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- Publisher Website: 10.1371/journal.pone.0017429
- Scopus: eid_2-s2.0-79952146521
- PMID: 21364759
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Article: NAViGaTing the micronome - using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs
Title | NAViGaTing the micronome - using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs |
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Authors | |
Issue Date | 2011 |
Citation | PLoS ONE, 2011, v. 6, n. 2, article no. e17429 How to Cite? |
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. |
Persistent Identifier | http://hdl.handle.net/10722/292620 |
PubMed Central ID | |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Shirdel, Elize A. | - |
dc.contributor.author | Xie, Wing | - |
dc.contributor.author | Mak, Tak W. | - |
dc.contributor.author | Jurisica, Igor | - |
dc.date.accessioned | 2020-11-17T14:56:52Z | - |
dc.date.available | 2020-11-17T14:56:52Z | - |
dc.date.issued | 2011 | - |
dc.identifier.citation | PLoS ONE, 2011, v. 6, n. 2, article no. e17429 | - |
dc.identifier.uri | http://hdl.handle.net/10722/292620 | - |
dc.description.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. | - |
dc.language | eng | - |
dc.relation.ispartof | PLoS ONE | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.title | NAViGaTing the micronome - using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs | - |
dc.type | Article | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1371/journal.pone.0017429 | - |
dc.identifier.pmid | 21364759 | - |
dc.identifier.pmcid | PMC3045450 | - |
dc.identifier.scopus | eid_2-s2.0-79952146521 | - |
dc.identifier.volume | 6 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | article no. e17429 | - |
dc.identifier.epage | article no. e17429 | - |
dc.identifier.eissn | 1932-6203 | - |
dc.identifier.isi | WOS:000287764100061 | - |
dc.identifier.issnl | 1932-6203 | - |