The prediction of the porcine pre-microRNAs in genome-wide based on support vector machine (SVM) and homology searching

BMC Genomics

Research Article

Yumei Yang¹  Qishan Wang¹  Zhen Wang¹  Yuchun Pan¹  Kan He² 

[1] School of Agriculture and Biology, Department of Animal Science, Shanghai Jiao Tong University, 200240, Shanghai, PR China;Shanghai Key Laboratory of Veterinary Biotechnology, 200240, Shanghai, PR China;School of Agriculture and Biology, Department of Animal Science, Shanghai Jiao Tong University, 200240, Shanghai, PR China;Shanghai Key Laboratory of Veterinary Biotechnology, 200240, Shanghai, PR China;Department of Biology, Faculty of Science, Hong Kong Baptist University, Hong Kong, China;

关键词: Porcine;    Pre-miRNA;    SVM;    Homology searching;   

DOI  :  10.1186/1471-2164-13-729

 received in 2011-12-26, accepted in 2012-12-22,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundMicroRNAs (miRNAs) are a class of small non-coding RNAs that regulate gene expression by targeting mRNAs for translation repression or mRNA degradation. Although many miRNAs have been discovered and studied in human and mouse, few studies focused on porcine miRNAs, especially in genome wide.ResultsHere, we adopted computational approaches including support vector machine (SVM) and homology searching to make a global scanning on the pre-miRNAs of pigs. In our study, we built the SVM-based porcine pre-miRNAs classifier with a sensitivity of 100%, a specificity of 91.2% and a total prediction accuracy of 95.6%, respectively. Moreover, 2204 novel porcine pre-miRNA candidates were found by using SVM-based pre-miRNAs classifier. Besides, 116 porcine pre-miRNA candidates were detected by homology searching.ConclusionsWe identified the porcine pre-miRNA in genome-wide through computational approaches by utilizing the data sets of pigs and set up the porcine pre-miRNAs library which may provide us a global scanning on the pre-miRNAs of pigs in genome level and would benefit subsequent experimental research on porcine miRNA functional and expression analysis.

【 授权许可】

Unknown   
© Wang et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.