Prediction of pre-miRNA with multiple stem-loops using pruning algorithm

Authors:
Xiaofeng Song;Minghao Wang;Yi-Ping Phoebe Chen;Huating Wang;Ping Han;Hao Sun
Affiliations:
Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;Faculty of Science, Technology and Engineering, La Trobe University, Melbourne, Australia;Department of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China and Li Ka Shing Institute of Health Sciences, The Chinese University of ...;The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, China;Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China and Department of Chemical Pathology, The Chinese University of Hong Ko ...
Venue:
Computers in Biology and Medicine
Year:
2013

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Abstract

In addition to experimental identification of pre-miRNAs, the computational prediction method is also becoming a hot research spot. Most existing prediction methods are usually excluding those pre-miRNAs with multiple loops. But as more and more miRNA have been identified, quite a number of miRNA precursor with multiple loops have been found. Therefore, determining how to effectively identify pre-miRNAs with multiple loops from the control dataset with multiple loops is an imperative problem. In this work, a pruning algorithm is presented to identify the main branch from the multiple stem-loops of pre-miRNA. A stack algorithm is employed to describe the secondary structure of pre-miRNA in four different patterns, and a recursive algorithm is employed to split the multiple stem-loops of pre-miRNA into several small branches, and to identify its main branch. Statistic results indicate that the information of the main branch can be represented as the whole sequence of pre-miRNA. Some features of main branch are extracted to describe pre-miRNA intrinsic features, and SVM classifier is implemented to recognize real pre-miRNA with multiple stem-loops. Based on training and testing on dataset from miRBase12.0, SVM classifier achieves sensitivity of 75.76% on RM-POS and specificity of 98.12% on RM-CDS, and specificity of 91.28% on RM-NCR. The obtained results indicated that the information of main branch after pruning can represent intrinsic features of pre-miRNA with multiple stem-loops. The proposed method in this work provides a powerful predicting method to recognize the real pre-miRNA with multiple stem-loops.