GoFCards: an integrated database and analytic platform for gain of function variants in humans
Gain-of-function (GOF) variants, which introduce new or amplify protein functions, are essential for understanding disease mechanisms. Despite advances in genomics and functional research, identifying and analyzing pathogenic GOF variants remains challenging owing to fragmented data and database lim...
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Veröffentlicht in: | Nucleic acids research 2024-11 |
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creator | Zhao, Wenjing Tao, Youfu Xiong, Jiayi Liu, Lei Wang, Zhongqing Shao, Chuhan Shang, Ling Hu, Yue Xu, Yishu Su, Yingluo Yu, Jiahui Feng, Tianyi Xie, Junyi Xu, Huijuan Zhang, Zijun Peng, Jiayi Wu, Jianbin Zhang, Yuchang Zhu, Shaobo Xia, Kun Tang, Beisha Zhao, Guihu Li, Jinchen Li, Bin |
description | Gain-of-function (GOF) variants, which introduce new or amplify protein functions, are essential for understanding disease mechanisms. Despite advances in genomics and functional research, identifying and analyzing pathogenic GOF variants remains challenging owing to fragmented data and database limitations, underscoring the difficulty in accessing critical genetic information. To address this challenge, we manually reviewed the literature, pinpointing 3089 single-nucleotide variants and 72 insertions and deletions in 579 genes associated with 1299 diseases from 2069 studies, and integrated these with the 3.5 million predicted GOF variants. Our approach is complemented by a proprietary scoring system that prioritizes GOF variants on the basis of the evidence supporting their GOF effects and provides predictive scores for variants that lack existing documentation. We then developed a database named GoFCards for general geneticists and clinicians to easily obtain GOF variants in humans (http://www.genemed.tech/gofcards). This database also contains data from >150 sources and offers comprehensive variant-level and gene-level annotations, with the aim of providing users with convenient access to detailed and relevant genetic information. Furthermore, GoFCards empowers users with limited bioinformatic skills to analyze and annotate genetic data, and prioritize GOF variants. GoFCards offers an efficient platform for interpreting GOF variants and thereby advancing genetic research. |
doi_str_mv | 10.1093/nar/gkae1079 |
format | Article |
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Despite advances in genomics and functional research, identifying and analyzing pathogenic GOF variants remains challenging owing to fragmented data and database limitations, underscoring the difficulty in accessing critical genetic information. To address this challenge, we manually reviewed the literature, pinpointing 3089 single-nucleotide variants and 72 insertions and deletions in 579 genes associated with 1299 diseases from 2069 studies, and integrated these with the 3.5 million predicted GOF variants. Our approach is complemented by a proprietary scoring system that prioritizes GOF variants on the basis of the evidence supporting their GOF effects and provides predictive scores for variants that lack existing documentation. We then developed a database named GoFCards for general geneticists and clinicians to easily obtain GOF variants in humans (http://www.genemed.tech/gofcards). This database also contains data from >150 sources and offers comprehensive variant-level and gene-level annotations, with the aim of providing users with convenient access to detailed and relevant genetic information. Furthermore, GoFCards empowers users with limited bioinformatic skills to analyze and annotate genetic data, and prioritize GOF variants. GoFCards offers an efficient platform for interpreting GOF variants and thereby advancing genetic research.</description><identifier>ISSN: 0305-1048</identifier><identifier>ISSN: 1362-4962</identifier><identifier>EISSN: 1362-4962</identifier><identifier>DOI: 10.1093/nar/gkae1079</identifier><identifier>PMID: 39578693</identifier><language>eng</language><publisher>England</publisher><ispartof>Nucleic acids research, 2024-11</ispartof><rights>The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c933-c6ea69e6687d3ff68f4f180a62d2f3c06c49bc3147f15a65e73299bad400cf083</cites><orcidid>0000-0003-3335-9303 ; 0000-0001-8090-6002 ; 0000-0002-5544-7788 ; 0000-0003-2120-1576 ; 0000-0002-8374-1087</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,781,785,865,27929,27930</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39578693$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhao, Wenjing</creatorcontrib><creatorcontrib>Tao, Youfu</creatorcontrib><creatorcontrib>Xiong, Jiayi</creatorcontrib><creatorcontrib>Liu, Lei</creatorcontrib><creatorcontrib>Wang, Zhongqing</creatorcontrib><creatorcontrib>Shao, Chuhan</creatorcontrib><creatorcontrib>Shang, Ling</creatorcontrib><creatorcontrib>Hu, Yue</creatorcontrib><creatorcontrib>Xu, Yishu</creatorcontrib><creatorcontrib>Su, Yingluo</creatorcontrib><creatorcontrib>Yu, Jiahui</creatorcontrib><creatorcontrib>Feng, Tianyi</creatorcontrib><creatorcontrib>Xie, Junyi</creatorcontrib><creatorcontrib>Xu, Huijuan</creatorcontrib><creatorcontrib>Zhang, Zijun</creatorcontrib><creatorcontrib>Peng, Jiayi</creatorcontrib><creatorcontrib>Wu, Jianbin</creatorcontrib><creatorcontrib>Zhang, Yuchang</creatorcontrib><creatorcontrib>Zhu, Shaobo</creatorcontrib><creatorcontrib>Xia, Kun</creatorcontrib><creatorcontrib>Tang, Beisha</creatorcontrib><creatorcontrib>Zhao, Guihu</creatorcontrib><creatorcontrib>Li, Jinchen</creatorcontrib><creatorcontrib>Li, Bin</creatorcontrib><title>GoFCards: