摘要
MicroRNAs are an important subclass of non-coding RNAs (ncRNA), and serve as main players into RNA interference (RNAi). Mature microRNA derived from stem-loop structure called precursor. Identification of precursor microRNA (pre-miRNA) is essential step to target microRNA in whole genome. The present work proposed 25 novel local features for identifying stem- loop structure of pre-miRNAs, which captures characteristics on both the sequence and structure. Firstly, we pulled the stem of hairpins and aligned the bases in bulges and internal loops used ‘―’, and then counted 24 base-pairs (‘AA’, ‘AU’, …, ‘―G’, except ‘――’) in pulled stem (formalized by length of pulled stem) as features vector of Support Vector Machine (SVM). Performances of three classifiers with our features and different kernels trained on human data were all superior to Triplet-SVM-classifier’s in po- sitive and negative testing data sets. Moreover, we achieved higher prediction accuracy through combining 7 global sequence-structure. The result indicates validity of novel local features.
MicroRNAs are an important subclass of non-coding RNAs (ncRNA), and serve as main players into RNA interference (RNAi). Mature microRNA derived from stem-loop structure called precursor. Identification of precursor microRNA (pre-miRNA) is essential step to target microRNA in whole genome. The present work proposed 25 novel local features for identifying stem- loop structure of pre-miRNAs, which captures characteristics on both the sequence and structure. Firstly, we pulled the stem of hairpins and aligned the bases in bulges and internal loops used ‘―’, and then counted 24 base-pairs (‘AA’, ‘AU’, …, ‘―G’, except ‘――’) in pulled stem (formalized by length of pulled stem) as features vector of Support Vector Machine (SVM). Performances of three classifiers with our features and different kernels trained on human data were all superior to Triplet-SVM-classifier’s in po- sitive and negative testing data sets. Moreover, we achieved higher prediction accuracy through combining 7 global sequence-structure. The result indicates validity of novel local features.
