Xu Suowen
Department of Medicine, Aab Cardiovascular Research Institute, University of Rochester School of Medicine and Dentistry, RochesterNY, United States.
Front Pharmacol. 2017 Aug 25;8:563. doi: 10.3389/fphar.2017.00563. eCollection 2017.
In the post-genomic, big data era, our understanding of vascular diseases has been deepened by multiple state-of-the-art "-omics" approaches, including genomics, epigenomics, transcriptomics, proteomics, lipidomics and metabolomics. Genome-wide transcriptomic profiling, such as gene microarray and RNA-sequencing, emerges as powerful research tools in systems medicine and revolutionizes transcriptomic analysis of the pathological mechanisms and therapeutics of vascular diseases. In this article, I will highlight the workflow of transcriptomic profiling, outline basic bioinformatics analysis, and summarize recent gene profiling studies performed in vascular cells as well as in human and mice diseased samples. Further mining of these public repository datasets will shed new light on our understanding of the cellular basis of vascular diseases and offer novel potential targets for therapeutic intervention.
在后基因组大数据时代，我们对血管疾病的认识通过多种先进的“组学”方法得到了深化，这些方法包括基因组学、表观基因组学、转录组学、蛋白质组学、脂质组学和代谢组学。全基因组转录组分析，如基因芯片和RNA测序，已成为系统医学中强大的研究工具，并彻底改变了对血管疾病病理机制和治疗方法的转录组分析。在本文中，我将重点介绍转录组分析的工作流程，概述基本的生物信息学分析，并总结最近在血管细胞以及人类和小鼠疾病样本中进行的基因分析研究。对这些公共存储库数据集的进一步挖掘将为我们对血管疾病细胞基础的理解提供新的线索，并为治疗干预提供新的潜在靶点。
