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Employing machine learning for reliable miRNA target identification in plants.

Jha A, Shankar R - BMC Genomics (2011)

Bottom Line: 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.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 performance was assessed and compared through comprehensive testing and benchmarking, suggesting a reliable performance and gross usability for transcriptome wide plant miRNA target identification.

View Article: PubMed Central - HTML - PubMed

Affiliation: Studio of Computational Biology & Bioinformatics, Biotechnology Division, Institute of Himalayan Bioresource Technology, Council of Scientific & Industrial Research, Palampur 176061 (HP), India.

ABSTRACT

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.

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Related in: MedlinePlus

Snapshot of standalone GUI version of p-TAREF. Like its web-server counterpart, the standalone GUI version too provides concurrency and most of the features, enabling quick standalone scanning of batch and large amount of sequence data. It also shows a progress bar to intimate about the status of analysis.
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Figure 3: Snapshot of standalone GUI version of p-TAREF. Like its web-server counterpart, the standalone GUI version too provides concurrency and most of the features, enabling quick standalone scanning of batch and large amount of sequence data. It also shows a progress bar to intimate about the status of analysis.

Mentions: The Standalone GUI version of p-TAREF was developed considering the realistic approach to scan large amount of transcriptome data for miRNA targeting. Performing such task on web-server is a time consuming approach which takes lots of time in loading the data itself and fetching it back, while both of the processes are connection and INTERNET availability dependent. In that way, Standalone GUI version could be very helpful in running p-TAREF locally and in user friendly manner. The entire interface has been developed using QT C++ Library which is also available for download from the server page's download section. The Standalone GUI is easily deployable even on a simple laptop or desktop machine as well as on high-end servers. In case, if the required dependencies are not pre-installed on user's machine, p-TAREF installation system verifies this and automatically installs all the per-requisites itself. The standalone GUI version provides an option to load sequence query file in a batch mode, adjust the mismatch level for experimentally validated interactions similarity search, options to adjust the binding free energy cut-off, options to choose for kernel dependent plant models as well as adjust the number of processors to be used to implement concurrency. A progress bar appears to display the running job status and amount of sequences scanned. Figure 3 shows the running state of Standalone GUI version of p-TAREF.


Employing machine learning for reliable miRNA target identification in plants.

Jha A, Shankar R - BMC Genomics (2011)

Snapshot of standalone GUI version of p-TAREF. Like its web-server counterpart, the standalone GUI version too provides concurrency and most of the features, enabling quick standalone scanning of batch and large amount of sequence data. It also shows a progress bar to intimate about the status of analysis.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3293931&req=5

Figure 3: Snapshot of standalone GUI version of p-TAREF. Like its web-server counterpart, the standalone GUI version too provides concurrency and most of the features, enabling quick standalone scanning of batch and large amount of sequence data. It also shows a progress bar to intimate about the status of analysis.
Mentions: The Standalone GUI version of p-TAREF was developed considering the realistic approach to scan large amount of transcriptome data for miRNA targeting. Performing such task on web-server is a time consuming approach which takes lots of time in loading the data itself and fetching it back, while both of the processes are connection and INTERNET availability dependent. In that way, Standalone GUI version could be very helpful in running p-TAREF locally and in user friendly manner. The entire interface has been developed using QT C++ Library which is also available for download from the server page's download section. The Standalone GUI is easily deployable even on a simple laptop or desktop machine as well as on high-end servers. In case, if the required dependencies are not pre-installed on user's machine, p-TAREF installation system verifies this and automatically installs all the per-requisites itself. The standalone GUI version provides an option to load sequence query file in a batch mode, adjust the mismatch level for experimentally validated interactions similarity search, options to adjust the binding free energy cut-off, options to choose for kernel dependent plant models as well as adjust the number of processors to be used to implement concurrency. A progress bar appears to display the running job status and amount of sequences scanned. Figure 3 shows the running state of Standalone GUI version of p-TAREF.

Bottom Line: 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.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 performance was assessed and compared through comprehensive testing and benchmarking, suggesting a reliable performance and gross usability for transcriptome wide plant miRNA target identification.

View Article: PubMed Central - HTML - PubMed

Affiliation: Studio of Computational Biology & Bioinformatics, Biotechnology Division, Institute of Himalayan Bioresource Technology, Council of Scientific & Industrial Research, Palampur 176061 (HP), India.

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus