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Employing machine learning for reliable miRNA target identification in plants

IR@CSIR-IHBT

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Title Employing machine learning for reliable miRNA
target identification in plants
 
Creator Jha, Gopaljee
Shankar, R
 
Subject Plant sciences
 
Description Background: miRNAs are ~21 nucleotide long small noncoding RNA molecules, formed endogenously in most of
the eukaryotes, which mainly control their target genes post transcriptionally by interacting and silencing them.
While a lot of tools has been developed for animal miRNA target system, plant miRNA target identification system
has witnessed limited development. Most of them have been centered around exact complementarity match. Very
few of them considered other factors like multiple target sites and role of flanking regions.
Result: In the present work, a Support Vector Regression (SVR) approach has been implemented for plant miRNA
target identification, utilizing position specific dinucleotide density variation information around the target sites, to
yield highly reliable result. It has been named as p-TAREF (plant-Target Refiner). Performance comparison for p-
TAREF was done with other prediction tools for plants with utmost rigor and where p-TAREF was found better
performing in several aspects. Further, p-TAREF was run over the experimentally validated miRNA targets from
species like Arabidopsis, Medicago, Rice and Tomato, and detected them accurately, suggesting gross usability of p-
TAREF for plant species. Using p-TAREF, target identification was done for the complete Rice transcriptome,
supported by expression and degradome based data. miR156 was found as an important component of the Rice
regulatory system, where control of genes associated with growth and transcription looked predominant. The
entire methodology has been implemented in a multi-threaded parallel architecture in Java, to enable fast
processing for web-server version as well as standalone version. This also makes it to run even on a simple desktop
computer in concurrent mode. It also provides a facility to gather experimental support for predictions made,
through on the spot expression data analysis, in its web-server version.
Conclusion: A machine learning multivariate feature tool has been implemented in parallel and locally installable
form, for plant miRNA target identification. The performance was assessed and compared through comprehensive
testing and benchmarking, suggesting a reliable performance and gross usability for transcriptome wide plant
miRNA target identification
 
Date 2011
 
Type Article
PeerReviewed
 
Format application/pdf
 
Identifier http://ihbt.csircentral.net/1105/1/42_employing.pdf
Jha, Gopaljee and Shankar, R (2011) Employing machine learning for reliable miRNA target identification in plants. BMC Genomics, 12. p. 636.
 
Relation http://ihbt.csircentral.net/1105/