The standard workflow in gene expression profile analysis to identify gene function is the clustering by various metrics and techniques, and the following analyses, such as sequence analyses of upstream regions. A further challenging analysis is the inference of a gene regulatory network, and some computational methods have been intensively developed to deduce the gene regulatory network. Here, we describe our web server for inferring a framework of regulatory networks from a large number of gene expression profiles, based on graphical Gaussian modeling (GGM) in combination with hierarchical clustering (http:// eureka.ims.u-tokyo.ac.jp/asian). GGM is based on a simple mathematical structure, which is the calculation of the inverse of the correlation coefficient matrix between variables, and therefore, our server can analyze a wide variety of data within a reasonable computational time. The server allows users to input the expression profiles, and it outputs the dendrogram of genes by several hierarchical clustering techniques, the cluster number estimated by a stopping rule for hierarchical clustering and the network between the clusters by GGM, with the respective graphical presentations. Thus, the ASIAN (Automatic System for Inferring A Network) web server provides an initial basis for inferring regulatory relationships, in that the clustering serves as the first step toward identifying the gene function.
|Original language||English (US)|
|Journal||Nucleic acids research|
|Issue number||SUPPL. 2|
|State||Published - Jul 2005|
Bibliographical noteFunding Information:
Funding to pay the Open Access publication charges for this article was provided by a Grant-in-Aid for Scientific Research on Priority Areas ‘Genome Information Science’ (grant 17017015), from the Ministry of Education, Culture, Sports, Science and Technology of Japan.
ASJC Scopus subject areas