疾病egps:基于基因型和表型的遗传病辅助诊断系统。

IF 4.4 3区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Bioinformatics Pub Date : 2023-09-02 DOI:10.1093/bioinformatics/btad517
Daoyi Huang, Jianping Jiang, Tingting Zhao, Shengnan Wu, Pin Li, Yongfen Lyu, Jincai Feng, Mingyue Wei, Zhixing Zhu, Jianlei Gu, Yongyong Ren, Guangjun Yu, Hui Lu
{"title":"疾病egps:基于基因型和表型的遗传病辅助诊断系统。","authors":"Daoyi Huang,&nbsp;Jianping Jiang,&nbsp;Tingting Zhao,&nbsp;Shengnan Wu,&nbsp;Pin Li,&nbsp;Yongfen Lyu,&nbsp;Jincai Feng,&nbsp;Mingyue Wei,&nbsp;Zhixing Zhu,&nbsp;Jianlei Gu,&nbsp;Yongyong Ren,&nbsp;Guangjun Yu,&nbsp;Hui Lu","doi":"10.1093/bioinformatics/btad517","DOIUrl":null,"url":null,"abstract":"<p><strong>Summary: </strong>The next-generation sequencing brought opportunities for the diagnosis of genetic disorders due to its high-throughput capabilities. However, the majority of existing methods were limited to only sequencing candidate variants, and the process of linking these variants to a diagnosis of genetic disorders still required medical professionals to consult databases. Therefore, we introduce diseaseGPS, an integrated platform for the diagnosis of genetic disorders that combines both phenotype and genotype data for analysis. It offers not only a user-friendly GUI web application for those without a programming background but also scripts that can be executed in batch mode for bioinformatics professionals. The genetic and phenotypic data are integrated using the ACMG-Bayes method and a novel phenotypic similarity method, to prioritize the results of genetic disorders. diseaseGPS was evaluated on 6085 cases from Deciphering Developmental Disorders project and 187 cases from Shanghai Children's hospital. The results demonstrated that diseaseGPS performed better than other commonly used methods.</p><p><strong>Availability and implementation: </strong>diseaseGPS is available to freely accessed at https://diseasegps.sjtu.edu.cn with source code at https://github.com/BioHuangDY/diseaseGPS.</p>","PeriodicalId":8903,"journal":{"name":"Bioinformatics","volume":"39 9","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500091/pdf/","citationCount":"0","resultStr":"{\"title\":\"diseaseGPS: auxiliary diagnostic system for genetic disorders based on genotype and phenotype.\",\"authors\":\"Daoyi Huang,&nbsp;Jianping Jiang,&nbsp;Tingting Zhao,&nbsp;Shengnan Wu,&nbsp;Pin Li,&nbsp;Yongfen Lyu,&nbsp;Jincai Feng,&nbsp;Mingyue Wei,&nbsp;Zhixing Zhu,&nbsp;Jianlei Gu,&nbsp;Yongyong Ren,&nbsp;Guangjun Yu,&nbsp;Hui Lu\",\"doi\":\"10.1093/bioinformatics/btad517\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Summary: </strong>The next-generation sequencing brought opportunities for the diagnosis of genetic disorders due to its high-throughput capabilities. However, the majority of existing methods were limited to only sequencing candidate variants, and the process of linking these variants to a diagnosis of genetic disorders still required medical professionals to consult databases. Therefore, we introduce diseaseGPS, an integrated platform for the diagnosis of genetic disorders that combines both phenotype and genotype data for analysis. It offers not only a user-friendly GUI web application for those without a programming background but also scripts that can be executed in batch mode for bioinformatics professionals. The genetic and phenotypic data are integrated using the ACMG-Bayes method and a novel phenotypic similarity method, to prioritize the results of genetic disorders. diseaseGPS was evaluated on 6085 cases from Deciphering Developmental Disorders project and 187 cases from Shanghai Children's hospital. The results demonstrated that diseaseGPS performed better than other commonly used methods.</p><p><strong>Availability and implementation: </strong>diseaseGPS is available to freely accessed at https://diseasegps.sjtu.edu.cn with source code at https://github.com/BioHuangDY/diseaseGPS.</p>\",\"PeriodicalId\":8903,\"journal\":{\"name\":\"Bioinformatics\",\"volume\":\"39 9\",\"pages\":\"\"},\"PeriodicalIF\":4.4000,\"publicationDate\":\"2023-09-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500091/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bioinformatics\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1093/bioinformatics/btad517\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/bioinformatics/btad517","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0

摘要

摘要:新一代测序由于其高通量能力,为遗传疾病的诊断带来了机会。然而,大多数现有的方法仅限于对候选变异进行测序,并且将这些变异与遗传疾病的诊断联系起来的过程仍然需要医学专业人员查阅数据库。因此,我们引入了disease egps,这是一个综合的遗传疾病诊断平台,结合了表型和基因型数据进行分析。它不仅为那些没有编程背景的人提供了一个用户友好的GUI web应用程序,而且还为生物信息学专业人员提供了可以批处理模式执行的脚本。利用ACMG-Bayes方法和一种新的表型相似性方法整合遗传和表型数据,对遗传疾病的结果进行优先排序。对来自破译发育障碍项目的6085例患儿和来自上海儿童医院的187例患儿进行egps评价。结果表明,疾病egps比其他常用的方法效果更好。可用性和实现:可以在https://diseasegps.sjtu.edu.cn免费访问疾病管理系统,源代码在https://github.com/BioHuangDY/diseaseGPS。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
diseaseGPS: auxiliary diagnostic system for genetic disorders based on genotype and phenotype.

Summary: The next-generation sequencing brought opportunities for the diagnosis of genetic disorders due to its high-throughput capabilities. However, the majority of existing methods were limited to only sequencing candidate variants, and the process of linking these variants to a diagnosis of genetic disorders still required medical professionals to consult databases. Therefore, we introduce diseaseGPS, an integrated platform for the diagnosis of genetic disorders that combines both phenotype and genotype data for analysis. It offers not only a user-friendly GUI web application for those without a programming background but also scripts that can be executed in batch mode for bioinformatics professionals. The genetic and phenotypic data are integrated using the ACMG-Bayes method and a novel phenotypic similarity method, to prioritize the results of genetic disorders. diseaseGPS was evaluated on 6085 cases from Deciphering Developmental Disorders project and 187 cases from Shanghai Children's hospital. The results demonstrated that diseaseGPS performed better than other commonly used methods.

Availability and implementation: diseaseGPS is available to freely accessed at https://diseasegps.sjtu.edu.cn with source code at https://github.com/BioHuangDY/diseaseGPS.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Bioinformatics
Bioinformatics 生物-生化研究方法
CiteScore
11.20
自引率
5.20%
发文量
753
审稿时长
2.1 months
期刊介绍: The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.
期刊最新文献
MEHunter: Transformer-based mobile element variant detection from long reads Metabolic syndrome may be more frequent in treatment-naive sarcoidosis patients. Coracle—A Machine Learning Framework to Identify Bacteria Associated with Continuous Variables CoSIA: an R Bioconductor package for CrOss Species Investigation and Analysis LncLocFormer: a Transformer-based deep learning model for multi-label lncRNA subcellular localization prediction by using localization-specific attention mechanism
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1