Characterization of statistical features for plant microRNA prediction

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dc.creatorThakur, Vivek
dc.creatorWanchana, Samart
dc.creatorXu, Mercedes
dc.creatorBruskiewich, Richard
dc.creatorQuick, William Paul
dc.creatorMosig, Axel
dc.creatorZhu, Xinguang
dc.date2011-12
dc.date2024-12-19T12:55:38Z
dc.date2024-12-19T12:55:38Z
dc.date.accessioned2026-06-27T04:11:32Z
dc.descriptionSeveral 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.
dc.identifierhttps://hdl.handle.net/10568/165931
dc.identifier.urihttp://hdl.handle.net/123456789/24575
dc.languageen
dc.publisherSpringer
dc.rightsOpen Access
dc.sourceThakur, 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
dc.subjectforecasting
dc.subjectmaize
dc.subjectmathematical models
dc.subjectmonocotyledons
dc.subjectnucleotide sequences
dc.subjectrna
dc.subjectstability
dc.titleCharacterization of statistical features for plant microRNA prediction
dc.typeJournal Article

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Instituto Internacional de Investigación del Arroz (IRRI)