir/). It takes heterogonous outputs of different predictors and generates integrated numerical predictions and ranks SNPs based on them. Afterward, we used ISNPranker to identify the most deleterious coding SNPs (cSNPs) of the human aryl hydrocarbon receptor (AHR) gene. AHR is a ligand-activated transcription factor that governs many molecular and cellular mechanisms and cSNPs may affect its structure, interactions, and function. Forty validated cSNPs of AHR were initially analyzed using 16 publicly available SNP analyzers and the results were introduced to the ISNPranker and integrated predictions were obtained. The cSNPs were ranked in 34 levels of danger and rs200257782 in the ARNT dimerization domain (ADD121-289) of AHR was identified as the most deleterious cSNP. The rs148360742, which affect ADD40-79 and Hsp90 binding domain (HBD27-79) was in the second rank and the third and fourth ranks were occupied by ADD121-289-located variations rs571123681 and rs141667112 respectively. In conclusion, we introduced ISNPranker, which is a web-tool for integrative ranking of SNPs, and we showed that AHR structure and function may be highly sensitive to the cSNPs in the ARNT dimerization domain.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Aryl hydrocarbon receptor; Bioinformatics; Function; ISNPranker; Polymorphism; Structure

Mesh:

Substances:

Year:  2020        PMID: 33264727      PMCID: PMC8815319          DOI: 10.1016/j.compbiolchem.2020.107416

Source DB:  PubMed          Journal:  Comput Biol Chem        ISSN: 1476-9271            Impact factor:   2.877


  70 in total

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Journal:  Toxicol Lett       Date:  2018-04-25       Impact factor: 4.372

2.  Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information.

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Journal:  Bioinformatics       Date:  2006-08-07       Impact factor: 6.937

Review 3.  Bioinformatic tools for identifying disease gene and SNP candidates.

Authors:  Sean D Mooney; Vidhya G Krishnan; Uday S Evani
Journal:  Methods Mol Biol       Date:  2010

Review 4.  Aryl hydrocarbon receptor (AHR): "pioneer member" of the basic-helix/loop/helix per-Arnt-sim (bHLH/PAS) family of "sensors" of foreign and endogenous signals.

Authors:  Daniel W Nebert
Journal:  Prog Lipid Res       Date:  2017-06-09       Impact factor: 16.195

5.  A TDO2-AhR signaling axis facilitates anoikis resistance and metastasis in triple-negative breast cancer.

Authors:  Nicholas C D'Amato; Thomas J Rogers; Michael A Gordon; Lisa I Greene; Dawn R Cochrane; Nicole S Spoelstra; Travis G Nemkov; Angelo D'Alessandro; Kirk C Hansen; Jennifer K Richer
Journal:  Cancer Res       Date:  2015-09-11       Impact factor: 12.701

6.  SNPeffect 4.0: on-line prediction of molecular and structural effects of protein-coding variants.

Authors:  Greet De Baets; Joost Van Durme; Joke Reumers; Sebastian Maurer-Stroh; Peter Vanhee; Joaquin Dopazo; Joost Schymkowitz; Frederic Rousseau
Journal:  Nucleic Acids Res       Date:  2011-11-10       Impact factor: 16.971

7.  ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules.

Authors:  Haim Ashkenazy; Shiran Abadi; Eric Martz; Ofer Chay; Itay Mayrose; Tal Pupko; Nir Ben-Tal
Journal:  Nucleic Acids Res       Date:  2016-05-10       Impact factor: 16.971

8.  PMut: a web-based tool for the annotation of pathological variants on proteins, 2017 update.

Authors:  Víctor López-Ferrando; Andrea Gazzo; Xavier de la Cruz; Modesto Orozco; Josep Ll Gelpí
Journal:  Nucleic Acids Res       Date:  2017-07-03       Impact factor: 16.971

9.  Computational approaches for predicting the biological effect of p53 missense mutations: a comparison of three sequence analysis based methods.

Authors:  Ewy Mathe; Magali Olivier; Shunsuke Kato; Chikashi Ishioka; Pierre Hainaut; Sean V Tavtigian
Journal:  Nucleic Acids Res       Date:  2006-03-06       Impact factor: 16.971

10.  Collective judgment predicts disease-associated single nucleotide variants.

Authors:  Emidio Capriotti; Russ B Altman; Yana Bromberg
Journal:  BMC Genomics       Date:  2013-05-28       Impact factor: 3.969

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  1 in total

1.  Association of NOS3-c.894G>T transversion with susceptibility to metabolic syndrome in Azar-cohort population: A case-control study and in silico analysis of the SNP molecular effects.

Authors:  Ensiyeh Seyedrezazadeh; Elnaz Faramarzi; Nasim Bakhtiyari; Atefeh Ansarin; Neda Gilani; Amir Amiri-Sadeghan; Maryam Seyyedi; Khalil Ansarin; Younes Aftabi
Journal:  Iran J Basic Med Sci       Date:  2021-03       Impact factor: 2.699

  1 in total

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