Accurate Plant MicroRNA Prediction Can Be Achieved Using Sequence Motif Features
Accurate Plant MicroRNA Prediction Can Be Achieved Using Sequence Motif Features作者机构：Computer Science The College of Sakhnin Sakhnin Israel The Institute of Applied Research The Galilee Society Shefa-’Amr Israel Molecular Biology and Genetics Izmir Institute of Technology Urla Turkey Bionia Incorporated IZTEKGEB A8 Urla Turkey
出 版 物：《Journal of Intelligent Learning Systems and Applications》 (智能学习系统与应用（英文）)
年 卷 期：2016年第8卷第1期
页 面：9-22页
学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学]
主 题：MicroRNA Prediction Plant Bioinformatics Machine Learning Sequence Motifs
摘 要：MicroRNAs (miRNAs) are short (~21 nt) nucleotide sequences that are either co-transcribed during the production of mRNA or are organized in intergenic regions transcribed by RNA polymerase II. In animals, Drosha, and in plants DCL1 recognize pre-miRNAs which set themselves apart by their characteristic stem loop (hairpin) structure. This structure appears important for their recognition during the process of maturation leading to functioning mature miRNAs. A large body of research is available for computational pre-miRNA detection in animals, but less within the plant kingdom. For the prediction of pre-miRNAs, usually machine learning approaches are employed. Therefore, it is necessary to convert the pre-miRNAs into a set of features that can be calculated and many such features have been described. We here select a subset of the previously described features and add sequence motifs as new features. The resulting model which we called MotifmiRNAPred was tested on known pre-miRNAs listed in miRBase and its accuracy was compared to existing approaches in the field. With an accuracy of 99.95% for the generalized plant model, it distinguishes itself from previously published results which reach an average accuracy between 74% and 98%. We believe that our approach is useful for prediction of pre-miRNAs in plants without per species adjustment.
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