Statistical methods to enhance reproducible microbiome discovery

项目总结 微生物群在人类健康和疾病中起着重要的作用，通常 以高通量基因组测序为特征。然而，实验室的过程 微生物元基因组测序所需的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。如果不进行修正，则 样本和研究的特定变异很容易超过变异的幅度，这是由于 治疗或疾病状况。因此，疾病和感染的诊断和治疗 基于微生物的测序受到掩盖真实生物信号的虚假噪声的阻碍。 这项研究的总体目标是开发新的统计方法来分析 微生物组数据，包括分类、功能和代谢数据。我们的统计模型将 明确地建模批次和技术变化，使我们能够区分而不是合并， 生物信号和非生物噪声。我们的新型号将利用共同收集的 序列数据，如阳性对照和技术复制，通常不会使用 由研究人员对微生物组数据进行统计分析。通过设计统计方法 使用现有数据源，我们将减少所需的排序数量和成本 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、菌株和代谢物的差异丰度，以及 微生物群落的多样性，不丢弃生物信号或检测到虚假 由于实验室规程和仪器不完善而产生的技术噪音。这些方法是 适用于广泛的实验设计(包括观察性和纵向)， 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发为 免费提供的开源软件，其中将包括广泛的教程和论坛 用户问题。通过避免由于采样和研究特定伪影而检测信号， 我们的方法将增加微生物组研究的重复性，并促进 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.