Affordable Access

Access to the full text

Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks

Authors
  • Carlson, Marc RJ1
  • Zhang, Bin2
  • Fang, Zixing3, 4
  • Mischel, Paul S5
  • Horvath, Steve1, 6
  • Nelson, Stanley F1, 7
  • 1 David Geffen School of Medicine at UCLA, Gonda (Goldschmied) Neuroscience and Genetics Research Center, Department of Human Genetics, 695 Charles E. Young Drive South, Los Angeles, CA, 90095-7088, USA , Los Angeles
  • 2 Rosetta Inpharmatics LLC, 401 Terry Avenue North, Seattle, WA, 98109, USA , Seattle
  • 3 Cancer Prevention Institute, 4100 South Kettering Blvd., Dayton, OH, 45439, USA , Dayton
  • 4 Wright State University, Department of Community Health, School of Medicine, 136 F.A. White Health Center 3640 Colonel Glenn Highway, Dayton, OH, 45435, USA , Dayton
  • 5 Department of Pathology and Laboratory Medicine, UCLA, 10833 Le Conte Ave., Los Angeles, CA, 90095, USA , Los Angeles
  • 6 Department of Biostatistics, UCLA, CHS Suite 51-236 650 Charles E. Young Dr., Los Angeles, CA, 90095, USA , Los Angeles
  • 7 David Geffen School of Medicine, UCLA, Department of Psychiatry, 760 Westwood Plaza, Los Angeles, CA, 90095, USA , Los Angeles
Type
Published Article
Journal
BMC Genomics
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Mar 03, 2006
Volume
7
Issue
1
Identifiers
DOI: 10.1186/1471-2164-7-40
Source
Springer Nature
Keywords
License
Yellow

Abstract

BackgroundGenes 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.ResultsHere 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.ConclusionApplication of these techniques can allow a finer scale prediction of relative gene importance for a particular process within a group of similarly expressed genes.

Report this publication

Statistics

Seen <100 times