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HGCA2.0:基于 RNA-Seq 的. 基因共表达分析网络工具

HGCA2.0: An RNA-Seq Based Webtool for Gene Coexpression Analysis in .

机构信息

Centre of Systems Biology, Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece.

Section of Cell Biology and Biophysics, Department of Biology, National and Kapodistrian University of Athens, 15701 Athens, Greece.

出版信息

Cells. 2023 Jan 21;12(3):388. doi: 10.3390/cells12030388.

Abstract

Genes with similar expression patterns in a set of diverse samples may be considered coexpressed. Human Gene Coexpression Analysis 2.0 (HGCA2.0) is a webtool which studies the global coexpression landscape of human genes. The website is based on the hierarchical clustering of 55,431 genes based on a large-scale coexpression analysis of 3500 GTEx bulk RNA-Seq samples of healthy individuals, which were selected as the best representative samples of each tissue type. HGCA2.0 presents subclades of coexpressed genes to a gene of interest, and performs various built-in gene term enrichment analyses on the coexpressed genes, including gene ontologies, biological pathways, protein families, and diseases, while also being unique in revealing enriched transcription factors driving coexpression. HGCA2.0 has been successful in identifying not only genes with ubiquitous expression patterns, but also tissue-specific genes. Benchmarking showed that HGCA2.0 belongs to the top performing coexpression webtools, as shown by STRING analysis. HGCA2.0 creates working hypotheses for the discovery of gene partners or common biological processes that can be experimentally validated. It offers a simple and intuitive website design and user interface, as well as an API endpoint.

摘要

基因在一组不同的样本中具有相似的表达模式,可被认为是共表达的。人类基因共表达分析 2.0 (HGCA2.0)是一个研究人类基因全局共表达景观的网络工具。该网站基于对 3500 个健康个体的 GTEx 批量 RNA-Seq 样本的大规模共表达分析,对 55431 个基因进行层次聚类,这些样本被选为每种组织类型的最佳代表性样本。HGCA2.0 向感兴趣的基因展示共表达基因的亚群,并对共表达基因执行各种内置的基因术语富集分析,包括基因本体论、生物途径、蛋白质家族和疾病,同时还独特地揭示了驱动共表达的丰富转录因子。HGCA2.0 不仅成功地识别了具有普遍表达模式的基因,还识别了组织特异性基因。基准测试表明,HGCA2.0 属于表现最佳的共表达网络工具之一,如 STRING 分析所示。HGCA2.0 为发现基因伙伴或可通过实验验证的共同生物学过程创造了工作假设。它提供了简单直观的网站设计和用户界面,以及一个 API 端点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c81/9913097/01d619daa9c3/cells-12-00388-g002.jpg

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