Statistical methods to enhance reproducible microbiome discovery

项目摘要 微生物组在人类健康和疾病中起着重要作用， 使用高通量基因组测序进行表征。然而，实验室处理 微生物宏基因组测序所需的测量噪声可能会引入虚假的测量噪声， 例如，DNA提取、扩增、测序深度、GC偏差、批次效应， 实验室方案和生物信息学处理。如果不进行校正， 样本和研究特定的变异很容易超过变异的幅度， 治疗或疾病状态。因此，疾病和感染的诊断和治疗 基于微生物测序的方法受到掩盖真实生物信号的假噪声的阻碍。 本研究的总体目标是开发新的统计方法， 微生物组数据，包括分类、功能和代谢数据。我们的统计模型 明确模型批次和技术变化，使我们能够区分，而不是混为一谈， 生物信号和非生物噪声。我们的新模型将利用常见的 序列数据，如阳性对照和技术重复，通常不使用 研究人员对微生物组数据进行了统计分析。通过设计统计方法 使用现有数据源，我们将减少测序所需的数量和成本， 检测真实的生物信号我们的模型将允许我们执行假设检验， 微生物基因、菌株和代谢物的丰度差异，以及 微生物群落的多样性，而不会丢弃生物信号或检测到虚假的 由于不完善的实验室协议和仪器造成的技术噪音。所述方法 适用于广泛的实验设计（包括观察和纵向）， 生物医学研究方法（包括模型系统和临床试验）和测序 平台（包括标记基因和全基因组测序以及光谱分析） 代谢和蛋白质组学分析方法）。我们的统计方法将分布为 免费提供的开源软件，其中将包括广泛的教程和论坛， 用户提问。通过避免检测信号由于样品-和研究-！特定的人工制品， 我们的方法将提高微生物组研究的可重复性， 确定微生物组科学中的治疗和诊断机会。

PROJECT SUMMARY The microbiome, which plays an important role in human health and disease, is generally characterized using high throughput genome sequencing. However, the laboratory processes required for microbial metagenomic sequencing can introduce spurious measurement noise due to, for example, DNA extraction, amplification, sequencing depth, GC bias, batch effects, laboratory protocols, and bioinformatics processing. Without correction, the magnitude of sample- and study- specific variation can easily exceed the magnitude of variation due to treatment or disease status. Therefore, diagnosis and treatment of diseases and infections based on microbial sequencing is impeded by spurious noise that masks true biological signal. The overall goals of this research are to develop new statistical methods for the analysis of microbiome data, including taxonomic, functional, and metabolic data. Our statistical models will explicitly model batch and technical variation, allowing us to distinguish, rather than conflate, biological signal and non-biological noise. Our new models will leverage commonly-collected sequence data, such as positive controls and technical replicates, which are not typically utilized by researchers in their statistical analysis of microbiome data. By designing statistical methods that use existing data sources, we will reduce the amount and cost of sequencing required to detect true biological signals. Our models will allow us to perform hypothesis testing for differential abundance of microbial genes, strains, and metabolites, as well as shifts in the diversity of microbial communities, without discarding biological signal or detecting spurious technical noise due to imperfect laboratory protocols and instrumentation. The methods are applicable to a broad range of experimental designs (including observational and longitudinal), biomedical research methods (including model systems and clinical trials), and sequencing platforms (including marker gene and whole genome sequencing as well as spectrometric methods for metabolic and proteomic profiling). Our statistical methods will be distributed as freely available, open-source software, which will include extensive tutorials, and forums for user questions. By avoiding detection of signals due to sample- and study-!specific artefacts, our methods will increase the reproducibility of microbiome research, and facilitate the identification of therapeutic and diagnostic opportunities in microbiome science.