{"title":"GeniePool 2.0: advancing variant analysis through CHM13-T2T, AlphaMissense, gnomAD V4 integration, and variant co-occurrence queries.","authors":"Grisha Weintraub, Noam Hadar, Ehud Gudes, Shlomi Dolev, Ohad S Birk","doi":"10.1093/database/baae130","DOIUrl":null,"url":null,"abstract":"<p><p>Originally developed to meet the challenges of genomic data deluge, GeniePool emerged as a pioneering platform, enabling efficient storage, accessibility, and analysis of vast genomic datasets, enabled due to its data lake architecture. Building on this foundation, GeniePool 2.0 advances genomic analysis through the integration of cutting-edge variant databases, such as CHM13-T2T, AlphaMissense, and gnomAD V4, coupled with the capability for variant co-occurrence queries. This evolution offers an unprecedented level of granularity and scope in genomic analyses, from enhancing our understanding of variant pathogenicity and phenotypic associations to facilitating research collaborations. The introduction of CHM13-T2T provides a more accurate reference for human genetic variation, AlphaMissense enriches the platform with protein-level impact predictions of missense mutations, and gnomAD V4 offers a comprehensive view of human genetic diversity. Additionally, the innovative feature for variant co-occurrence analysis is pivotal for exploring the combined effects of genetic variations, advancing our comprehension of compound heterozygosity, epistasis, and polygenic risk factors in disease pathogenesis. GeniePool 2.0 is a comprehensive and scalable platform, which aims to enhance genomic data analysis and contribute to genomic research, potentially supporting new discoveries and clinical innovations. Database URL: https://GeniePool.link.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2024 ","pages":""},"PeriodicalIF":3.4000,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11673193/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Database: The Journal of Biological Databases and Curation","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/database/baae130","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Originally developed to meet the challenges of genomic data deluge, GeniePool emerged as a pioneering platform, enabling efficient storage, accessibility, and analysis of vast genomic datasets, enabled due to its data lake architecture. Building on this foundation, GeniePool 2.0 advances genomic analysis through the integration of cutting-edge variant databases, such as CHM13-T2T, AlphaMissense, and gnomAD V4, coupled with the capability for variant co-occurrence queries. This evolution offers an unprecedented level of granularity and scope in genomic analyses, from enhancing our understanding of variant pathogenicity and phenotypic associations to facilitating research collaborations. The introduction of CHM13-T2T provides a more accurate reference for human genetic variation, AlphaMissense enriches the platform with protein-level impact predictions of missense mutations, and gnomAD V4 offers a comprehensive view of human genetic diversity. Additionally, the innovative feature for variant co-occurrence analysis is pivotal for exploring the combined effects of genetic variations, advancing our comprehension of compound heterozygosity, epistasis, and polygenic risk factors in disease pathogenesis. GeniePool 2.0 is a comprehensive and scalable platform, which aims to enhance genomic data analysis and contribute to genomic research, potentially supporting new discoveries and clinical innovations. Database URL: https://GeniePool.link.
期刊介绍:
Huge volumes of primary data are archived in numerous open-access databases, and with new generation technologies becoming more common in laboratories, large datasets will become even more prevalent. The archiving, curation, analysis and interpretation of all of these data are a challenge. Database development and biocuration are at the forefront of the endeavor to make sense of this mounting deluge of data.
Database: The Journal of Biological Databases and Curation provides an open access platform for the presentation of novel ideas in database research and biocuration, and aims to help strengthen the bridge between database developers, curators, and users.