长非编码RNA功能预测网络模型与算法研究

项目名称： 长非编码RNA功能预测网络模型与算法研究

项目编号： No.61303118

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 郭杏莉

作者单位： 西安电子科技大学

项目金额： 28万元

中文摘要： 基因组本身尤其是控制基因开关的复杂调控机制导致了物种及个体间的巨大差异，人类基因组大约98%非编码基因参与调控2%蛋白质编码基因的表达，尤其是长非编码RNA参与调控了不同层面的基因表达，具有重要的生物功能，与重大疾病密切相关。面临数量不断增加的长非编码RNA，本项目拟开展大规模长非编码RNA功能预测问题的网络模型与算法研究。通过集成多个层面不同类型的长非编码RNA数据，研究多源数据分析、集成方法，构建反映长非编码RNA与其它生物分子功能关联的生物网络；研究基于网络模型的高效算法，实现大规模长非编码RNA功能预测；基于功能预测结果，分析其功能类别，研究长非编码RNA数据的生物特征与功能之间的内在联系，揭示隐含的生物学规律，研究功能异常与疾病的关系，探索长非编码RNA作为潜在分子标记和药物靶标的应用价值；最后开发长非编码RNA相关数据库和功能预测软件。

中文关键词： 长非编码RNA；复杂网络；功能预测；二级结构；复杂疾病

英文摘要： The huge differences arising from organisms and individuals are ascribed to not only the genome itself but also the regulatory principles of gene switches controlling gene expression. There are about 98% non-coding genes controlling 2% protein-coding genes expression in the human genome. Especially the long non-coding RNAs (lncRNAs), which play many key roles in diverse biological processes, and are involved in human diseases. With accumulating number of lncRNAs whose functions are remained to be uncovered, to investigate their functions at large scale is an urgent and challenging task. In this project, network models and algorithms for large-scale lncRNA function prediction are focused and investigated. The biological network are constructed by analyzing and integrating multi-source lncRNA data, representing the functional links among lncRNAs and other biological molecules. The network-based algorithms with superior performance are designed to tackle the task of large-scale function prediction. Based on the function annotation of lncRNAs, function categories for lncRNAs are analyzed and clustered; the inherent relationship between the function annotation and the biological properties of lncRNA data are investigated. And that between the dysfunctions in lncRNAs and complex diseases will be analyzed and exploited

英文关键词： long noncoding RNA；complex network；function prediction；secondary structure；complex disease