Characterization of statistical features for plant microRNA prediction

BMC Genomics

Methodology Article

Axel Mosig¹  Mercedes Xu¹  Xin-Guang Zhu¹  Vivek Thakur²  Samart Wanchana²  William Paul Quick³  Richard Bruskiewich³ 

[1] Chinese Academy of Sciences and Max Planck Society (CAS-MPG) Partner Institute for Computational Biology, Key Laboratory of Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, 200031, ShanghaiPR, China;Chinese Academy of Sciences and Max Planck Society (CAS-MPG) Partner Institute for Computational Biology, Key Laboratory of Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 320 Yueyang Road, 200031, ShanghaiPR, China;International Rice Research Institute (IRRI), DAPO Box 7777, Metro Manila, Philippines;International Rice Research Institute (IRRI), DAPO Box 7777, Metro Manila, Philippines;

关键词: miRNA Family;    Mature miRNAs;    Minimum Free Energy;    miRNA Precursor;    Plant miRNAs;   

DOI  :  10.1186/1471-2164-12-108

 received in 2010-08-07, accepted in 2011-02-16,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundSeveral 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.ResultsThe 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.ConclusionsIn 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.

【 授权许可】

CC BY   
© Thakur et al; licensee BioMed Central Ltd. 2011