%0 Journal Article
%T Gene Regulation, Modulation, and Their Applications in Gene Expression Data Analysis
%A Mario Flores
%A Tzu-Hung Hsiao
%A Yu-Chiao Chiu
%A Eric Y. Chuang
%A Yufei Huang
%A Yidong Chen
%J Advances in Bioinformatics
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/360678
%X Common microarray and next-generation sequencing data analysis concentrate on tumor subtype classification, marker detection, and transcriptional regulation discovery during biological processes by exploring the correlated gene expression patterns and their shared functions. Genetic regulatory network (GRN) based approaches have been employed in many large studies in order to scrutinize for dysregulation and potential treatment controls. In addition to gene regulation and network construction, the concept of the network modulator that has significant systemic impact has been proposed, and detection algorithms have been developed in past years. Here we provide a unified mathematic description of these methods, followed with a brief survey of these modulator identification algorithms. As an early attempt to extend the concept to new RNA regulation mechanism, competitive endogenous RNA (ceRNA), into a modulator framework, we provide two applications to illustrate the network construction, modulation effect, and the preliminary finding from these networks. Those methods we surveyed and developed are used to dissect the regulated network under different modulators. Not limit to these, the concept of ¡°modulation¡± can adapt to various biological mechanisms to discover the novel gene regulation mechanisms. 1. Introduction With the development of microarray [1] and lately the next generation sequencing techniques [2], transcriptional profiling of biological samples, such as tumor samples [3¨C5] and samples from other model organisms, have been carried out in order to study sample subtypes at molecular level or transcriptional regulation during the biological processes [6¨C8]. While common data analysis methods employ hierarchical clustering algorithms or pattern classification to explore correlated genes and their functions, the genetic regulatory network (GRN) approaches were employed to scrutinize for dysregulation between different tumor groups or biological processes (see reviews [9¨C12]). To construct the network, most of research is focused on methods based on gene expression data derived from high-throughput technologies by using metrics such as Pearson or Spearman correlation [13], mutual information [14], co-determination method [15, 16], Bayesian methods [17, 18], and probabilistic Boolean networks [19]. Recently, new transcriptional regulation via competitive endogenous RNA (ceRNAs) has been proposed [20, 21], introducing additional dimension in modeling gene regulation. This type of regulation requires the knowledge of microRNA (miRNA) binding targets
%U http://www.hindawi.com/journals/abi/2013/360678/