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

By M. Carlson, Bin Zhang, Z. Fang, P. Mischel, Steve Horvath, Stanley F. Nelson. BMC Genomics, 2006

363 citations recorded by the source corpus.

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.

Subfields: Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/a05c6c3c1986139dda37c10702e6bd6049be27c9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atomic and Molecular Physics, and Optics

Citations in the corpus

WGCNA: an R package for weighted correlation network analysis (2008, methodology): “WGCNA has been used to analyze gene expression data from brain cancer [10], yeast cell cycle [13], mouse genetics [14-17], primate brain tissue [18-20], diabetes [21], chronic fatigue patients [22] and plants [23].”
The Polygenic and Monogenic Basis of Blood Traits and Diseases (2020, background): “Biological networks are organized hierarchically (Ravasz et al., 2002; Ravasz and Barabási, 2003; Carlson et al., 2006).”
Geometric Interpretation of Gene Coexpression Network Analysis (2008, background): “A detailed biological description of the modules and the importance of intramodular connectivity can be found in previous work [33].”
Is My Network Module Preserved and Reproducible? (2011, background): “Intramodular hub genes often play a central role in the module [5,33–35].”
Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R (2008, methodology): “This variant has been used to identify biologically meaningful gene clusters in microarray data from several species such as yeast (Carlson et al., 2006; Dong and Horvath, 2007) and mouse (Ghazalpour et al., 2006), but has not previously been systematically described nor made publicly available.”
Gene network interconnectedness and the generalized topological overlap measure (2007, background): “Second, the higher-order GTOM dissimilarities d T,[2] and d T,[3] show a high correlation of 0.78.”
Eigengene networks for studying the relationships between co-expression modules (2007, methodology): “This module detection approach has been successfully used in several studies [6,7,11,18,22,30].”
Analysis of global control of Escherichia coli carbohydrate uptake (2007, neutral): “Our module detection approach has led to biologically meaningful modules in several applications [9,10,12,20,28-30] but we make no claim that it is optimal.”
Molecular ecological network analyses (2012, background): “Thus, the resulting network is more or less subjective [19,20,85,87].”
Comparison of co-expression measures: mutual information, correlation, and model based indices (2012, background): “A detailed description of the data set can be found in [64].”