title: NAViGaTing the micronome--using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs.
abstract: BACKGROUND
  MicroRNAs are a class of small RNAs known to regulate gene expression at the transcript level, the protein level, or both. Since microRNA binding is sequence-based but possibly structure-specific, work in this area has resulted in multiple databases storing predicted microRNA:target relationships computed using diverse algorithms. We integrate prediction databases, compare predictions to in vitro data, and use cross-database predictions to model the microRNA:transcript interactome--referred to as the micronome--to study microRNA involvement in well-known signalling pathways as well as associations with disease. We make this data freely available with a flexible user interface as our microRNA Data Integration Portal--mirDIP (http://ophid.utoronto.ca/mirDIP).
  
  
  RESULTS
  mirDIP integrates prediction databases to elucidate accurate microRNA:target relationships. Using NAViGaTOR to produce interaction networks implicating microRNAs in literature-based, KEGG-based and Reactome-based pathways, we find these signalling pathway networks have significantly more microRNA involvement compared to chance (p<0.05), suggesting microRNAs co-target many genes in a given pathway. Further examination of the micronome shows two distinct classes of microRNAs; universe microRNAs, which are involved in many signalling pathways; and intra-pathway microRNAs, which target multiple genes within one signalling pathway. We find universe microRNAs to have more targets (p<0.0001), to be more studied (p<0.0002), and to have higher degree in the KEGG cancer pathway (p<0.0001), compared to intra-pathway microRNAs.
  
  
  CONCLUSIONS
  Our pathway-based analysis of mirDIP data suggests microRNAs are involved in intra-pathway signalling. We identify two distinct classes of microRNAs, suggesting a hierarchical organization of microRNAs co-targeting genes both within and between pathways, and implying differential involvement of universe and intra-pathway microRNAs at the disease level.
authors.affiliation: Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
authors.forename: Elize A
authors.initials: EA
authors.lastname: Shirdel
authors.forename: Wing
authors.initials: W
authors.lastname: Xie
authors.forename: Tak W
authors.initials: TW
authors.lastname: Mak
authors.forename: Igor
authors.initials: I
authors.lastname: Jurisica
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030561:Databases, Nucleic Acid; D005544:Forecasting / Q000379:methods; D015894:Genome, Human; D006801:Humans; D053858:Metabolic Networks and Pathways / Q000235:genetics / Q000502:physiology; D035683:MicroRNAs / Q000235:genetics* / Q000378:metabolism; D008954:Models, Biological; D025941:Protein Interaction Mapping; D017423:Sequence Analysis, RNA / Q000379:methods*; D015398:Signal Transduction / Q000235:genetics*; D012984:Software; D054928:Validation Studies as Topic
chemical_list: D035683:MicroRNAs