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首页> 外文期刊>BMC Genomics >Genome-scale MicroRNA target prediction through clustering with Dirichlet process mixture model
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Genome-scale MicroRNA target prediction through clustering with Dirichlet process mixture model

机译：基因组级MicroRNA通过与Dirichlet过程混合模型聚类的靶预测

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MicroRNA regulation is fundamentally responsible for fine-tuning the whole gene network in human and has been implicated in most physiological and pathological conditions. Studying regulatory impact of microRNA on various cellular and disease processes has resulted in numerous computational tools that investigate microRNA-mRNA interactions through the prediction of static binding site highly dependent on sequence pairing. However, what hindered the practical use of such target prediction is the interplay between competing and cooperative microRNA binding that complicates the whole regulatory process exceptionally. We developed a new method for improved microRNA target prediction based on Dirichlet Process Gaussian Mixture Model (DPGMM) using a large collection of molecular features associated with microRNA, mRNA, and the interaction sites. Multiple validations based on microRNA-mRNA interactions reported in recent large-scale sequencing analyses and a screening test on the entire human transcriptome show that our model outperformed several state-of-the-art tools in terms of promising predictive power on binding sites specific to transcript isoforms with reduced false positive prediction. Last, we illustrated the use of predicted targets in constructing conditional microRNA-mediated gene regulation networks in human cancer. The probability-based binding site prediction provides not only a useful tool for differentiating microRNA targets according to the estimated binding potential but also a capability highly important for exploring dynamic regulation where binding competition is involved.
机译：MicroRNA规则从根本上涉及细微调整人类的整个基因网络，并涉及大多数生理和病理条件。研究MicroRNA对各种细胞和疾病过程的调节局导致许多计算工具通过高度依赖于序列配对的静态结合位点来研究微小RNA-mRNA相互作用。然而，这种目标预测的实际使用是什么阻碍了竞争和合作微小RNA结合之间的相互作用，其使整个监管过程的异常例外。我们利用与微窝，mRNA和相互作用位点相关的大量分子特征，开发了一种基于Dirichlet工艺高斯混合模型（DPGMM）的微小RONA靶预测的新方法。基于MicroRNA-mRNA相互作用的多种验证报告，在近期大规模测序分析和整个人体转录组上的筛选试验表明，在有希望的预测性的预测性的预测力方面，我们的模型在特定于其特异性的预测力方面表现出几种最先进的工具转录同种型具有减少的假阳性预测。最后，我们说明了使用预测的靶标在人癌中构建条件微小RORNA介导的基因调控网络中的使用。基于概率的绑定站点预测不仅提供了根据估计的结合潜力来区分微瘤目标的有用工具，而且对于探索涉及结合竞争的动态调节，这也是非常重要的能力。

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