Characterization of statistical features for plant microRNA prediction
Several tools are available to identify miRNAs from deep-sequencing data, however, only a few of them, like miRDeep, can identify novel miRNAs and are also available as a standalone application. Given the difference between plant and animal miRNAs, particularly in terms of distribution of hairpin le...
| Autores principales: | , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Springer
2011
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/165931 |
| _version_ | 1855527693846577152 |
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| author | Thakur, Vivek Wanchana, Samart Xu, Mercedes Bruskiewich, Richard Quick, William Paul Mosig, Axel Zhu, Xinguang |
| author_browse | Bruskiewich, Richard Mosig, Axel Quick, William Paul Thakur, Vivek Wanchana, Samart Xu, Mercedes Zhu, Xinguang |
| author_facet | Thakur, Vivek Wanchana, Samart Xu, Mercedes Bruskiewich, Richard Quick, William Paul Mosig, Axel Zhu, Xinguang |
| author_sort | Thakur, Vivek |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Several tools are available to identify miRNAs from deep-sequencing data, however, only a few of them, like miRDeep, can identify novel miRNAs and are also available as a standalone application. Given the difference between plant and animal miRNAs, particularly in terms of distribution of hairpin length and the nature of complementarity with its duplex partner (or miRNA star), the underlying (statistical) features of miRDeep and other tools, using similar features, are likely to get affected. The potential effects on features, such as minimum free energy, stability of secondary structures, excision length, etc., were examined, and the parameters of those displaying sizable changes were estimated for plant specific miRNAs. We found most of these features acquired a new set of values or distributions for plant specific miRNAs. While the length of conserved positions (nucleus) in mature miRNAs were relatively longer in plants, the difference in distribution of minimum free energy, between real and background hairpins, was marginal. However, the choice of source (species) of background sequences was found to affect both the minimum free energy and miRNA hairpin stability. The new parameters were tested on an Illumina dataset from maize seedlings, and the results were compared with those obtained using default parameters. The newly parameterized model was found to have much improved specificity and sensitivity over its default counterpart. In summary, the present study reports behavior of few general and tool-specific statistical features for improving the prediction accuracy of plant miRNAs from deep-sequencing data. |
| format | Journal Article |
| id | CGSpace165931 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2011 |
| publishDateRange | 2011 |
| publishDateSort | 2011 |
| publisher | Springer |
| publisherStr | Springer |
| record_format | dspace |
| spelling | CGSpace1659312025-05-14T10:24:12Z Characterization of statistical features for plant microRNA prediction Thakur, Vivek Wanchana, Samart Xu, Mercedes Bruskiewich, Richard Quick, William Paul Mosig, Axel Zhu, Xinguang forecasting maize mathematical models monocotyledons nucleotide sequences rna stability Several tools are available to identify miRNAs from deep-sequencing data, however, only a few of them, like miRDeep, can identify novel miRNAs and are also available as a standalone application. Given the difference between plant and animal miRNAs, particularly in terms of distribution of hairpin length and the nature of complementarity with its duplex partner (or miRNA star), the underlying (statistical) features of miRDeep and other tools, using similar features, are likely to get affected. The potential effects on features, such as minimum free energy, stability of secondary structures, excision length, etc., were examined, and the parameters of those displaying sizable changes were estimated for plant specific miRNAs. We found most of these features acquired a new set of values or distributions for plant specific miRNAs. While the length of conserved positions (nucleus) in mature miRNAs were relatively longer in plants, the difference in distribution of minimum free energy, between real and background hairpins, was marginal. However, the choice of source (species) of background sequences was found to affect both the minimum free energy and miRNA hairpin stability. The new parameters were tested on an Illumina dataset from maize seedlings, and the results were compared with those obtained using default parameters. The newly parameterized model was found to have much improved specificity and sensitivity over its default counterpart. In summary, the present study reports behavior of few general and tool-specific statistical features for improving the prediction accuracy of plant miRNAs from deep-sequencing data. 2011-12 2024-12-19T12:55:38Z 2024-12-19T12:55:38Z Journal Article https://hdl.handle.net/10568/165931 en Open Access Springer Thakur, Vivek; Wanchana, Samart; Xu, Mercedes; Bruskiewich, Richard; Quick, William Paul; Mosig, Axel and Zhu, Xin-Guang. 2011. Characterization of statistical features for plant microRNA prediction. BMC Genomics, Volume 12, no. 1 |
| spellingShingle | forecasting maize mathematical models monocotyledons nucleotide sequences rna stability Thakur, Vivek Wanchana, Samart Xu, Mercedes Bruskiewich, Richard Quick, William Paul Mosig, Axel Zhu, Xinguang Characterization of statistical features for plant microRNA prediction |
| title | Characterization of statistical features for plant microRNA prediction |
| title_full | Characterization of statistical features for plant microRNA prediction |
| title_fullStr | Characterization of statistical features for plant microRNA prediction |
| title_full_unstemmed | Characterization of statistical features for plant microRNA prediction |
| title_short | Characterization of statistical features for plant microRNA prediction |
| title_sort | characterization of statistical features for plant microrna prediction |
| topic | forecasting maize mathematical models monocotyledons nucleotide sequences rna stability |
| url | https://hdl.handle.net/10568/165931 |
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