Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA.
Bioinformatics. 2022 Sep 15;38(18):4442-4445. doi: 10.1093/bioinformatics/btac524.
In the post genome-wide association study (GWAS) era, omics techniques have characterized information beyond genomic variants to include cell and tissue type-specific gene transcription, transcription factor binding sites, expression quantitative trait loci (eQTL) and many other biological layers. Analysis of omics data and its integration has in turn improved the functional interpretation of disease-associated genetic variants. Over 170 000 transcriptomic and epigenomic datasets corresponding to studies of various cell and tissue types under specific disease, treatment and exposure conditions are available in the Gene Expression Omnibus resource. Although these datasets are valuable to guide the design of experimental validation studies to understand the function of disease-associated genetic loci, in their raw form, they are not helpful to experimental researchers who lack adequate computational resources or experience analyzing omics data. We sought to create an integrated re-source of tissue-specific results from omics studies that is guided by disease-specific knowledge to facilitate the design of experiments that can provide biologically meaningful insights into genetic associations.
We designed the Reducing Associations by Linking Genes and omics Results web app to provide multi-layered omics information based on results from GWAS, transcriptomic, epigenomic and eQTL studies for gene-centric analysis and visualization. With a focus on asthma datasets, the integrated omics results it contains facilitate the formulation of hypotheses related to airways disease-associated genes and can be addressed with experimental validation studies.
The REALGAR web app is available at: http://realgar.org/. The source code is available at: https://github.com/HimesGroup/realgar.
Supplementary data are available at Bioinformatics online.
在后全基因组关联研究（GWAS）时代，组学技术已经超越了基因组变异，描述了包括细胞和组织类型特异性基因转录、转录因子结合位点、表达数量性状基因座（eQTL）和许多其他生物学层的信息。对组学数据的分析及其整合反过来又提高了与疾病相关的遗传变异的功能解释。在基因表达综合数据库资源中，有超过 170000 个转录组学和表观基因组学数据集，这些数据集对应于各种细胞和组织类型在特定疾病、治疗和暴露条件下的研究。尽管这些数据集对于指导实验验证研究的设计以了解与疾病相关的遗传基因座的功能非常有价值，但在其原始形式下，对于缺乏足够计算资源或分析组学数据经验的实验研究人员来说，它们没有帮助。我们试图创建一个由疾病特异性知识指导的组织特异性结果的综合资源，以促进可以为遗传关联提供生物学意义上的见解的实验设计。
我们设计了 Reducing Associations by Linking Genes and omics Results 网络应用程序，以提供基于 GWAS、转录组学、表观基因组学和 eQTL 研究结果的基因中心分析和可视化的多层次组学信息。该应用程序侧重于哮喘数据集，其中包含的综合组学结果有助于形成与气道疾病相关基因相关的假说，并可以通过实验验证研究来解决。
REALGAR 网络应用程序可在以下网址获得：http://realgar.org/。源代码可在以下网址获得：https://github.com/HimesGroup/realgar。
补充数据可在生物信息学在线获得。
