Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks
1 Department of Human Genetics, David Geffen School of Medicine at UCLA, Gonda (Goldschmied) Neuroscience and Genetics Research Center, 695 Charles E. Young Drive South, Los Angeles, CA 90095-7088, USA
2 Rosetta Inpharmatics LLC, 401 Terry Avenue North, Seattle, WA 98109, USA
3 Cancer Prevention Institute 4100 South Kettering Blvd., Dayton, OH 45439, USA
4 Department of Community Health, School of Medicine, Wright State University 136 F.A. White Health Center 3640 Colonel Glenn Highway, Dayton, OH 45435, USA
5 Department of Pathology and Laboratory Medicine, UCLA, 10833 Le Conte Ave. Los Angeles, CA 90095, USA
6 Department of Biostatistics, UCLA, CHS Suite 51-236 650 Charles E. Young Dr. Los Angeles, CA 90095, USA
7 Department of Psychiatry, David Geffen School of Medicine, UCLA, 760 Westwood Plaza Los Angeles, CA 90095, USA
BMC Genomics 2006, 7:40 doi:10.1186/1471-2164-7-40Published: 3 March 2006
Genes and proteins are organized into functional modular networks in which the network context of a gene or protein has implications for cellular function. Highly connected hub proteins, largely responsible for maintaining network connectivity, have been found to be much more likely to be essential for yeast survival.
Here we investigate the properties of weighted gene co-expression networks formed from multiple microarray datasets. The constructed networks approximate scale-free topology, but this is not universal across all datasets. We show strong positive correlations between gene connectivity within the whole network and gene essentiality as well as gene sequence conservation. We demonstrate the preservation of a modular structure of the networks formed, and demonstrate that, within some of these modules, it is possible to observe a strong correlation between connectivity and essentiality or between connectivity and conservation within the modules particularly within modules containing larger numbers of essential genes.
Application of these techniques can allow a finer scale prediction of relative gene importance for a particular process within a group of similarly expressed genes.