Development of bioinformatics tools to understand mechanisms of non-coding small RNA interactions

开发生物信息学工具来了解非编码小RNA相互作用的机制

• 批准号: RGPIN-2014-04195
• 负责人: Turcotte, Marcel
• 金额: $ 1.46万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=587660
• 关键词: Development; bioinformatics; tools; understand; mechanisms

RNA elements are transcribed (expressed) DNA fragments that are not translated into proteins. They play key roles in translation, splicing, and gene regulation. In Diplonema, mitochondrial genes are fragmented into modules, which are transcribed separately and then assembled by an unknown mechanism. It has been hypothesized that this process be mediated by RNA elements. Likewise in animal cells, messenger RNAs and RNA elements are packaged together in a non-random fashion into microvisicles called exosomes. These vesicles play important roles in regular and dysfunctional physiological processes. What is common to the above examples is that the biological function of these elements critically depends on intra- and inter- RNA interactions. Computational tools to assist the identification and annotation of RNA interaction motifs are seriously lacking. Our group has solid expertise in solving RNA structure puzzles. In recent years, we developed tools for the simultaneous alignment and structure prediction of RNA sequences (eXtended Dynalign and pDynalign). We used suffix arrays for the discovery of RNA secondary structure motifs (Seed). More recently, we adapted frequent subgraph mining for the discovery of RNA interaction motifs (RiboFSM). With our life science collaborators, we studied the IRES motifs in the UTRs of mammalian genes, and developed computational approaches to investigate the possible role of RNA elements in cis- and trans- splicing activities in the mitochondrial genome of Diplonema. High-throughput (HT) methods, such as expression microarrays, genome-wide physical and genomic interaction screens, while allowing to monitor the behavior of the cell as a whole, are generating wealth of information that needs to be studied and interpreted. To help experts interpret experimental data, we recently proposed a novel logic-based Annotation Concept Synthesis and Enrichment Analysis (ACSEA) approach. ACSEA merges inductive logic reasoning with statistical inference to discover complex concepts from experiments. Finally, we also introduced a logic based approach, called Module Inducer, to study the architecture of DNA landmarks. These approaches will be further extended in the proposed research. This proposal has two themes and five objectives: 1. Identification of RNA interaction motifs 1.1 Adapt frequent subgraph mining to determine RNA interaction motifs 1.2 Generalize the suffix array based approaches, namely Seed, to determine RNA interaction motifs 1.3 Apply and compare the newly developed tools to analyze the mitochondrial genome of Diplonema, as well as exosome shuttle RNAs 2. Integration and mining of heterogeneous data 2.1 Research the use of Description Logic, as an alternative to first-order logic, for Annotation Concept Synthesis and Enrichment Analysis 2.2 Extend Module Inducer to use ACSEA for the inference of structural rules describing the architecture of transcription factor binding sites The long term objective of this research is to combine the motif discovery methods developed in theme 1 with the data integration techniques established in theme 2 so as to create performant ncRNA gene detection methods combining structural and contextual information. These tools will help life science experts validate or refute hypotheses about the roles and mechanisms of RNA elements. RNA elements play critical roles in the cell, the dysregulation of the associated processes is often associated with disease state, consequently progress in understanding them will therefore have a direct impact on human health, agriculture, and on our understanding of cellular biology in general.

RNA元件是不翻译成蛋白质的转录（表达）DNA片段。它们在翻译、剪接和基因调控中发挥关键作用。在Diplonema中，线粒体基因被片段化为模块，这些模块被单独转录，然后通过未知的机制组装。据推测，这一过程由RNA元件介导。同样，在动物细胞中，信使RNA和RNA元件以非随机的方式包装在一起，形成称为外泌体的微泡。这些囊泡在正常和功能失调的生理过程中发挥重要作用。上述例子的共同之处在于，这些元件的生物学功能关键取决于RNA内和RNA间的相互作用。计算机工具，以协助识别和注释的RNA相互作用基序是严重缺乏。 我们的团队在解决RNA结构难题方面拥有坚实的专业知识。近年来，我们开发了用于RNA序列的同时比对和结构预测的工具（eXtended Dynalign和pDynalign）。我们使用后缀阵列来发现RNA二级结构基序（Seed）。最近，我们采用频繁子图挖掘来发现RNA相互作用基序（RiboFSM）。与我们的生命科学合作者一起，我们研究了哺乳动物基因UTR中的IRES基序，并开发了计算方法来研究RNA元件在Diplonema线粒体基因组中顺式和反式剪接活动中的可能作用。 高通量（HT）方法，如表达微阵列，全基因组物理和基因组相互作用筛选，同时允许监测细胞作为一个整体的行为，产生大量的信息，需要研究和解释。为了帮助专家解释实验数据，我们最近提出了一种新的基于逻辑的注释概念合成和富集分析（ACSEA）方法。ACSEA将归纳逻辑推理与统计推理相结合，从实验中发现复杂的概念。最后，我们还介绍了一种基于逻辑的方法，称为模块诱导器，来研究DNA界标的结构。这些方法将在拟议的研究中进一步扩展。 该提案有两个主题和五个目标： 1. RNA相互作用基序的鉴定 1.1利用频繁子图挖掘确定RNA相互作用模体 1.2推广基于后缀数组的方法，即种子，以确定RNA相互作用基序 1.3应用和比较新开发的工具来分析双丝藻的线粒体基因组以及外泌体穿梭RNA 2.异构数据的集成与挖掘 2.1研究描述逻辑的使用，作为一阶逻辑的替代，用于注释概念合成和丰富分析 2.2本研究的长期目标是将主题1中提出的模体发现方法与主题2中建立的数据集成技术联合收割机相结合，建立一种结合结构信息和背景信息的高效ncRNA基因检测方法。 这些工具将帮助生命科学专家验证或反驳有关RNA元件的作用和机制的假设。RNA元件在细胞中起着关键作用，相关过程的失调通常与疾病状态有关，因此了解它们的进展将对人类健康，农业以及我们对细胞生物学的理解产生直接影响。