an integrated database and analytic platform for gain of function variants in humans</title><title>Nucleic acids research</title><addtitle>Nucleic Acids Res</addtitle><description>Gain-of-function (GOF) variants, which introduce new or amplify protein functions, are essential for understanding disease mechanisms. Despite advances in genomics and functional research, identifying and analyzing pathogenic GOF variants remains challenging owing to fragmented data and database limitations, underscoring the difficulty in accessing critical genetic information. To address this challenge, we manually reviewed the literature, pinpointing 3089 single-nucleotide variants and 72 insertions and deletions in 579 genes associated with 1299 diseases from 2069 studies, and integrated these with the 3.5 million predicted GOF variants. Our approach is complemented by a proprietary scoring system that prioritizes GOF variants on the basis of the evidence supporting their GOF effects and provides predictive scores for variants that lack existing documentation. We then developed a database named GoFCards for general geneticists and clinicians to easily obtain GOF variants in humans (http://www.genemed.tech/gofcards). This database also contains data from >150 sources and offers comprehensive variant-level and gene-level annotations, with the aim of providing users with convenient access to detailed and relevant genetic information. Furthermore, GoFCards empowers users with limited bioinformatic skills to analyze and annotate genetic data, and prioritize GOF variants. GoFCards offers an efficient platform for interpreting GOF variants and thereby advancing genetic research.</description><issn>0305-1048</issn><issn>1362-4962</issn><issn>1362-4962</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNo9kLFOwzAQQC0EoqWwMSOPDITaOceJ2VBFC1Ills5EF8cugcQptoPUvyeoheHupNPTGx4h15zdc6Zg7tDPt59oOMvVCZlykGkilExPyZQByxLORDEhFyF8MMYFz8Q5mYDK8kIqmJK3Vb9coK_DA0VHGxfN1mM0Na0xYoXBjO96HGz3sdF012K0ve_ouOgWG0d7S-3gdGx6R7_RN-hiGD30fejQhUtyZrEN5up4Z2SzfNosnpP16-pl8bhOtAJItDQolZGyyGuwVhZWWF4wlGmdWtBMaqEqDVzklmcoM5NDqlSFtWBMW1bAjNwetDvffw0mxLJrgjZti870QyiBQ8oFAJcjendAte9D8MaWO9906PclZ-Vv0HIMWv4FHfGbo3moOlP_w38F4QfKXHLO</recordid><startdate>20241123</startdate><enddate>20241123</enddate><creator>Zhao, Wenjing</creator><creator>Tao, Youfu</creator><creator>Xiong, Jiayi</creator><creator>Liu, Lei</creator><creator>Wang, Zhongqing</creator><creator>Shao, Chuhan</creator><creator>Shang, Ling</creator><creator>Hu, Yue</creator><creator>Xu, Yishu</creator><creator>Su, Yingluo</creator><creator>Yu, Jiahui</creator><creator>Feng, Tianyi</creator><creator>Xie, Junyi</creator><creator>Xu, Huijuan</creator><creator>Zhang, Zijun</creator><creator>Peng, Jiayi</creator><creator>Wu, Jianbin</creator><creator>Zhang, Yuchang</creator><creator>Zhu, Shaobo</creator><creator>Xia, Kun</creator><creator>Tang, Beisha</creator><creator>Zhao, Guihu</creator><creator>Li, Jinchen</creator><creator>Li, Bin</creator><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-3335-9303</orcidid><orcidid>https://orcid.org/0000-0001-8090-6002</orcidid><orcidid>https://orcid.org/0000-0002-5544-7788</orcidid><orcidid>https://orcid.org/0000-0003-2120-1576</orcidid><orcidid>https://orcid.org/0000-0002-8374-1087</orcidid></search><sort><creationdate>20241123</creationdate><title>GoFCards: an integrated database and analytic platform for gain of function variants in humans</title><author>Zhao, Wenjing ; 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Despite advances in genomics and functional research, identifying and analyzing pathogenic GOF variants remains challenging owing to fragmented data and database limitations, underscoring the difficulty in accessing critical genetic information. To address this challenge, we manually reviewed the literature, pinpointing 3089 single-nucleotide variants and 72 insertions and deletions in 579 genes associated with 1299 diseases from 2069 studies, and integrated these with the 3.5 million predicted GOF variants. Our approach is complemented by a proprietary scoring system that prioritizes GOF variants on the basis of the evidence supporting their GOF effects and provides predictive scores for variants that lack existing documentation. We then developed a database named GoFCards for general geneticists and clinicians to easily obtain GOF variants in humans (http://www.genemed.tech/gofcards). 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title | GoFCards: an integrated database and analytic platform for gain of function variants in humans |
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