miR-BAG: bagging based identification of microRNA precursors.
Non-coding elements such as miRNAs play key regulatory roles in living systems. These ultra-short, ∼21 bp long, RNA molecules are derived from their hairpin precursors and usually participate in negative gene regulation by binding the target mRNAs. Discovering miRNA candidate regions across the geno...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2012-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3458082?pdf=render |
| _version_ | 1818294285576962048 |
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| author | Ashwani Jha Rohit Chauhan Mrigaya Mehra Heikham Russiachand Singh Ravi Shankar |
| author_facet | Ashwani Jha Rohit Chauhan Mrigaya Mehra Heikham Russiachand Singh Ravi Shankar |
| author_sort | Ashwani Jha |
| collection | DOAJ |
| description | Non-coding elements such as miRNAs play key regulatory roles in living systems. These ultra-short, ∼21 bp long, RNA molecules are derived from their hairpin precursors and usually participate in negative gene regulation by binding the target mRNAs. Discovering miRNA candidate regions across the genome has been a challenging problem. Most of the existing tools work reliably only for limited datasets. Here, we have presented a novel reliable approach, miR-BAG, developed to identify miRNA candidate regions in genomes by scanning sequences as well as by using next generation sequencing (NGS) data. miR-BAG utilizes a bootstrap aggregation based machine learning approach, successfully creating an ensemble of complementary learners to attain high accuracy while balancing sensitivity and specificity. miR-BAG was developed for wide range of species and tested extensively for performance over a wide range of experimentally validated data. Consideration of position-specific variation of triplet structural profiles and mature miRNA anchored structural profiles had a positive impact on performance. miR-BAG's performance was found consistent and the accuracy level was observed to be >90% for most of the species considered in the present study. In a detailed comparative analysis, miR-BAG performed better than six existing tools. Using miR-BAG NGS module, we identified a total of 22 novel miRNA candidate regions in cow genome in addition to a total of 42 cow specific miRNA regions. In practice, discovery of miRNA regions in a genome demands high-throughput data analysis, requiring large amount of processing. Considering this, miR-BAG has been developed in multi-threaded parallel architecture as a web server as well as a user friendly GUI standalone version. |
| first_indexed | 2024-12-13T03:29:19Z |
| format | Article |
| id | doaj.art-f861e17e79f94969938205d15a69a8b7 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-13T03:29:19Z |
| publishDate | 2012-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-f861e17e79f94969938205d15a69a8b72022-12-22T00:01:12ZengPublic Library of Science (PLoS)PLoS ONE1932-62032012-01-0179e4578210.1371/journal.pone.0045782miR-BAG: bagging based identification of microRNA precursors.Ashwani JhaRohit ChauhanMrigaya MehraHeikham Russiachand SinghRavi ShankarNon-coding elements such as miRNAs play key regulatory roles in living systems. These ultra-short, ∼21 bp long, RNA molecules are derived from their hairpin precursors and usually participate in negative gene regulation by binding the target mRNAs. Discovering miRNA candidate regions across the genome has been a challenging problem. Most of the existing tools work reliably only for limited datasets. Here, we have presented a novel reliable approach, miR-BAG, developed to identify miRNA candidate regions in genomes by scanning sequences as well as by using next generation sequencing (NGS) data. miR-BAG utilizes a bootstrap aggregation based machine learning approach, successfully creating an ensemble of complementary learners to attain high accuracy while balancing sensitivity and specificity. miR-BAG was developed for wide range of species and tested extensively for performance over a wide range of experimentally validated data. Consideration of position-specific variation of triplet structural profiles and mature miRNA anchored structural profiles had a positive impact on performance. miR-BAG's performance was found consistent and the accuracy level was observed to be >90% for most of the species considered in the present study. In a detailed comparative analysis, miR-BAG performed better than six existing tools. Using miR-BAG NGS module, we identified a total of 22 novel miRNA candidate regions in cow genome in addition to a total of 42 cow specific miRNA regions. In practice, discovery of miRNA regions in a genome demands high-throughput data analysis, requiring large amount of processing. Considering this, miR-BAG has been developed in multi-threaded parallel architecture as a web server as well as a user friendly GUI standalone version.http://europepmc.org/articles/PMC3458082?pdf=render |
| spellingShingle | Ashwani Jha Rohit Chauhan Mrigaya Mehra Heikham Russiachand Singh Ravi Shankar miR-BAG: bagging based identification of microRNA precursors. PLoS ONE |
| title | miR-BAG: bagging based identification of microRNA precursors. |
| title_full | miR-BAG: bagging based identification of microRNA precursors. |
| title_fullStr | miR-BAG: bagging based identification of microRNA precursors. |
| title_full_unstemmed | miR-BAG: bagging based identification of microRNA precursors. |
| title_short | miR-BAG: bagging based identification of microRNA precursors. |
| title_sort | mir bag bagging based identification of microrna precursors |
| url | http://europepmc.org/articles/PMC3458082?pdf=render |
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