• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

稳定的茎状结构使香农熵能够将非编码 RNA 与随机背景区分开来。

Stable stem enabled Shannon entropies distinguish non-coding RNAs from random backgrounds.

机构信息

Department of Computer Science, University of Georgia, Athens, Georgia 30602, USA.

出版信息

BMC Bioinformatics. 2012 Apr 12;13 Suppl 5(Suppl 5):S1. doi: 10.1186/1471-2105-13-S5-S1.

DOI:10.1186/1471-2105-13-S5-S1
PMID:22537005
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3358654/
Abstract

BACKGROUND

The computational identification of RNAs in genomic sequences requires the identification of signals of RNA sequences. Shannon base pairing entropy is an indicator for RNA secondary structure fold certainty in detection of structural, non-coding RNAs (ncRNAs). Under the Boltzmann ensemble of secondary structures, the probability of a base pair is estimated from its frequency across all the alternative equilibrium structures. However, such an entropy has yet to deliver the desired performance for distinguishing ncRNAs from random sequences. Developing novel methods to improve the entropy measure performance may result in more effective ncRNA gene finding based on structure detection.

RESULTS

This paper shows that the measuring performance of base pairing entropy can be significantly improved with a constrained secondary structure ensemble in which only canonical base pairs are assumed to occur in energetically stable stems in a fold. This constraint actually reduces the space of the secondary structure and may lower the probabilities of base pairs unfavorable to the native fold. Indeed, base pairing entropies computed with this constrained model demonstrate substantially narrowed gaps of Z-scores between ncRNAs, as well as drastic increases in the Z-score for all 13 tested ncRNA sets, compared to shuffled sequences.

CONCLUSIONS

These results suggest the viability of developing effective structure-based ncRNA gene finding methods by investigating secondary structure ensembles of ncRNAs.

摘要

背景

在基因组序列中计算识别 RNA 需要识别 RNA 序列的信号。香农碱基配对熵是检测结构非编码 RNA(ncRNA)时 RNA 二级结构折叠确定性的指标。在二级结构的玻尔兹曼系综中,根据其在所有替代平衡结构中的频率来估计碱基对的概率。然而,这种熵在区分 ncRNA 和随机序列方面尚未达到预期的性能。开发改进熵度量性能的新方法可能会导致基于结构检测的更有效的 ncRNA 基因发现。

结果

本文表明,通过假设在折叠中仅发生在能量稳定茎中的规范碱基对,可以显著提高碱基配对熵的测量性能。这种约束实际上缩小了二级结构的空间,并且可能降低了不利于天然折叠的碱基对的概率。实际上,与随机序列相比,使用这种约束模型计算的碱基配对熵显示出 ncRNA 之间 Z 分数差距明显缩小,以及所有 13 个测试的 ncRNA 集的 Z 分数都有了大幅提高。

结论

这些结果表明,通过研究 ncRNA 的二级结构系综,开发有效的基于结构的 ncRNA 基因发现方法是可行的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/024b/3358654/4502b86bb073/1471-2105-13-S5-S1-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/024b/3358654/a8cb25aa83b0/1471-2105-13-S5-S1-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/024b/3358654/c1bd27b489ac/1471-2105-13-S5-S1-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/024b/3358654/b2a4ddd0c921/1471-2105-13-S5-S1-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/024b/3358654/4502b86bb073/1471-2105-13-S5-S1-4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/024b/3358654/a8cb25aa83b0/1471-2105-13-S5-S1-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/024b/3358654/c1bd27b489ac/1471-2105-13-S5-S1-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/024b/3358654/b2a4ddd0c921/1471-2105-13-S5-S1-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/024b/3358654/4502b86bb073/1471-2105-13-S5-S1-4.jpg

