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、稳健性、适应性和分类组织的保护模式和分化模式。进化选择在不同的功能位点上创造了不同的保护速率，从而在不同的基因组区域产生了不同的比较特征。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，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。由于编码区域的保守模式不同于内含子或基因间区域的模式，原则上，通过使用参考基因组，比较计算分析可以显著提高靶基因组中基因的计算鉴定。这项工作的重点是对这些基本问题进行系统的调查，目的是建立一个原型，模块化和适应性的基因识别系统，可以在不同的物种上进行训练。用于基因组序列建模和比较基因组分析的进化比对的模块化和自适应架构具有三个重要特征：A)一个组合组织，允许对特定生物的跨物种分析进行全局模型训练。b)一种新的机制来执行贝叶斯模型适应特定的同源基因对进行比较，以完善解释，从而提高预测精度。c)灵活地整合其他来源的证据进行比较基因鉴定，包括与蛋白质、ESTs和基因表达数据库的匹配。提出的研究有许多广泛的意义，对可以从比较分析和新基因发现中提取的进化模式进行编目，建立一个高度可用的比较基因组分析软件原型，将免费分发给学术界或为了提高基因组区域的注释准确性；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，并培养新一代计算生物学研究人员，以获得对生物过程和复杂计算方法的更深入了解。

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.