NAViGaTing the micronome--using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs.
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 co...
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doaj-5c89970f61114dca90e3584b2d6afed02020-11-25T01:45:07ZengPublic Library of Science (PLoS)PLoS ONE1932-62032011-02-0162e1742910.1371/journal.pone.0017429NAViGaTing the micronome--using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs.Elize A ShirdelWing XieTak W MakIgor JurisicaMicroRNAs 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).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.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.http://europepmc.org/articles/PMC3045450?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Elize A Shirdel Wing Xie Tak W Mak Igor Jurisica |
spellingShingle |
Elize A Shirdel Wing Xie Tak W Mak Igor Jurisica NAViGaTing the micronome--using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs. PLoS ONE |
author_facet |
Elize A Shirdel Wing Xie Tak W Mak Igor Jurisica |
author_sort |
Elize A Shirdel |
title |
NAViGaTing the micronome--using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs. |
title_short |
NAViGaTing the micronome--using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs. |
title_full |
NAViGaTing the micronome--using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs. |
title_fullStr |
NAViGaTing the micronome--using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs. |
title_full_unstemmed |
NAViGaTing the micronome--using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs. |
title_sort |
navigating the micronome--using multiple microrna prediction databases to identify signalling pathway-associated micrornas. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2011-02-01 |
description |
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).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.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. |
url |
http://europepmc.org/articles/PMC3045450?pdf=render |
work_keys_str_mv |
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