In-Silico Method for Predicting Pathogenic Missense Variants Using Online Tools: AURKA Gene as a Model
analysis provides a fast, simple, and cost-free method for identifying potentially pathogenic single nucleotide variants. To propose a simple and relatively fast method for the prediction of variant pathogenicity using free online (IS) tools with gene as a model. We aim to propose a methodology to p...
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Veröffentlicht in: | Iranian journal of biotechnology 2024-04, Vol.22 (2), p.e3787-e3787 |
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container_title | Iranian journal of biotechnology |
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creator | Maciel-Cruz, Eric Jonathan Figuera-Villanueva, Luis Eduardo Gómez-Flores-Ramos, Liliana Hernández-Peña, Rubiceli Gallegos-Arreola, Martha Patricia |
description | analysis provides a fast, simple, and cost-free method for identifying potentially pathogenic single nucleotide variants.
To propose a simple and relatively fast method for the prediction of variant pathogenicity using free online
(IS) tools with
gene as a model.
We aim to propose a methodology to predict variants with high pathogenic potential using computational analysis, using
gene as model. We predicted a protein model and analyzed 209 out of 64,369
variants obtained from Ensembl database. We used bioinformatic tools to predict pathogenicity. The results were compared through the VarSome website, which includes its own pathogenicity score and the American College of Medical Genetics (ACMG) classification.
Out of the 209 analyzed variants, 16 were considered pathogenic, and 13 were located in the catalytic domain. The most frequent protein changes were size and hydrophobicity modifications of amino acids. Proline and Glycine amino acid substitutions were the most frequent changes predicted as pathogenic. These bioinformatic tools predicted functional changes, such as protein up or down-regulation, gain or loss of molecule interactions, and structural protein modifications. When compared to the ACMG classification, 10 out of 16 variants were considered likely pathogenic, with 7 out of 10 changes at Proline/Glycine substitutions.
This method allows quick and cost-free bulk variant screening to identify variants with pathogenic potential for further association and/or functional studies. |
doi_str_mv | 10.30498/ijb.2024.413800.3787 |
format | Article |
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To propose a simple and relatively fast method for the prediction of variant pathogenicity using free online
(IS) tools with
gene as a model.
We aim to propose a methodology to predict variants with high pathogenic potential using computational analysis, using
gene as model. We predicted a protein model and analyzed 209 out of 64,369
variants obtained from Ensembl database. We used bioinformatic tools to predict pathogenicity. The results were compared through the VarSome website, which includes its own pathogenicity score and the American College of Medical Genetics (ACMG) classification.
Out of the 209 analyzed variants, 16 were considered pathogenic, and 13 were located in the catalytic domain. The most frequent protein changes were size and hydrophobicity modifications of amino acids. Proline and Glycine amino acid substitutions were the most frequent changes predicted as pathogenic. These bioinformatic tools predicted functional changes, such as protein up or down-regulation, gain or loss of molecule interactions, and structural protein modifications. When compared to the ACMG classification, 10 out of 16 variants were considered likely pathogenic, with 7 out of 10 changes at Proline/Glycine substitutions.
This method allows quick and cost-free bulk variant screening to identify variants with pathogenic potential for further association and/or functional studies.</description><identifier>ISSN: 1728-3043</identifier><identifier>EISSN: 2322-2921</identifier><identifier>DOI: 10.30498/ijb.2024.413800.3787</identifier><identifier>PMID: 39220333</identifier><language>eng</language><publisher>Iran: National Institute of Genetic Engineering and Biotechnology</publisher><subject>Brief Report</subject><ispartof>Iranian journal of biotechnology, 2024-04, Vol.22 (2), p.e3787-e3787</ispartof><rights>Copyright: © 2021 The Author(s); Published by Iranian Journal of Biotechnology.</rights><rights>Copyright: © 2021 The Author(s); Published by Iranian Journal of Biotechnology</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11364922/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11364922/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27903,27904,53769,53771</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39220333$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Maciel-Cruz, Eric Jonathan</creatorcontrib><creatorcontrib>Figuera-Villanueva, Luis Eduardo</creatorcontrib><creatorcontrib>Gómez-Flores-Ramos, Liliana</creatorcontrib><creatorcontrib>Hernández-Peña, Rubiceli</creatorcontrib><creatorcontrib>Gallegos-Arreola, Martha Patricia</creatorcontrib><title>In-Silico Method for Predicting Pathogenic Missense Variants Using Online Tools: AURKA Gene as a Model</title><title>Iranian journal of biotechnology</title><addtitle>Iran J Biotechnol</addtitle><description>analysis provides a fast, simple, and cost-free method for identifying potentially pathogenic single nucleotide variants.
To propose a simple and relatively fast method for the prediction of variant pathogenicity using free online
(IS) tools with
gene as a model.
We aim to propose a methodology to predict variants with high pathogenic potential using computational analysis, using
gene as model. We predicted a protein model and analyzed 209 out of 64,369
variants obtained from Ensembl database. We used bioinformatic tools to predict pathogenicity. The results were compared through the VarSome website, which includes its own pathogenicity score and the American College of Medical Genetics (ACMG) classification.
