PDF(Pubmed)
Abstract：
The Cancer Genome Atlas (TCGA) and analogous projects have yielded invaluable tumor-associated genomic data. Despite several web-based platforms designed to enhance accessibility, certain analyses require prior bioinformatic expertise. To address this need, we developed Gene ENrichment Identifier (GENI, https://www.shaullab.com/geni), which is designed to promptly compute correlations for genes of interest against the entire transcriptome and rank them against well-established biological gene sets. Additionally, it generates comprehensive tables containing genes of interest and their corresponding correlation coefficients, presented in publication-quality graphs. Furthermore, GENI has the capability to analyze multiple genes simultaneously within a given gene set, elucidating their significance within a specific biological context. Overall, GENI\'s user-friendly interface simplifies the biological interpretation and analysis of cancer patient-associated data, advancing the understanding of cancer biology and accelerating scientific discoveries.
摘要：
癌症基因组图谱（TCGA）和类似的项目已经产生了宝贵的肿瘤相关基因组数据。尽管有几个基于Web的平台旨在增强可访问性，某些分析需要事先的生物信息学专业知识。为了满足这一需求，我们开发了基因富集标识符(GENI，https://www.shaullab.com/geni),它旨在快速计算感兴趣的基因与整个转录组的相关性，并将它们与建立良好的生物基因集进行排序。此外,它生成包含感兴趣基因及其相应相关系数的综合表格，在出版质量图中呈现。此外，GENI有能力同时分析给定基因集中的多个基因，阐明它们在特定生物学背景下的意义。总的来说,GENI的用户友好界面简化了癌症患者相关数据的生物学解释和分析，推进对癌症生物学的理解和加速科学发现。
