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LIKELIHOOD ALIGNMENT AND EVOLUTIONARY MODELS

LIKELIHOOD ALIGNMENT AND EVOLUTIONARY MODELS
可能性对齐和进化模型
批准号:
3308813
负责人:
Jeffrey L. Thorne
金额:
$3.4万
依托单位国家:
美国
项目类别:
财政年份:
1993
资助国家:
美国
项目状态:
已结题
起止时间:
1993-09-01 至 1995-08-31

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中文摘要
翻译
序列是一种关于进化对应关系的假说 DNA或蛋白质序列。序列比对是研究生物多样性的关键。 分子进化。此外,它们还可用于高度检测 保守的功能结构域,是某些策略的组成部分 搜索序列数据库。广泛使用的对齐技术依赖于 既未陈述又不清楚的进化假设。即席 这些技术的基础使人们对它们产生的比对产生怀疑。 拟议的研究将涉及对齐方法的开发。 基于显式进化模型的推论。特价 将注意使这些模型尽可能切合实际。 核苷酸组成的区域异质性, 进化速率,以及相对一般的长度分布 将允许插入-删除事件。 进化模型将提供一个潜在的框架 应用包括:蛋白质二级结构的推断, 进化参数的估计和确定 路线中的可靠区域。此外,还提供了一种同时 将介绍多个序列的比对和系统发育重建。 从这项研究中产生的序列分析技术将 有一个可能性的基础,并可以利用这样一个事实 对统计推断的方法进行了深入研究。这个 这些技术的进化观和概率性质 应该结合在一起,使它们比之前提出的更准确。
英文摘要
An alignment is a hypothesis about the evolutionary correspondence between DNA or protein sequences. Sequence alignments are crucial for the study of molecular evolution. In addition, they can be used to detect highly conserved functional domains and are a component of some strategies for searching sequence databases. Widely-used alignment techniques rely on evolutionary assumptions that are both unstated and unclear. The ad hoc basis of these techniques casts doubt on the alignments that they produce. The proposed research will involve development of methods for alignment inference that are based upon explicit evolutionary models. Special attention will be paid to making these models as realIstic as possible. Regional heterogeneity of nucleotide composition, heterogeneity of evolutionary rates, and relatively general distributions for lengths of insertion-deletion events will be allowed. The evolutionary models will provide an underlying framework for applications that include: the inference of protein secondary structure, the estimation of evolutionary parameters, and the determination of reliable regions in alignments. In addition, a method to simultaneously align multiple sequences and reconstruct phylogenies will be introduced. The sequence analysis techniques that arise from this research will all have a likelihood basis and can take advantage of the fact that likelihood approaches to statistical inference have been intensively studied. The evolutionary perspective and probabilistic nature of these techniques should combine to make them more accurate than those previously proposed.
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Evolutionary inferences from protein-coding genes
Evolutionary inferences from protein-coding genes
Evolutionary inferences from protein-coding genes
Evolutionary inferences from protein-coding genes
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