Phylogenetic Binning of Metagenomic Sequence Data

描述(由申请人提供)：独立于培养的元基因组研究对于了解我们与组成人类微生物组的生物体的关系，确定维持健康的最佳微生物组成，以及设计选择性治疗策略以消除病原体而不损害有益物种至关重要。为了有效地利用元基因组数据，必须对原始DNA序列数据(READ)进行计算处理(组装)以获得更长的序列(重叠群)。现有的用于这一目的的软件包在提供大的、分类上不同的样本时效率相当低，导致无法组装的读数的相当大的浪费。通过放松严格来最大化组装效率的努力可能会导致来自不相关生物体(嵌合人工制品)的序列不适当地连接，从而损害数据的准确性和有用性。作为预过滤步骤，对原始片段进行分类分库有望提高元基因组序列组装效率，减少由于样本复杂性造成的统计噪声，并允许将原始片段整合到更长、更有信息的重叠群中，而不会产生嵌合人工产物。对于复杂混合物中不太丰富的物种来说，好处应该特别显著。我们开发了在真实元基因组数据集中量化分类入库程序性能和汇编改进的方法，包括可重现的校准标准，以实现对现有软件的有效参数优化，并为未来的软件开发提供可靠的基准。我们的具体目标是1)开发适用于原始阅读和组装重叠群的大规模元基因组序列数据分类的新计算方法；2)开发软件和协议，将分类数据入库作为预处理，以提高现有序列组装软件的效率；3)通过使用人工创建的模型和不同大小和复杂性的真实元基因组数据集进行定量、统计测试，提高不同组装软件程序的基准性能；4)使新的计算方法和性能评估工具可供一般科学界使用。

DESCRIPTION (provided by applicant): Culture-independent metagenomic studies are essential for understanding our relationship with the organisms comprising the human microbiome, defining optimal microbial composition to maintain health, and devising selective treatment strategies to eliminate pathogens without harming beneficial species. To use metagenomic data effectively, raw DNA sequence data (reads) must be processed computationally (assembled) to obtain longer sequences (contigs). Existing software packages for this purpose are quite inefficient when presented with large, taxonomically diverse samples, resulting in considerable wastage of reads that cannot be assembled. Efforts to maximize assembly efficiency by relaxing stringency can lead to inappropriate joining of sequences from unrelated organisms (chimeric artifacts), compromising data accuracy and usefulness. Taxonomic binning of raw reads as a pre-filtering step is expected to improve metagenomic sequence assembly efficiency, reducing statistical noise due to sample complexity and allowing incorporation of raw reads into longer, more informative contigs without incurring chimeric artifacts. Benefits should be especially significant for less abundant species in complex mixtures. We have developed methods to quantify taxonomic binning program performance and assembly improvements in real metagenomic data sets, including reproducible calibration standards, to enable efficient parameter optimization for existing software and provide reliable benchmarks for future software development. Our specific aims are to 1) develop new computational methods for large-scale taxonomic classification of metagenomic sequence data, applicable to raw reads as well as assembled contigs; 2) develop software and protocols to use taxonomic data binning as a pre-treatment to increase efficiency of existing sequence assembly software; 3) benchmark performance enhancement for different assembly software programs using quantitative, statistical tests with both artificially created models and real-life metagenomic data sets of varying size and complexity; 4) make new computational methods and performance evaluation tools available to the general scientific community.