Out of the 209 analyzed variants, 16 were considered pathogenic, and 13 were located in the catalytic domain. The most frequent protein changes were size and hydrophobicity modifications of amino acids. Proline and Glycine amino acid substitutions were the most frequent changes predicted as pathogenic. These bioinformatic tools predicted functional changes, such as protein up or down-regulation, gain or loss of molecule interactions, and structural protein modifications. When compared to the ACMG classification, 10 out of 16 variants were considered likely pathogenic, with 7 out of 10 changes at Proline/Glycine substitutions.
This method allows quick and cost-free bulk variant screening to identify variants with pathogenic potential for further association and/or functional studies.</description><subject>Brief Report</subject><issn>1728-3043</issn><issn>2322-2921</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNpVkN1KAzEQRoMotlYfQckLbE0ybbLxRkrRWmxp0dbbJZtk25RtUjar4Nsb8Qe9GjjzzcdwELqkpA9kIPNrtyv7jLBBf0AhJ4mKXByhLgPGMiYZPUZdKliepTR00FmMO0KGXBI4RR2QjBEA6KJq6rNnVzsd8Ny222BwFRq8bKxxunV-g5cq0Y31TuO5i9H6aPGLapzybcTr-BlZ-Np5i1ch1PEGj9ZPjyM8sYmoiBWeB2Prc3RSqTrai-_ZQ-v7u9X4IZstJtPxaJYdqBRtptKzXIApda4MV2CZ5BaGRhNOQFjFjSqNzHmlQUpZiYrmWkgpDCmTAk2hh26_eg-v5d4abX3bqLo4NG6vmvciKFf833i3LTbhraAU-CBZSQ1Xfxt-T3-UwQeibnBf</recordid><startdate>202404</startdate><enddate>202404</enddate><creator>Maciel-Cruz, Eric Jonathan</creator><creator>Figuera-Villanueva, Luis Eduardo</creator><creator>Gómez-Flores-Ramos, Liliana</creator><creator>Hernández-Peña, Rubiceli</creator><creator>Gallegos-Arreola, Martha Patricia</creator><general>National Institute of Genetic Engineering and Biotechnology</general><scope>NPM</scope><scope>5PM</scope></search><sort><creationdate>202404</creationdate><title>In-Silico Method for Predicting Pathogenic Missense Variants Using Online Tools: AURKA Gene as a Model</title><author>Maciel-Cruz, Eric Jonathan ; Figuera-Villanueva, Luis Eduardo ; Gómez-Flores-Ramos, Liliana ; Hernández-Peña, Rubiceli ; Gallegos-Arreola, Martha Patricia</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p197t-a304673dbc8ad6a3e296e35dc06037ea6dabd986fc3999f7f18c7997d0b800c13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Brief Report</topic><toplevel>online_resources</toplevel><creatorcontrib>Maciel-Cruz, Eric Jonathan</creatorcontrib><creatorcontrib>Figuera-Villanueva, Luis Eduardo</creatorcontrib><creatorcontrib>Gómez-Flores-Ramos, Liliana</creatorcontrib><creatorcontrib>Hernández-Peña, Rubiceli</creatorcontrib><creatorcontrib>Gallegos-Arreola, Martha Patricia</creatorcontrib><collection>PubMed</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Iranian journal of biotechnology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Maciel-Cruz, Eric Jonathan</au><au>Figuera-Villanueva, Luis Eduardo</au><au>Gómez-Flores-Ramos, Liliana</au><au>Hernández-Peña, Rubiceli</au><au>Gallegos-Arreola, Martha Patricia</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>In-Silico Method for Predicting Pathogenic Missense Variants Using Online Tools: AURKA Gene as a Model</atitle><jtitle>Iranian journal of biotechnology</jtitle><addtitle>Iran J Biotechnol</addtitle><date>2024-04</date><risdate>2024</risdate><volume>22</volume><issue>2</issue><spage>e3787</spage><epage>e3787</epage><pages>e3787-e3787</pages><issn>1728-3043</issn><eissn>2322-2921</eissn><abstract>analysis provides a fast, simple, and cost-free method for identifying potentially pathogenic single nucleotide variants.
To propose a simple and relatively fast method for the prediction of variant pathogenicity using free online
(IS) tools with
gene as a model.
We aim to propose a methodology to predict variants with high pathogenic potential using computational analysis, using
gene as model. We predicted a protein model and analyzed 209 out of 64,369
variants obtained from Ensembl database. We used bioinformatic tools to predict pathogenicity. The results were compared through the VarSome website, which includes its own pathogenicity score and the American College of Medical Genetics (ACMG) classification.
Out of the 209 analyzed variants, 16 were considered pathogenic, and 13 were located in the catalytic domain. The most frequent protein changes were size and hydrophobicity modifications of amino acids. Proline and Glycine amino acid substitutions were the most frequent changes predicted as pathogenic. These bioinformatic tools predicted functional changes, such as protein up or down-regulation, gain or loss of molecule interactions, and structural protein modifications. When compared to the ACMG classification, 10 out of 16 variants were considered likely pathogenic, with 7 out of 10 changes at Proline/Glycine substitutions.
This method allows quick and cost-free bulk variant screening to identify variants with pathogenic potential for further association and/or functional studies.</abstract><cop>Iran</cop><pub>National Institute of Genetic Engineering and Biotechnology</pub><pmid>39220333</pmid><doi>10.30498/ijb.2024.413800.3787</doi><oa>free_for_read</oa></addata></record> |
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subjects | Brief Report |
title | In-Silico Method for Predicting Pathogenic Missense Variants Using Online Tools: AURKA Gene as a Model |
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