MOLECULAR SEQUENCE DATA

描述（改编自研究者摘要）：收集 正在以前所未有的速度进行。 几个完整的基因组，成千上万的蛋白质，和几个 现在可获得数百种不同核酸和蛋白质结构。 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， 了解生物学中的信息内容和传递 分子基础上的系统。 该提案旨在促进 更深入地了解基因组结构，功能和进化， 经验性、描述性和交互式统计和计算 方法. 重点是五个主要领域：一。发展 基因组分析的统计理论和算法。 有几 其中以往工作中关于统计评分的方法， r扫描过程，字数将大大增加， 特别是关于基因组不均一性的分析。二. 组成偏差，基因组签名，和进化关系。 研究人员将继续评估全基因组差异 以及物种内部和物种之间的相似性， 相对丰度作为基因组标记。 三.密码子和残基使用 模式. 详细了解独特的密码子和残基选择 可以帮助基因预测，表征给定的 基因，并对基因家族进行分类。 联同 上一个主题中，探测编码使用约束的新方法是 提出，与进化，DNA结构和载体的影响， 设计 四.外显子与内含子序列模式的对比; 外显子和基因的预测。 剪接基因的鉴定 真核生物基因组DNA是目前最紧迫的挑战之一， 从大规模测序项目。 研究人员提出新的 用于表征外显子、内含子和蛋白质序列特征的方法。 诉核酸和蛋白质分析软件的研究进展 序列的 研究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.