HHMMiR: efficient de novo prediction of microRNAs using hierarchical hidden Markov models.
HHMMiR: efficient de novo prediction of microRNAs using hierarchical hidden Markov models.
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DOI：
10.1186/1471-2105-10-s1-s35
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发表时间：
2009-01-30
影响因子：
3
通讯作者：
Benos PV
中科院分区：
文献类型：
--
作者：
Kadri S;Hinman V;Benos PV
微小RNA（miRNA）是小型非编码单链RNA（20-23 nt），已知其充当基因表达的转录后和翻译调节因子。虽然它们最初被忽视，但它们在许多重要的生物学过程中的作用，如发育，细胞分化和癌症，近年来已被确定。尽管它们具有生物学意义，但在新测序的生物体中鉴定miRNA基因在很大程度上仍然基于进化保守的广泛使用，这并不总是可用的。我们已经开发了HHMMiR，一种在缺乏进化保守性的情况下进行从头miRNA发夹预测的新方法。我们的方法实现了层次隐马尔可夫模型（HHMM），利用基于区域的结构以及序列信息的miRNA前体。我们首先通过总结来自公开数据库的数据建立了典型miRNA发夹结构的模板。然后我们使用这个模板来开发HHMM拓扑。我们的算法实现了84%的平均灵敏度和88%的特异性，对人类miRNA前体数据的10倍交叉验证。我们还表明，这个模型，对人类序列的训练，以及其他脊椎动物和无脊椎动物物种的发夹。此外，人类训练的模型能够正确分类约97%的植物miRNA前体。这种方法在如此多样化的物种中的成功表明序列保守性对于miRNA预测是不必要的。这可能导致有效预测几乎任何生物体中的miRNA基因。
MicroRNAs (miRNAs) are small non-coding single-stranded RNAs (20–23 nts) that are known to act as post-transcriptional and translational regulators of gene expression. Although, they were initially overlooked, their role in many important biological processes, such as development, cell differentiation, and cancer has been established in recent times. In spite of their biological significance, the identification of miRNA genes in newly sequenced organisms is still based, to a large degree, on extensive use of evolutionary conservation, which is not always available. We have developed HHMMiR, a novel approach for de novo miRNA hairpin prediction in the absence of evolutionary conservation. Our method implements a Hierarchical Hidden Markov Model (HHMM) that utilizes region-based structural as well as sequence information of miRNA precursors. We first established a template for the structure of a typical miRNA hairpin by summarizing data from publicly available databases. We then used this template to develop the HHMM topology. Our algorithm achieved average sensitivity of 84% and specificity of 88%, on 10-fold cross-validation of human miRNA precursor data. We also show that this model, trained on human sequences, works well on hairpins from other vertebrate as well as invertebrate species. Furthermore, the human trained model was able to correctly classify ~97% of plant miRNA precursors. The success of this approach in such a diverse set of species indicates that sequence conservation is not necessary for miRNA prediction. This may lead to efficient prediction of miRNA genes in virtually any organism.