Statistical modeling of RNA structure profiling experiments enables parsimonious reconstruction of structure landscapes.
Statistical modeling of RNA structure profiling experiments enables parsimonious reconstruction of structure landscapes.
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DOI：
10.1038/s41467-018-02923-8
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发表时间：
2018-02-09
影响因子：
16.6
通讯作者：
Aviran S
中科院分区：
文献类型：
--
作者：
Li H;Aviran S
RNA在多种细胞过程中起着关键的调节作用，其功能通常来自于结构的折叠和结构之间的转换。许多rna进一步依赖于替代结构的共存，这些结构控制着它们对细胞信号的反应。然而，无论是在实验上还是在计算上，表征异质景观都是困难的。近年来，结构剖面实验已成为一种强大而经济的结构表征方法，可以改善计算结构预测。迄今为止，人们的努力都集中在预测一种最优结构上，而在多结构预测方面进展甚少。在这里，我们报告了一种概率建模方法，该方法可以预测一组简约的共存结构，并从结构剖面数据中估计它们的丰度。通过分析大量数据集，我们展示了强大的景观重建和对结构动力学的定量见解。这项工作为结构景观的数据定向表征建立了一个框架，以帮助实验者进行结构-功能研究。不同的实验和计算方法可用于研究RNA结构。在这里，作者提出了一种数据导向的复杂RNA结构景观重建的计算方法，该方法预测了一组简约的共存结构，并从结构分析数据中估计了它们的丰度。
RNA plays key regulatory roles in diverse cellular processes, where its functionality often derives from folding into and converting between structures. Many RNAs further rely on co-existence of alternative structures, which govern their response to cellular signals. However, characterizing heterogeneous landscapes is difficult, both experimentally and computationally. Recently, structure profiling experiments have emerged as powerful and affordable structure characterization methods, which improve computational structure prediction. To date, efforts have centered on predicting one optimal structure, with much less progress made on multiple-structure prediction. Here, we report a probabilistic modeling approach that predicts a parsimonious set of co-existing structures and estimates their abundances from structure profiling data. We demonstrate robust landscape reconstruction and quantitative insights into structural dynamics by analyzing numerous data sets. This work establishes a framework for data-directed characterization of structure landscapes to aid experimentalists in performing structure-function studies. Different experimental and computational approaches can be used to study RNA structures. Here, the authors present a computational method for data-directed reconstruction of complex RNA structure landscapes, which predicts a parsimonious set of co-existing structures and estimates their abundances from structure profiling data.
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DOI：
10.1261/rna.055756.115
发表时间：
2016-08
期刊：
RNA (New York, N.Y.)
影响因子：
--
作者：
Deng F;Ledda M;Vaziri S;Aviran S
通讯作者：
Aviran S
影响因子：
5.8
作者：
Choudhary, Krishna;Shih, Nathan P.;Aviran, Sharon
通讯作者：
Aviran, Sharon
DOI：
10.1073/pnas.1219988110
发表时间：
2013-04-02
影响因子：
11.1
作者：
Hajdin, Christine E.;Bellaousov, Stanislav;Weeks, Kevin M.
通讯作者：
Weeks, Kevin M.
影响因子：
5.8
作者：
Choudhary, Krishna;Ruan, Luyao;Aviran, Sharon
通讯作者：
Aviran, Sharon
DOI：
10.1126/science.1215063
发表时间：
2012-01-13
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Baker JL;Sudarsan N;Weinberg Z;Roth A;Stockbridge RB;Breaker RR
通讯作者：
Breaker RR