Phylogenetic Binning of Metagenomic Sequence Data

宏基因组序列数据的系统发育分箱

• 批准号: 7919281
• 负责人: Eric Ellsworth Allen
• 金额: $ 19.31万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-24 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7919281
• 关键词: Affect; Algorithms; Archives; Benchmarking; Calibration; Classification; Communities; Complex Mixtures; Computer software; Computing Methodologies; DNA; DNA Sequence; Data; Data Compromising; Data Set; Evaluation; Future; Genome; Genomics; Goals; Health; Horizontal Gene Transfer; Human; Human Microbiome; Individual; Knowledge; Lead; Length; Life; Metabolic; Metagenomics; Methods; Metric; Modeling; Morphologic artifacts; Noise; Organism; Performance; Phylogenetic Analysis; Procedures; Process; Protocols documentation; Public Health; Publishing; Reading; Recovery; Reproducibility; Research; Sampling; Sensitivity and Specificity; Sequence Analysis; Techniques; Testing; Time; Validation; Work; base; computerized tools; gene function; improved; metagenomic sequencing; microbial; new technology; novel; pathogen; programs; software development; tool; treatment strategy

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.

描述（由申请人提供）：与文化无关的宏基因组研究对于理解我们与包括人类微生物组的生物的关系至关重要，定义了保持健康的最佳微生物组成，并制定选择性治疗策略以消除病原体而不损害有益物种。为了有效地使用宏基因组数据，必须对原始DNA序列数据（读取）进行计算处理（组装）以获得更长的序列（重叠群）。出于此目的的现有软件包效率很大，当时有大型的分类学样本，从而大大浪费了无法组装的读数。通过放松严格性来最大程度地提高组装效率的努力会导致不适当地连接来自无关的生物（嵌合伪影）的序列，从而损害了数据的准确性和实用性。原始读取作为预滤波步骤的分类学构造有望提高宏基因组序列组装效率，从而降低了由于样品复杂性而引起的统计噪声，并允许将原始读取纳入较长，更有信息的重叠群中，而不会引起嵌合式伪影。对于复杂混合物中的丰富物种，收益应特别重要。我们已经开发了量化分类学分类计划性能和实际元基因组数据集的组装改进的方法，包括可再现的校准标准，以实现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。我们的具体目的是1）开发新的计算方法，用于用于元基因组序列数据的大规模分类分类，适用于原始读取和组装重叠群； 2）开发软件和协议，以使用分类学数据将其作为预处理，以提高现有序列组装软件的效率； 3）使用人工创建的模型和现实生活中的元基因组数据集的不同大小和复杂性的实数统计测试对不同的组装软件程序进行基准性能增强； 4）制作新的计算方法和绩效评估工具可供一般科学界使用。