Characterization of statistical features for plant microRNA prediction.
Characterization of statistical features for plant microRNA prediction.
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DOI：
10.1186/1471-2164-12-108
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发表时间：
2011-02-16
期刊：
影响因子：
4.4
通讯作者：
Zhu XG
中科院分区：
文献类型：
--
作者：
Thakur V;Wanchana S;Xu M;Bruskiewich R;Quick WP;Mosig A;Zhu XG
有几种工具可用于从深度测序数据中识别miRNAs，然而，只有少数工具，如miRDeep，可以识别新的miRNAs，并且也可以作为独立应用程序使用。鉴于植物和动物miRNAs之间的差异，特别是在发夹长度分布和与其双链伙伴(或miRNAstar)的互补性方面，miRDeep和使用类似特征的其他工具的潜在(统计)特征可能会受到影响。考察了其对特征的潜在影响，如最小自由能、二级结构的稳定性、切除长度等，并估计了植物特定miRNAs中那些有较大变化的参数。我们发现，这些特征中的大多数都获得了一组新的植物特有miRNAs的值或分布。虽然成熟miRNAs中保守位置(核)的长度相对较长，但真实发夹和背景发夹之间的最小自由能分布差异很小。然而，背景序列来源(物种)的选择被发现既影响最小自由能又影响miRNA发夹的稳定性。在来自玉米幼苗的Illumina数据集上对新参数进行了测试，并将结果与使用默认参数得到的结果进行了比较。与默认模型相比，新的参数化模型具有更高的特异度和灵敏度。总而言之，本研究报告了一些通用的和特定于工具的统计特征的行为，以提高从深度测序数据预测植物miRNAs的准确性。
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.
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