寻找一致、稳定的RNA序列局部最优结构及其在发现核糖开关元件中的应用。

Yuan Li, Cuncong Zhong, Shaojie Zhang
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引用次数: 0

摘要

许多非编码rna (ncRNAs)可以折叠成不同的天然结构并执行不同的生物学功能。通过分析ncRNA的能量格局,可以对ncRNA的替代天然结构进行计算预测。在此之前,我们已经开发了一种计算方法RNASLOpt来预测单个RNA的替代天然结构。在本文中,为了提高预测的准确性,我们将相关ncRNA序列家族的结构保守信息纳入预测中。我们提出了一种比较方法,RNAConSLOpt,以产生所有可能的共识SLOpt堆栈配置,这些配置在一个相关ncrna家族的共识能量景观上是守恒的。基准测试表明,与RNASLOpt相比,RNAConSLOpt可以减少候选结构的数量,并能准确预测ncrna的备选天然结构。此外,该管道在芽孢杆菌属细菌中的应用已经发现了几个新的核糖开关候选物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Finding consensus stable local optimal structures for aligned RNA sequences and its application to discovering riboswitch elements.

Many non-coding RNAs (ncRNAs) can fold into alternate native structures and perform different biological functions. The computational prediction of an ncRNA's alternate native structures can be conducted by analysing the ncRNA's energy landscape. Previously, we have developed a computational approach, RNASLOpt, to predict alternate native structures for a single RNA. In this paper, in order to improve the accuracy of the prediction, we incorporate structural conservation information among a family of related ncRNA sequences to the prediction. We propose a comparative approach, RNAConSLOpt, to produce all possible consensus SLOpt stack configurations that are conserved on the consensus energy landscape of a family of related ncRNAs. Benchmarking tests show that RNAConSLOpt can reduce the number of candidate structures compared with RNASLOpt, and can predict ncRNAs' alternate native structures accurately. Moreover, an application of the proposed pipeline to bacteria in Bacillus genus has discovered several novel riboswitch candidates.

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来源期刊
International Journal of Bioinformatics Research and Applications
International Journal of Bioinformatics Research and Applications Health Professions-Health Information Management
CiteScore
0.60
自引率
0.00%
发文量
26
期刊介绍: Bioinformatics is an interdisciplinary research field that combines biology, computer science, mathematics and statistics into a broad-based field that will have profound impacts on all fields of biology. The emphasis of IJBRA is on basic bioinformatics research methods, tool development, performance evaluation and their applications in biology. IJBRA addresses the most innovative developments, research issues and solutions in bioinformatics and computational biology and their applications. Topics covered include Databases, bio-grid, system biology Biomedical image processing, modelling and simulation Bio-ontology and data mining, DNA assembly, clustering, mapping Computational genomics/proteomics Silico technology: computational intelligence, high performance computing E-health, telemedicine Gene expression, microarrays, identification, annotation Genetic algorithms, fuzzy logic, neural networks, data visualisation Hidden Markov models, machine learning, support vector machines Molecular evolution, phylogeny, modelling, simulation, sequence analysis Parallel algorithms/architectures, computational structural biology Phylogeny reconstruction algorithms, physiome, protein structure prediction Sequence assembly, search, alignment Signalling/computational biomedical data engineering Simulated annealing, statistical analysis, stochastic grammars.
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