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Meta-analysis of gene expression signatures reveals hidden links among diverse biological processes in Arabidopsis.
Lai, Liming; Ge, Steven X.
Affiliation
  • Lai L; Department of Mathematics and Statistics, South Dakota State University, Brookings, South Dakota, United States of America.
  • Ge SX; Department of Mathematics and Statistics, South Dakota State University, Brookings, South Dakota, United States of America.
PLoS One ; 9(11): e108567, 2014.
Article in En | MEDLINE | ID: mdl-25398003
The model plant Arabidopsis has been well-studied using high-throughput genomics technologies, which usually generate lists of differentially expressed genes under various conditions. Our group recently collected 1065 gene lists from 397 gene expression studies as a knowledgebase for pathway analysis. Here we systematically analyzed these gene lists by computing overlaps in all-vs.-all comparisons. We identified 16,261 statistically significant overlaps, represented by an undirected network in which nodes correspond to gene lists and edges indicate significant overlaps. The network highlights the correlation across the gene expression signatures of the diverse biological processes. We also partitioned the main network into 20 sub-networks, representing groups of highly similar expression signatures. These are common sets of genes that were co-regulated under different treatments or conditions and are often related to specific biological themes. Overall, our result suggests that diverse gene expression signatures are highly interconnected in a modular fashion.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Biological Phenomena / Arabidopsis / Gene Expression Profiling Type of study: Systematic_reviews Language: En Journal: PLoS One Journal subject: CIENCIA / MEDICINA Year: 2014 Document type: Article Affiliation country: United States Country of publication: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Biological Phenomena / Arabidopsis / Gene Expression Profiling Type of study: Systematic_reviews Language: En Journal: PLoS One Journal subject: CIENCIA / MEDICINA Year: 2014 Document type: Article Affiliation country: United States Country of publication: United States