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StreAM-... formula ...: algorithms for analyzing coarse grained RNA dynamics based on Markov models of connectivity-graphs

机译:StreAM-...公式...:基于连通图的马尔可夫模型的粗粒RNA动力学分析算法

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摘要

BackgroundIn this work, we present a new coarse grained representation of RNA dynamics. It is based on adjacency matrices and their interactions patterns obtained from molecular dynamics simulations. RNA molecules are well-suited for this representation due to their composition which is mainly modular and assessable by the secondary structure alone. These interactions can be represented as adjacency matrices of k nucleotides. Based on those, we define transitions between states as changes in the adjacency matrices which form Markovian dynamics. The intense computational demand for deriving the transition probability matrices prompted us to develop StreAM-Tg, a stream-based algorithm for generating such Markov models of k-vertex adjacency matrices representing the RNA.
机译:背景在这项工作中,我们提出了一种新的粗略的RNA动力学表示方法。它基于邻接矩阵及其从分子动力学模拟获得的相互作用模式。由于RNA分子的组成主要是模块化的,并且仅通过二级结构即可评估,因此非常适合此表示形式。这些相互作用可以表示为k个核苷酸的邻接矩阵。基于这些,我们将状态之间的过渡定义为形成马尔可夫动力学的邻接矩阵的变化。对导出过渡概率矩阵的大量计算需求促使我们开发StreAM-Tg,这是一种基于流的算法,用于生成代表RNA的k顶点邻接矩阵的此类Markov模型。

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