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ITR/AP(DEB): Collaborative Research `Computing Optimal Phylogenetic Trees under Genome Rearrangement Metrics'

ITR/AP(DEB): Collaborative Research `Computing Optimal Phylogenetic Trees under Genome Rearrangement Metrics'
ITR/AP(DEB):合作研究“在基因组重排指标下计算最佳系统发育树”
批准号:
0113654
负责人:
Robert Jansen
金额:
$28.8万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2001
资助国家:
美国
项目状态:
已结题
起止时间:
2001-10-01 至 2005-09-30

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ABSTRACTPI Jansen - #0113654PI Moret - #0113095This award provides support for a collaborative project between two biologists (Jansen and Boore) and three computer scientists (Moret, Warnow, and Bader) to develop and test new algorithms and software for constructing evolutionary trees (phylogenies) from the rapidly increasing amount of genomic data. The estimation of phylogenies is crucial to a wide range of basic and applied biological problems such as the epidemiology of AIDS, the identification of viral agents, the analysis of protein structure and function, the prediction of RNA structure; and, of course, it is the basic tool for inferring evolutionary history. Methods for the reconstruction of phylogenies are most commonly applied to DNA sequences. In many cases these sequences change at rates that are too low or too high for recovering accurate evolutionary trees. One new type data that is fast gaining favor in the biology community is gene order and content within whole genomes, but so far very few computational methods have been developed for these types of data. The three goals of the project are to: (1) develop algorithms and software for reconstructing evolutionary trees from gene order and content data; (2) assess the performance of these algorithms through extensive simulation studies under parameter-rich models of genome evolution and through application to real datasets; and (3) develop high-performance implementations of the best algorithms designed in the project using algorithm engineering techniques and a flexible approach to parallelization. This project will make significant contributions in biology, through the development of new models of genome evolution and an improved understanding of the evolution of chloroplast and mitochondrial genomes, and in computer science, through the development and testing of new algorithms for genomic data and the application of high-performance computing to problems in phylogenetic analysis.
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