相似文献

1
Stable stem enabled Shannon entropies distinguish non-coding RNAs from random backgrounds.稳定的茎状结构使香农熵能够将非编码 RNA 与随机背景区分开来。
BMC Bioinformatics. 2012 Apr 12;13 Suppl 5(Suppl 5):S1. doi: 10.1186/1471-2105-13-S5-S1.
2
TurboFold: iterative probabilistic estimation of secondary structures for multiple RNA sequences.TurboFold:用于多个 RNA 序列的二级结构的迭代概率估计。
BMC Bioinformatics. 2011 Apr 20;12:108. doi: 10.1186/1471-2105-12-108.
3
Analyzing modular RNA structure reveals low global structural entropy in microRNA sequence.分析模块化RNA结构揭示了微小RNA序列中较低的全局结构熵。
J Bioinform Comput Biol. 2011 Apr;9(2):283-98. doi: 10.1142/s0219720011005495.
4
Detection of non-coding RNAs on the basis of predicted secondary structure formation free energy change.基于预测的二级结构形成自由能变化检测非编码RNA。
BMC Bioinformatics. 2006 Mar 27;7:173. doi: 10.1186/1471-2105-7-173.
5
Directed acyclic graph kernels for structural RNA analysis.用于结构RNA分析的有向无环图核
BMC Bioinformatics. 2008 Jul 22;9:318. doi: 10.1186/1471-2105-9-318.
6
Information-theoretic uncertainty of SCFG-modeled folding space of the non-coding RNA.非编码 RNA 的 SCFG 模型折叠空间的信息论不确定性。
J Theor Biol. 2013 Feb 7;318:140-63. doi: 10.1016/j.jtbi.2012.10.023. Epub 2012 Nov 14.
7
From structure prediction to genomic screens for novel non-coding RNAs.从结构预测到新型非编码 RNA 的基因组筛选。
PLoS Comput Biol. 2011 Aug;7(8):e1002100. doi: 10.1371/journal.pcbi.1002100. Epub 2011 Aug 4.
8
A local multiple alignment method for detection of non-coding RNA sequences.一种用于检测非编码RNA序列的局部多重比对方法。
Bioinformatics. 2009 Jun 15;25(12):1498-505. doi: 10.1093/bioinformatics/btp261. Epub 2009 Apr 17.
9
STRAL: progressive alignment of non-coding RNA using base pairing probability vectors in quadratic time.STRAL:利用碱基配对概率向量在二次时间内对非编码RNA进行渐进比对。
Bioinformatics. 2006 Jul 1;22(13):1593-9. doi: 10.1093/bioinformatics/btl142. Epub 2006 Apr 13.
10
RNA secondary structure prediction using conditional random fields model.使用条件随机场模型进行RNA二级结构预测。
Int J Data Min Bioinform. 2013;7(2):118-34. doi: 10.1504/ijdmb.2013.053195.

引用本文的文献

1
Secondary structural entropy in RNA switch (Riboswitch) identification.RNA开关(核糖开关)识别中的二级结构熵
BMC Bioinformatics. 2015 Apr 28;16:133. doi: 10.1186/s12859-015-0523-2.
2
Characterising RNA secondary structure space using information entropy.利用信息熵刻画 RNA 二级结构空间。
BMC Bioinformatics. 2013;14 Suppl 2(Suppl 2):S22. doi: 10.1186/1471-2105-14-S2-S22. Epub 2013 Jan 21.
3
Information-theoretic uncertainty of SCFG-modeled folding space of the non-coding RNA.非编码 RNA 的 SCFG 模型折叠空间的信息论不确定性。

本文引用的文献

1
Analysis of four-way junctions in RNA structures.RNA结构中四向接头的分析。
J Mol Biol. 2009 Jul 17;390(3):547-59. doi: 10.1016/j.jmb.2009.04.084. Epub 2009 May 13.
2
Infernal 1.0: inference of RNA alignments.Infernal 1.0:RNA比对推断
Bioinformatics. 2009 May 15;25(10):1335-7. doi: 10.1093/bioinformatics/btp157. Epub 2009 Mar 23.
3
Computational methods in noncoding RNA research.非编码RNA研究中的计算方法。
J Theor Biol. 2013 Feb 7;318:140-63. doi: 10.1016/j.jtbi.2012.10.023. Epub 2012 Nov 14.
4
Simultaneous prediction of RNA secondary structure and helix coaxial stacking.同时预测 RNA 二级结构和螺旋共轴堆积。
BMC Genomics. 2012 Jun 11;13 Suppl 3(Suppl 3):S7. doi: 10.1186/1471-2164-13-S3-S7.
J Math Biol. 2008 Jan;56(1-2):15-49. doi: 10.1007/s00285-007-0122-6. Epub 2007 Sep 4.
4
Predicting helical coaxial stacking in RNA multibranch loops.预测RNA多分支环中的螺旋同轴堆积
RNA. 2007 Jul;13(7):939-51. doi: 10.1261/rna.305307. Epub 2007 May 16.
5
Annotating noncoding RNA genes.注释非编码RNA基因。
Annu Rev Genomics Hum Genet. 2007;8:279-98. doi: 10.1146/annurev.genom.8.080706.092419.
6
Identification and classification of conserved RNA secondary structures in the human genome.人类基因组中保守RNA二级结构的鉴定与分类
PLoS Comput Biol. 2006 Apr;2(4):e33. doi: 10.1371/journal.pcbi.0020033. Epub 2006 Apr 21.
7
Detection of non-coding RNAs on the basis of predicted secondary structure formation free energy change.基于预测的二级结构形成自由能变化检测非编码RNA。
BMC Bioinformatics. 2006 Mar 27;7:173. doi: 10.1186/1471-2105-7-173.
8
Topology of three-way junctions in folded RNAs.折叠RNA中三向接头的拓扑结构。
RNA. 2006 Jan;12(1):83-93. doi: 10.1261/rna.2208106.
9
Natural selection is not required to explain universal compositional patterns in rRNA secondary structure categories.解释rRNA二级结构类别中的普遍组成模式并不需要自然选择。
RNA. 2006 Jan;12(1):1-14. doi: 10.1261/rna.2183806.
10
A comparison of RNA folding measures.RNA折叠度量的比较
BMC Bioinformatics. 2005 Oct 3;6:241. doi: 10.1186/1471-2105-6-241.