Reverse engineering transcriptional gene networks.
The aim of this chapter is a step-by-step guide on how to infer gene networks from gene expression profiles. The definition of a gene network is given in Subheading 1, where the different types of networks are discussed. The chapter then guides the readers through a data-gathering process in order to build a compendium of gene expression profiles from a public repository. Gene expression profiles are then discretized and a statistical relationship between genes, called mutual information (MI), is computed. Gene pairs with insignificant MI scores are then discarded by applying one of the described pruning steps. The retained relationships are then used to build up a Boolean adjacency matrix used as input for a clustering algorithm to divide the network into modules (or communities). The gene network can then be used as a hypothesis generator for discovering gene function and analyzing gene signatures. Some case studies are presented, and an online web-tool called Netview is described.
Faculty of Engineering, University of Naples "Federico II", Naples, Italy.
Vincenzo
V
Belcastro
Diego
D
di Bernardo
D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D030541:Databases, Genetic; D063990:Gene Ontology; D053263:Gene Regulatory Networks; D005910:Glioma / Q000235:genetics; D006801:Humans; D008957:Models, Genetic; D058977:Molecular Sequence Annotation; D009136:Muscular Dystrophies / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis; D059386:Reverse Genetics; D057386:Search Engine; D012984:Software; D059467:Transcriptome