Stochastic Sampling of Structural Contexts Improves the Scalability and Accuracy of RNA 3D Modules Identification
Stochastic Sampling of Structural Contexts Improves the Scalability and Accuracy of RNA 3D Modules Identification
复制标题
结构上下文的随机采样提高了 RNA 3D 模块识别的可扩展性和准确性
DOI：
10.1101/834762
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
J. Waldispühl
中科院分区：
文献类型：
--
作者：
Roman Sarrazin;Hua;Vladimir Reinharz;C. Oliver;Yann Ponty;J. Waldispühl
RNA结构具有多层次的结构组织。二级结构由正则(即Watson-Crick和Wobble)螺旋组成，由环路连接，其局部构象是全球3D建筑的关键决定因素。这种局部3D结构由保守的非正则碱基对组成，称为RNA模块。因此，从序列数据预测它们是迈向3D结构建模的里程碑。遗憾的是，目前的3D模块识别方法的计算效率和范围都太有限，无法从模块数据库中积累的所有知识中受益。这里，我们介绍了BayesPairing 2，这是一种利用二级结构树分解的新的序列搜索算法，它可以降低计算复杂度，提高对新序列的预测。我们在75个模块和6360个RNA序列上对我们的方法进行了基准测试，报告的准确性与最先进的水平相当，运行时间有了相当大的改善。当识别单个序列上的200个模块时，BayesPairing 2比它的前一个版本快100倍以上，为全基因组应用打开了新的大门。
RNA structures possess multiple levels of structural organization. Secondary structures are made of canonical (i.e. Watson-Crick and Wobble) helices, connected by loops whose local conformations are critical determinants of global 3D architectures. Such local 3D structures consist of conserved sets of non-canonical base pairs, called RNA modules. Their prediction from sequence data is thus a milestone toward 3D structure modelling. Unfortunately, the computational efficiency and scope of the current 3D module identification methods are too limited yet to benefit from all the knowledge accumulated in modules databases. Here, we introduce BayesPairing 2, a new sequence search algorithm leveraging secondary structure tree decomposition which allows to reduce the computational complexity and improve predictions on new sequences. We benchmarked our methods on 75 modules and 6360 RNA sequences, and report accuracies that are comparable to the state of the art, with considerable running time improvements. When identifying 200 modules on a single sequence, BayesPairing 2 is over 100 times faster than its previous version, opening new doors for genome-wide applications.
登录
查看更多内容
影响因子：
16.6
作者：
Mustoe AM;Brooks CL;Al-Hashimi HM
通讯作者：
Al-Hashimi HM
影响因子：
--
作者：
Du, ZH;Lind, KE;James, TL
通讯作者：
James, TL
影响因子：
5.6
作者：
Tinoco, I;Bustamante, C
通讯作者：
Bustamante, C
影响因子：
4.5
作者：
Leontis, NB;Westhof, E
通讯作者：
Westhof, E