Domain-oriented functional analysis based on expression profiling

Wei Ding , Luquan Wang , Ping Qiu , Mitchel Kostich , Jonathan Greene and Marco Hernandez

Bioinformatics Group, Discovery Technology Department at Schering-Plough Research Institute, 2015 Galloping Hill Road, Kenilworth, New Jersey 07033, USA

author email corresponding author email

BMC Genomics 2002, 3:32doi:10.1186/1471-2164-3-32


Published:	31 October 2002

Abstract

Background

Co-regulation of genes may imply involvement in similar biological processes or related function. Many clusters of co-regulated genes have been identified using microarray experiments. In this study, we examined co-regulated gene families using large-scale cDNA microarray experiments on the human transcriptome.

Results

We present a simple model, which, for each probe pair, distills expression changes into binary digits and summarizes the expression of multiple members of a gene family as the Family Regulation Ratio. The set of Family Regulation Ratios for each protein family across multiple experiments is called a Family Regulation Profile. We analyzed these Family Regulation Profiles using Pearson Correlation Coefficients and derived a network diagram portraying relationships between the Family Regulation Profiles of gene families that are well represented on the microarrays. Our strategy was cross-validated with two randomly chosen data subsets and was proven to be a reliable approach.

Conclusion

This work will help us to understand and identify the functional relationships between gene families and the regulatory pathways in which each family is involved. Concepts presented here may be useful for objective clustering of protein functions and deriving a comprehensive protein interaction map. Functional genomic approaches such as this may also be applicable to the elucidation of complex genetic regulatory networks.