Titov Igor I, Vorozheykin Pavel S
Institute of Cytology and Genetics, SB RAS, 10 Lavrentyev Avenue, Novosibirsk 630090, Russian Federation , Novosibirsk State University, 2 Pirogov Street, Novosibirsk 630090, Russian Federation.
J Bioinform Comput Biol. 2013 Dec;11(6):1343009. doi: 10.1142/S0219720013430099. Epub 2013 Dec 11.
MicroRNAs (miRNAs) are small single-stranded noncoding RNAs that play an important role in post-transcriptional regulation of gene expression. In this paper, we present a web server for ab initio prediction of the human miRNAs and their precursors. The prediction methods are based on the hidden Markov Models and the context-structural characteristics. By taking into account the identified patterns of primary and secondary structures of the pre-miRNAs, a new HMM model is proposed and the existing context-structural Markov model is modified. The evaluation of the method performance has shown that it can accurately predict novel human miRNAs. Comparing with the existing methods we demonstrate that our method has a higher prediction quality both for human pre-miRNAs and miRNAs. The models have also showed good results in the prediction of the mouse miRNAs. The web server is available at http://wwwmgs.bionet.nsc.ru/mgs/programs/rnaanalys (mirror http://miRNA.at.nsu.ru ).
微小RNA（miRNA）是一类小的单链非编码RNA，在基因表达的转录后调控中发挥着重要作用。在本文中，我们展示了一个用于从头预测人类miRNA及其前体的网络服务器。预测方法基于隐马尔可夫模型和上下文结构特征。通过考虑已识别的前体miRNA的一级和二级结构模式，提出了一种新的HMM模型，并对现有的上下文结构马尔可夫模型进行了修改。方法性能评估表明，它可以准确预测新的人类miRNA。与现有方法相比，我们证明我们的方法在预测人类前体miRNA和miRNA方面都具有更高的预测质量。这些模型在预测小鼠miRNA方面也取得了良好的结果。该网络服务器可在http://wwwmgs.bionet.nsc.ru/mgs/programs/rnaanalys（镜像http://miRNA.at.nsu.ru）上获取。
