Using genome-wide expression profiling to define gene networks relevant to the study of complex traits: from RNA integrity to network topology.
Using genome-wide expression profiling to define gene networks relevant to the study of complex traits: from RNA integrity to network topology.
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DOI：
10.1016/b978-0-12-398323-7.00005-7
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发表时间：
2012
影响因子：
--
通讯作者：
Miles, M. F.
中科院分区：
文献类型：
--
作者：
O'Brien, M. A.;Costin, B. N.;Miles, M. F.
后基因组研究的基因的功能和它们在疾病中的作用，现在已经成为一个领域的密集研究，因为努力确定基因组的原始序列材料已经基本完成。全基因组方法的使用，如微阵列表达谱分析，以及最近的转录本丰度的RNA序列分析，使人们对基因组的运作有了前所未有的了解。然而，这种高通量数据的准确推导及其在生物功能方面的分析对于真正利用后基因组革命至关重要。本章将描述一种方法，重点是使用基因网络来组织和解释基因组表达数据。这些网络来源于对大型基因组数据集的统计分析和多种生物信息学数据资源的应用，可能允许识别与人类疾病相关的网络的关键控制元件，从而可能导致新的治疗方法的衍生。然而，正如本章所讨论的，如果没有对影响基因组表达数据推导的技术和统计因素的透彻理解，就无法利用这种网络。因此，虽然流行语可能是“这是网络......愚蠢的”，但对从RNA分离到基因组分析技术，多变量统计和生物信息学的因素的理解对于定义复杂生物学研究的完全有用的基因网络都是至关重要的。
Postgenomic studies of the function of genes and their role in disease have now become an area of intense study since efforts to define the raw sequence material of the genome have largely been completed. The use of whole-genome approaches such as microarray expression profiling and, more recently, RNA-sequence analysis of transcript abundance has allowed an unprecedented look at the workings of the genome. However, the accurate derivation of such high-throughput data and their analysis in terms of biological function has been critical to truly leveraging the postgenomic revolution. This chapter will describe an approach that focuses on the use of gene networks to both organize and interpret genomic expression data. Such networks, derived from statistical analysis of large genomic datasets and the application of multiple bioinformatics data resources, poten-tially allow the identification of key control elements for networks associated with human disease, and thus may lead to derivation of novel therapeutic approaches. However, as discussed in this chapter, the leveraging of such networks cannot occur without a thorough understanding of the technical and statistical factors influencing the derivation of genomic expression data. Thus, while the catch phrase may be “it's the network … stupid,” the understanding of factors extending from RNA isolation to genomic profiling technique, multivariate statistics, and bioinformatics are all critical to defining fully useful gene networks for study of complex biology.