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.