COMPARATIVE GENOMIC AND EVOLUTIONARY STUDIES

分子序列数据的积累正在以 前所未有的步伐。 几十个完整的基因组， 蛋白质，以及数百种不同的核酸和蛋白质 目前已有结构。 分子生物学的下一阶段将 越来越多地被描述、分类和 分析这些数据的目的是在分子基础上理解 信息的内容及其在生物系统中的传递。 我们 该提案旨在加深对基因组的了解 结构、功能和演变，使用经验性、描述性和 交互式统计和计算方法。 我们专注于四个 相互关联的主要领域。 I. 基因组签名和进化 关系。 我们将继续评估全基因组 物种内部和物种之间的差异和相似性， 二核苷酸相对丰度作为基因组标记。的应用 与基因组比较的二核苷酸相对丰度谱不 需要对齐。 二. 基因组密码子使用模式。详细 密码子和残基选择的知识可以帮助基因预测， 特征化给定基因的特性，并且在对基因进行分类时 家庭 结合上一个领域，我，我们提出新的方式， 探索密码子使用的限制， 进化、DNA结构和载体设计。 三. 成对和 蛋白质序列的多重比对。 实现多重比对 通过我们的新方法解释功能/结构 属性和演化。 这些调整将被广泛应用。 四. 基因组分析的统计方法。 我们将寻求 物种内和物种间基因组异质性的表征， 将寻求扩展，以适应r扫描的不均匀性 统计数据，评估在特定的分布异常， 沿着生物分子序列的相关标记。 我们将进一步 调查罕见和常见的单词，图案或成分偏见。 最后，我们将继续开发通用代码， 实现了我们所有的计算和统计方法的序列 分析.

The accumulation of molecular sequence data is proceeding at an unprecedented pace. Dozens of complete genomes, tens of thousands of proteins, and several hundred distinct nucleic acid and protein structures are now available. The next phase of molecular biology will be increasingly dominated by efforts to characterize, categorize, and analyze these data with the goal of understanding on a molecular basis the content of information and its transfer in biological systems. Our proposal is aimed at achieving a deeper understanding of genome structure, function, and evolution using empirical, descriptive and interactive statistical and computational methods. We focus on four interrelated primary areas. I. Genome signature and evolutionary relationships. We will continue the evaluation of genome-wide differences and similarities within and among species using the dinucleotide relative abundances as a genome signature. Applications of dinucleotide relative abundance profiles to genome comparisons do not require alignment. II. Genomic codon usage patterns. Detailed knowledge of codon and residue choices can help in gene prediction, in characterizing properties of a given gene, and in classifying gene families. In conjunction with the previous area, I, we propose new ways of probing constraints on codon usage that have implications for evolution, DNA structure, and vector design. III. Pairwise and multiple alignments of protein sequences. Multiple alignments achieved by our new methods are interpreted with respect to functional/structural properties and evolution. These alignments will be applied broadly. IV. Statistical methods for genome analysis. We will seek characterizations of genomic heterogeneity within and among species and will seek extensions that accommodate inhomogeneities for r-scan statistics that assess anomalies in the distribution of specific relevant markers along biomolecular sequences. We will further investigate rare and frequent words, motifs, or compositional biases. Finally, we will continue the development of versatile code that implements all our computational and statistical methods for sequence analysis.