Comparative Genomic Analysis Using Evidence Integration Frameworks

生物技术和测序的最新突破为复杂基因组序列的比较分析创造了许多新的机会。比较基因组学分析需要建立数据库和工具来解决重要的生物学问题，包括阐明导致物种多样化、健壮性、适应性和分类学组织的保护模式和分歧模式。进化选择在不同的功能位点上产生可变的保守率，从而在不同的基因组区域产生独特的比较特征。这些特征可以通过计算方法用于改进功能重要区域的检测，例如蛋白质编码外显子、RNA基因、启动子区域、起始位点和3 'UTR区域。由于编码区中的保守模式不同于内含子或基因间区域中的模式，因此比较计算分析原则上可以通过使用参考基因组来导致靶基因组中基因的显著改进的计算鉴定。这项工作的重点是对这些基本问题进行系统的调查，目标是建立一个原型，模块化和自适应的基因识别系统，可以在不同的物种上进行训练。 用于比较基因组分析的基因组序列建模和进化比对的模块化和自适应架构具有三个重要特征：a）允许用于特定生物体的跨物种分析的全局模型训练的组成组织。B）一种新的机制，用于对所比较的特定的一对直系同源基因进行模型的贝叶斯适应，以改进解释，从而提高预测准确性。c）灵活整合其他证据来源的能力，用于比较基因鉴定，包括与蛋白质、EST和基因表达数据库的匹配。拟议的研究有许多广泛的影响，编目可以从比较分析和发现新基因中提取的进化模式，建立一个高度可用的比较基因组分析软件原型，免费分发给学术界或提高基因组区域注释的准确性;建立可用于进化分析和生物学研究的功能位点比较数据库，培养新一代的计算生物学研究人员，以更深入地了解生物过程以及复杂的计算生物学。方法论。

Recent breakthroughs in biotechnology and sequencing create numerous new opportunities for comparative analysis of complex genomic sequences. Comparative genomic analysis requires building databases and tools to address important biological questions including elucidating the molds of conservation and patterns of divergence that lead to species diversification, robustness, fitness, and taxonomical organization. Evolutionary selection creates variable rates of conservation on different functional sites thereby producing distinctive comparative signatures in different genomic regions. These signatures can be exploited by computational methods for an improved detection of functionally important regions such as protein-coding exons, RNA genes, promoters regions, initiation sites and 3'UTR regions. Since the pattern of conservation in coding regions is different from the pattern in intronic or intergenic regions, a comparative computational analysis can lead, in principle, to a significantly improved computational identification of genes in a target genome by using a reference genome. This work focuses on a systematic investigation of these fundamental questions with the goal of producing a prototype, modular and adaptive gene identification system that can be trained on different species. A modular and adaptive architecture for modeling genomic sequences and evolutionary alignment for comparative genomic analysis has three important features: a) A compositional organization that allows global model training for cross-species analysis of specific organisms. b) A novel mechanism to perform Bayesian adaptation of the model to the specific pair of orthologous genes being compared in order to refine the interpretation and thereby improve prediction accuracy. c) A flexible capability to integrate other sources of evidence for comparative gene identification that include matches to proteins, ESTs and gene expression databases. There are a number of broad implications of the proposed research, cataloging the evolutionary patterns that can be extracted from comparative analysis and discovery of novel genes, building a highly usable software prototype for comparative genomic analysis that will be distributed freely to the academic community or order to improve the annotation accuracy of genomic regions; Producing comparative databases of functional sites that can be used for evolutionary analysis and biological research, and training a new generation of computational biology researchers to attain a deeper understanding of biological processes as well as sophisticated computational methodologies.