5. Motivation Information about regulation in plants is limited KEGG – two maps with 232 and 48 genes related to signalling AtRegNet – currently only covers 69 transcription factors in Arabidopsis, however data fro 9375 regulated genes Other types of data are more abundant Functional annotation Protein-protein interactions Gene expression Use the latter to compensate for the lack of the former
7. Inference methods Analysis of microarray data Meta-coexpression networks from NASC, ArrayExpress and GEO data Databases: ATTED-II, CoexpressDB Inter species comparison Ortholog detection methods: OrthoMCL, Inparanoid Databases: resources supporting OrthoXML format Prediction of interactions “Interolog” and domain-domain approaches Databases: AtPID, TAIR predicted interactome Prediction of functional role Experimentally-determined interaction Species A Orthology Species B Inferred interaction
8. The datasets for these application cases Functional annotation – Gene Ontology GOA EBI TAIR UniProtKB Interaction Experimental – BioGrid, IntAct, TAIR Predicted – interolog approach Expression data – gene coexpression networks Targeted subsets from NASC, ArrayExpress and GEO data
9. Example 1: NAR2.1-knockout microarray NAR2.1 is required to target the high-affinity nitrate transporter NRT2.1 to plasma membrane NRT2.1 is required to take up nitrate at low internal concentrations Possible involvement of NAR2.1 in nitrate sensing Another nitrate transporter (NRT1.1) have now been demonstrated to also function as a sensor Image source: Miller et. al. (2007)
10. From clusters to regulatory relationships Meta-coexpression network ~140 nitrogen-relevant arrays Gene list – nitrogen uptake mutant, grown under low nitrogen Mutant versus wild-type
11. From clusters to regulatory relationships Localisation: chloroplast Component of ribosome Regulation of transcription Markov clustering Functions at 50% coverage
