Classification of HIV-1 Using Coalescent Theory

使用合并理论对 HIV-1 进行分类

• 批准号: 78025979
• 负责人: Professor Dr. Mario Stanke
• 金额: --
• 依托单位: Lehrstuhl für Bioinformatik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2008
• 资助国家: 德国
• 起止时间: 2007-12-31 至 2010-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/78025979?language=en
• 关键词: Classification; HIV; Using; Coalescent; Theory

The aim of the project is to develop a method for automatically classifying HIV-1. The main part of this method consists in a scoring algorithm which rates a given classification. This algorithm reconstructs ancestral recombination graphs (ARG) of given HIV-1 sequences under restrictions determined by the given classification and finds an ARG with maximal probability by means of Markov Chain Monte Carlo methods. The probability of the most probable ARG is interpreted as a score for the classification. Afterwards, the classification is iteratively improved with respect to the score. In a preparing step, we will improve the jpHMM algorithm, a tool developed by our group to localize recombination in viral genomes, with respect to runtime and precision. Finally, we will systematically apply our method to re-annotate the HIV-1 genome database at Los Alamos National Laboratory (LANL). Development, evaluation and application of classification algorithm will be carried out in close collaboration with Dr. B. Korber and Dr. T. Leitner at LANL, two leading experts on HIV sequence analysis and evolution, and with Prof. D. Brown, University of Waterloo, Canada, a well-known expert on Hidden Markov Models.

该项目的目的是开发一种自动分类HIV-1的方法。该方法的主要部分在于评分算法，该算法对给定的分类进行评分。该算法在给定的分类条件下，重构给定HIV-1序列的祖先重组图（ARG），并利用马尔可夫链蒙特卡罗方法找到一个具有最大概率的ARG。最可能的ARG的概率被解释为分类的分数。然后，相对于分数迭代地改进分类。在准备步骤中，我们将改进jpHMM算法，这是我们小组开发的一种工具，用于在病毒基因组中定位重组，在运行时间和精度方面。最后，我们将系统地应用我们的方法重新注释洛斯阿拉莫斯国家实验室（LANL）的HIV-1基因组数据库。分类算法的开发、评估和应用将与B博士密切合作进行。科伯和T博士莱特纳教授，两位艾滋病毒序列分析和进化方面的顶尖专家，以及D。布朗，加拿大滑铁卢大学，著名的隐马尔可夫模型专家。