Title
Intragenomic Matching Reveals A Huge Potential For Mirna-Mediated Regulation In Plants
Abstract
microRNAs ( miRNAs) are important post-transcriptional regulators, but the extent of this regulation is uncertain, both with regard to the number of miRNA genes and their targets. Using an algorithm based on intragenomic matching of potential miRNAs and their targets coupled with support vector machine classification of miRNA precursors, we explore the potential for regulation by miRNAs in three plant genomes: Arabidopsis thaliana, Populus trichocarpa, and Oryza sativa. We find that the intragenomic matching in conjunction with a supervised learning approach contains enough information to allow reliable computational prediction of miRNA candidates without requiring conservation across species. Using this method, we identify similar to 1,200, similar to 2,500, and similar to 2,100 miRNA candidate genes capable of extensive base-pairing to potential target mRNAs in A. thaliana, P. trichocarpa, and O. sativa, respectively. This is more than five times the number of currently annotated miRNAs in the plants. Many of these candidates are derived from repeat regions, yet they seem to contain the features necessary for correct processing by the miRNA machinery. Conservation analysis indicates that only a few of the candidates are conserved between the species. We conclude that there is a large potential for miRNA-mediated regulatory interactions encoded in the genomes of the investigated plants. We hypothesize that some of these interactions may be realized under special environmental conditions, while others can readily be recruited when organisms diverge and adapt to new niches.
Year
DOI
Venue
2007
10.1371/journal.pcbi.0030238
PLOS COMPUTATIONAL BIOLOGY
Keywords
Field
DocType
supervised learning,post transcriptional regulation,micrornas,candidate gene,support vector machine,phenotype,base pair,microrna
Genome,Gene,Genome project,Candidate gene,Phenotype,Biology,microRNA,Arabidopsis thaliana,Genomic library,Bioinformatics,Genetics
Journal
Volume
Issue
ISSN
3
11
1553-7358
Citations 
PageRank 
References 
5
0.61
5
Authors
5
Name
Order
Citations
PageRank
Morten Lindow1171.36
Anders Jacobsen2171.36
Sanne Nygaard350.61
Yuan Mang450.61
Anders Krogh52181310.64