GOAT: Genetic Output Analysis Tool: An open source GWAS and genomic region visualization tool

Beatriz S. Kanzki, Victor Dupuy, C. Urvoy, Fodil Belghait, A. April, F. Harvey, François-Christophe Marois-Blanchet, M. Phillips, J. Tremblay, P. Hamet
{"title":"GOAT: Genetic Output Analysis Tool: An open source GWAS and genomic region visualization tool","authors":"Beatriz S. Kanzki, Victor Dupuy, C. Urvoy, Fodil Belghait, A. April, F. Harvey, François-Christophe Marois-Blanchet, M. Phillips, J. Tremblay, P. Hamet","doi":"10.1145/2896338.2897729","DOIUrl":null,"url":null,"abstract":"Genome wide association studies (GWAS) are a widely used approach in genetic research to identify genes or genetic variants involved in human diseases. Each GWAS examines millions of unique single nucleotide polymorphisms (SNPs) for their association to phenotypic traits and diseases. In the context of identifying complex associations in large patient cohorts, this type of study involves a vast amount of clinical and genetic data. In order to analyze these complex datasets efficiently we have developed the Genetic Output Analysis Tool (GOAT) to improve visualization and annotation of GWAS data. GOAT offers interactive search capabilities of GWAS results via specific queries to identify significant associations between multiple SNPs and phenotypes. GOAT was designed to be scalable and operate on top of \"Big Data\" technologies. The software interface offers researchers new visualization tools to help analyze this complex data. It is programmed in python and can be connected directly to any database using an Apache server. This paper outlines some of the GOAT's leading features and characteristics and compares them to existing open source GWAS visualization tools such as Locus Zoom and the Integrative Genomics Viewer (IGV). We also present future development plans for GOAT in order to provide researchers with improved performance, the visualization tools and ability to mine GWAS data for the most interesting and relevant information from their data.","PeriodicalId":146447,"journal":{"name":"Proceedings of the 6th International Conference on Digital Health Conference","volume":"102 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 6th International Conference on Digital Health Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2896338.2897729","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

Genome wide association studies (GWAS) are a widely used approach in genetic research to identify genes or genetic variants involved in human diseases. Each GWAS examines millions of unique single nucleotide polymorphisms (SNPs) for their association to phenotypic traits and diseases. In the context of identifying complex associations in large patient cohorts, this type of study involves a vast amount of clinical and genetic data. In order to analyze these complex datasets efficiently we have developed the Genetic Output Analysis Tool (GOAT) to improve visualization and annotation of GWAS data. GOAT offers interactive search capabilities of GWAS results via specific queries to identify significant associations between multiple SNPs and phenotypes. GOAT was designed to be scalable and operate on top of "Big Data" technologies. The software interface offers researchers new visualization tools to help analyze this complex data. It is programmed in python and can be connected directly to any database using an Apache server. This paper outlines some of the GOAT's leading features and characteristics and compares them to existing open source GWAS visualization tools such as Locus Zoom and the Integrative Genomics Viewer (IGV). We also present future development plans for GOAT in order to provide researchers with improved performance, the visualization tools and ability to mine GWAS data for the most interesting and relevant information from their data.
GOAT:基因输出分析工具:一个开源的GWAS和基因组区域可视化工具
基因组全关联研究(GWAS)是遗传研究中广泛使用的一种方法,用于鉴定与人类疾病有关的基因或遗传变异。每个GWAS检查数百万个独特的单核苷酸多态性(snp),以了解它们与表型性状和疾病的关联。在确定大型患者队列中复杂关联的背景下,这种类型的研究涉及大量的临床和遗传数据。为了有效地分析这些复杂的数据集,我们开发了遗传输出分析工具(GOAT)来改进GWAS数据的可视化和注释。GOAT通过特定查询提供GWAS结果的交互式搜索功能,以识别多个snp和表型之间的显著关联。GOAT被设计为可扩展的,并在“大数据”技术之上运行。软件界面为研究人员提供了新的可视化工具来帮助分析这些复杂的数据。它是用python编程的,可以使用Apache服务器直接连接到任何数据库。本文概述了GOAT的一些主要功能和特点,并将它们与现有的开源GWAS可视化工具(如Locus Zoom和Integrative Genomics Viewer (IGV))进行了比较。我们还提出了GOAT的未来发展计划,以便为研究人员提供改进的性能、可视化工具和从数据中挖掘最有趣和最相关信息的GWAS数据的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信