MOLECULAR SEQUENCE DATA

描述(改编自《调查者摘要》)：收藏 分子序列数据的研究正在以前所未有的速度进行。 几个完整的基因组，数以万计的蛋白质和几个 现在有数百种不同的核酸和蛋白质结构可用。 分子生物学的下一阶段将越来越多地由 努力对这些数据进行表征、分类和分析，以期 理解生物学中的信息内容和传递 以分子为基础的系统。这项提议的目的是促进 更深入地了解基因组的结构、功能和进化 经验性、描述性和互动性统计和计算 方法：研究方法。重点放在五个主要领域：一、发展 基因组分析的统计理论和算法。有几个 方法以前关于统计评分方法的工作， R-扫描过程，字数将显著延长， 尤其是关于基因组不均质性的分析。二、 成分偏差、基因组签名和进化关系。 研究人员将继续评估全基因组的差异 以及以二核苷酸为中心的物种内部和物种之间的相似性 相对丰度作为基因组的标志。三、密码子和残基的使用 模式。关于特殊密码子和残基选择的详细知识 可以帮助预测基因，描述给定的属性 基因，以及对基因家族的分类。与 上一个主题中，探索编码使用约束的新方法是 提出了对进化、DNA结构和载体的影响 设计。对比外显子和内含子的序列模式； 外显子和基因的预测。水稻中剪接基因的鉴定 真核基因组dna是人类面临的最紧迫的挑战之一。 来自大规模测序项目。调查人员提出了新的 鉴定外显子、内含子和蛋白质序列特征的方法。 五、核酸和蛋白质分析软件的进展 序列。调查人员将继续开发多功能 实现所有计算和统计方法的代码 用于序列分析。

DESCRIPTION (Adapted from the Investigator's Abstract): The collection of molecular sequence data is proceeding at an unprecedented pace. Several 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 the information content and transfer in biological systems on a molecular basis. This proposal is aimed at contributing to a deeper understanding of genome structure, function and evolution using empirical, descriptive and interactive statistical and computational methods. The focus is on five primary areas: I. Development of statistical theory and algorithms for genome analysis. There are several ways in which the previous work on the statistics of scoring methods, r-scan processes, and word counts will be significantly extended, particularly with respect to analysis of genome inhomogeneities. II. Compositional biases, genome signature, and evolutionary relationships. The investigators will continue the evaluation of genome-wide differences and similarities within and between species centered on the dinucleotide relative abundances as a genome signature. III. Codon and residue usage patterns. Detailed knowledge of distinctive 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 topic, new ways of probing constraints on coding usage is proposed, with implications for evolution, DNA structure, and vector design. IV. Contrasts in sequence patterns in exons versus introns; prediction of exons and genes. Identification of spliced genes in eukaryotic genomic DNA is one of the most pressing challenges arising from large-scale sequencing projects. The investigators propose new methods for characterizing exon, intron and protein sequence features. V. Advances in software for analysis of nucleic acid and protein sequences. The investigators will continue the development of versatile code that implements all of the computational and statistical methods for sequence analysis.