致病同义突变数据库与分析平台构建

项目名称： 致病同义突变数据库与分析平台构建

项目编号： No.61471112

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 无线电电子学、电信技术

项目作者： 朱毅华

作者单位： 东南大学

项目金额： 60万元

中文摘要： 由于不改变所编码的氨基酸，同义突变在以往致病基因突变的研究中往往被忽视,最近研究发现一些同义突变与人类疾病之间存在一定的相关性。高通量生物组学技术的广泛应用，使得我们可以系统全面的认识同义密码子位点存在的基因调控信息。本项目将收集基因组关联分析（GWAS）得到的疾病相关突变、ClinVar数据库中的临床相关突变以及其他文献中报道的疾病相关突变等已经发现的与疾病相关的同义突变数据,并按癌症、营养和代谢疾病、神经系统疾病等15个大类构建致病同义突变数据库。通过分析同义突变对密码子使用偏性、翻译效率、mRNA二级结构以及剪接等过程的影响，预测其可能的致病机制,进一步构建用于机器学习的模式识别特征及其计算方法和分析平台。本研究可对进一步认识疾病相关同义突变的生物学作用以及疾病的致病机制提供重要的参考，并为致病同义突变基因的识别与发现提供方法与工具。

中文关键词： 同义突变；致病同义突变预测；机器学习

英文摘要： Synonymous mutations are overlooked in causative mutation analysis, since they did not change the encoded amino acids. Recent studies found some synonymous mutations are related to human diseases. With the rapid progress of DNA sequencing technology and its wide application in functional genomics, we have found important regulatory roles of synonymous codon choices. In this proposal, we will collect disease related mutations from genome wide association studies, clinical related synonymous mutations deposited in Clinical variation Databases (ClinVar), and other functional synonymous mutations in publications. We will integrate these data with genomic data and its annotation to construct a database of disease related synonymous mutations. We will also analyze some important features that affects regulatory role of synonymous mutations, such as translation efficiency, RNA splicing, RNA structure. Based on these calculations, we will develop a machine-learning algorithm in prediction of disease related synonymous mutations. Finally, we will develop a web server for online prediction. This project will provide an important data source for causative synonymous mutation analysis, and will facilitate the analysis of personal genome and cancer genome.

英文关键词： Synonymous mutation;disease-related synonymous mutations prediction;machine learning