Analyzing Virus Gene Expression Data to Understand Regulatory Interactions

Funding and Staffing Details

This project is funded by a 3-year BBSRC/EPSRC grant under the Bioinformatics Initiative. The project is a collaboration between Xiaohui Liu of the Intelligent Data Analysis Group in the Department of Information Systems and Computing at Brunel University, Nigel Martin of the Database and Web Technologies Group in the Department of Computer Science and Information Systems at Birkbeck, Paul Kellam of the Windeyer Institute of Medical Sciences at UCL, and Christine Orengo of the Biomolecular Structure & Modelling Group in the Department of Biochemistry and Molecular Biology at UCL.

Project Aims

The project seeks to understand how to determine the genetic network of molecular interactions by applying multivariate time series (MTS) methods to the modelling of gene expression data, particularly in the virology domain. The Viral Genomics and Bioinformatics Group at UCL have already successfully produced an array of all known and putative open reading frames of human herpesvirus 8, and in collaboration with the Database Technology and Bioinformatics Groups at Birkbeck College are developing a database of viral protein families (VIDA). This project will extend VIDA with an expression array component, examine the data by applying clustering algorithms and related data pre-processing techinques, and construct models for understanding gene regulation and interaction. Two multivariate time series methods will be used: the Vector Auto-Regressive Process and Dynamic Bayesian Networks. Finally, relevant protein structures, functions, and transcriptional control mechanisms will be used to validate the models.

Project Publications

Comparing, Contrasting and Combining Clusters in Viral Gene Expression Data, P Kellam, X Liu, N.J.Martin, C.Orengo, S.Swift, A.Tucker, Proc. 6th Workshop on Intelligent Data Analysis in Medicine and Pharmacology, 56-62, (2001).

A Framework for Modelling Virus Gene Expression Data, P Kellam, X Liu, N.J.Martin, C.Orengo, S.Swift, A.Tucker, Intelligent Data Analysis, 6(3), 218-227, (2002).

Consensus Clustering and Functional Interpretation of Gene Expression Data, S Swift, A Tucker, V Vinciotti, N Martin, C Orengo, X Liu, P Kellam, Genome Biology, 5:R94 (2004).