非生物信息学专业人员对已存储基因表达数据集的简要分析：如何利用基因表达综合数据库（GEO）进行纤维化研究。

Simple Analysis of Deposited Gene Expression Datasets for the Non-Bioinformatician: How to Use GEO for Fibrosis Research.

作者信息

Guo Yang, Townsend Richard, Tsoi Lam C

机构信息

Department of Chemistry, University of Michigan, Ann Arbor, MI, USA.

Biotechnology, Henry Ford College, Dearborn, MI, USA.

出版信息

Methods Mol Biol. 2017;1627:511-525. doi: 10.1007/978-1-4939-7113-8_31.

DOI:10.1007/978-1-4939-7113-8_31

PMID:28836221

Abstract

In the past decade, high-throughput techniques have facilitated the "-omics" research. Transcriptomic study, for instance, has advanced our understanding on the expression landscape of different human diseases and cellular mechanisms. The National Center for Biotechnology Center (NCBI) initialized Genetic Expression Omnibus (GEO) to promote the sharing of transcriptomic data to facilitate biomedical research. In this chapter, we will illustrate how to use GEO to search and analyze the public available transcriptomic data, and we will provide easy to follow protocol for researchers to data mine the powerful resources in GEO to retrieve relevant information that can be valuable for fibrosis research.

摘要

在过去十年中，高通量技术推动了“组学”研究。例如，转录组学研究增进了我们对不同人类疾病的表达图谱和细胞机制的理解。美国国立生物技术信息中心（NCBI）启动了基因表达综合数据库（GEO），以促进转录组学数据的共享，推动生物医学研究。在本章中，我们将说明如何使用GEO搜索和分析公开可用的转录组学数据，并为研究人员提供易于遵循的方案，以便在GEO中挖掘强大的资源，检索对纤维化研究有价值的相关信息。