title: Using gene networks in EvoDevo analyses.
abstract: An intricate web of regulatory relationships between DNA, RNA, proteins and metabolites regulates how organisms achieve form and function. Genome sequencing combined with computational methods has allowed us to look at diverse readouts and generate a comprehensive framework for how molecules generate morphological phenotypes. RNAseq has evolved and proved useful for identifying links between transcription factor activity and transcript abundance, and for the generation of transcriptomes in non-model species through de novo assembly. Gene coexpression networks, combined with differential correlation analysis, offer the most versatile gene interaction exploratory tools, using gene expression patterns to determine potential associations and modularity of gene interactions and the differential associations between networks. Networks incorporating different types of biological data can help reduce the complexity of the entirety of biological information into discrete and interpretable pieces of data that can be the starting point for hypothesis construction and testing.
authors.affiliation: Department of Plant Biology, University of California at Davis, Davis, CA 95616, USA. Electronic address: nrsinha@ucdavis.edu.
authors.forename: Neelima R
authors.initials: NR
authors.lastname: Sinha
authors.affiliation: Department of Plant Biology, University of California at Davis, Davis, CA 95616, USA.
authors.forename: Steven D
authors.initials: SD
authors.lastname: Rowland
authors.affiliation: RIKEN Center for Sustainable Resource Science, Yokohama, Kanagawa 230-0045, Japan.
authors.forename: Yasunori
authors.initials: Y
authors.lastname: Ichihashi
mesh_terms: D019295:Computational Biology / Q000379:methods*; D053263:Gene Regulatory Networks; D010940:Plant Proteins / Q000235:genetics* / Q000378:metabolism; D010944:Plants / Q000235:genetics* / Q000378:metabolism
chemical_list: D010940:Plant Proteins