AToL: Collaborative Research: A Phylogenomic Toolbox for Assembling the Tree of Life Molecular Sequence Databases

爱荷华州立大学获得了一笔拨款，用于开发新的方法和软件工具，以帮助构建所有生物物种的谱系“生命之树”。这些努力的重点将是从庞大的分子序列数据库中提取信息，以建立更小的树的集合，这些树可以组装成更大、更全面的生命之树的图片。数据输入的规模很大：例如，基因库包含了从10万多个物种中采样的数千万个序列。然而，广泛的研究集中在从单个数据集构建树的问题上；相对而言，人们对从序列数据库中大量提取这些数据集然后组装合成知之甚少。我们的建议是研究一组新的计算问题，这些问题与基本的树构建问题本身一样具有挑战性。这主要发生在三个方面：(1)评估各种序列数据库中的潜在信息；(2)从数据库中最优提取数据，为单株树重建提供最优信息；(3)将这些较小的树整合为“超级树”（由具有共同物种的较小树组装而成的较大树），特别是通过识别构建最佳超级树所需的新序列的目标集。理论结果将通过分析三个不同的数据库来评估，这些数据库提出了一系列计算挑战（GenBank、SWISS-PROT和TIGR EGO数据库的一个子集）。这项工作将描述这些序列集的系统发育信息内容，识别可组合序列信息的最大集，构建数据库的非冗余分区以允许估计树的集合，并从这些集合中组装超树。跨学科团队包括系统发育生物学家和计算机科学家，他们在系统发育理论、数据分析、算法开发和实现方面具有丰富的经验。该项目还与三个现有的生命之树项目合作（每个项目都旨在重建生命之树的特定部分），以提供序列定位算法的测试。

Iowa State University has been awarded a grant to develop new methods and software tools to help construct the genealogical "tree of life" of all biological species. The focus of these efforts will be on extracting information from the vast molecular sequence databases to build collections of smaller trees that can be assembled into larger, more comprehensive pictures of the tree of life. The scale of the data input is large: GenBank, for example, contains tens of millions of sequences sampled from over 100,000 species. Whereas extensive research has focused on the problem of building a tree from a single data set; relatively little is known about extracting these data sets en masse from sequence databases and then assembling a synthesis. The proposal is to study a set of novel computational problems that are as challenging as the basic tree building problem itself. These occur in three broad areas: (1) assessment of the potential information in sequence databases of various kinds; (2) optimal extraction of data from databases to bring the best information to bear on individual tree reconstruction; and (3) integration of these smaller trees into "supertrees" (larger trees assembled from smaller ones that share species in common), especially by identifying target sets of new sequences needed to construct optimal supertrees. Theoretical results will be evaluated by analysis of three diverse databases that pose a range of computational challenges (a subset of GenBank, SWISS-PROT, and the TIGR EGO database). This work will characterize the phylogenetic information content of these sequence sets, identify maximal sets of combinable sequence information, construct nonredundant partitions of the database to permit estimation of collections of trees, and assemble supertrees from these collections. The interdisciplinary team includes phylogenetic biologists and computer scientists with experience in phylogenetic theory, data analysis, and algorithm development and implementation. The project is also collaborating with three existing Tree-of-Life projects (each aimed at reconstructing particular portions of the tree) to provide tests of the sequence targeting algorithms.