Improved Analysis of Metagenomes through the application of Read-Sized Profile HMMs to Marker Gene Subsequences

通过将 Read-Size Profile HMM 应用到标记基因子序列来改进宏基因组分析

• 批准号: 9181272
• 负责人: Jeremy Selengut
• 金额: $ 22.34万
• 依托单位: UNIV OF MARYLAND, COLLEGE PARK
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-02 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9181272
• 关键词: Access to Information; Address; Amino Acid Sequence; Antibiotic Resistance; Bacteria; Bacterial Genes; Biological; Biological Process; Complex; Computer software; Data; Data Analyses; Data Set; Databases; Detection; Disease; Drug resistance; Event; Gene Family; Gene Transfer; Genes; Genome; Genomic Segment; Health; Horizontal Gene Transfer; Human; Human Microbiome; Inflammatory; Knowledge; Length; Link; Measures; Metagenomics; Methods; Modeling; Nature; Noise; Organism; Oxidative Stress; Peptide Sequence Determination; Performance; Phylogenetic Analysis; Procedures; Process; Reading; Resources; Sampling; Signal Transduction; Taxon; Testing; Toxin; Trees; Update; Validation; Work; analytical tool; base; biological adaptation to stress; gene function; genome sequencing; improved; interest; markov model; member; metagenome; microbiome; novel strategies; reference genome; resistance gene; tool; web site

Project Summary The study of the human microbiome, with its multitudes of host-associated organisms, holds great promise for increasing our understanding of human health and disease. With its fragmented sequence data unlinked from genome of origin information, the particular challenge of metagenomics is how to provide reliable functional annotation and taxonomic assignment. Here we address these issues by leveraging existing profile hidden Markov models (HMMs) of functionally characterized gene families. Instead of relying on fragment matches to full-length genes or gene models, we will determine which segments of gene models are capable of high-quality annotations of function and origin, and focus on those. By this approach, the portions of the gene models that have low sequence conservation or have variable insertion/gap length (tending towards low recall), or those that are composed of sequence shared among multiple gene families and functions (tending towards low precision) are systematically eliminated, increasing overall signal-to-noise. The high-quality segments of the models (“mini” HMMs) will be our analytical tools. Using these methods we hope to provide robust approach that frees metagenomics from the limitations of assembly-first strategies, and thereby provide access to information about the numerous low-abundance species in complex biological samples. We will use bacterial single-copy genes as taxonomic markers, and will produce a database of these genes from high-quality genomes. We expect to identify ~80 suitable marker genes, determined for several thousand genomes. For each of these genes, we will produce a corresponding reference phylogenetic tree. In the course of producing these resources, the existing models (TIGRFAMs and Pfam HMMs) will be updated based on the current set of reference genomes and a constant, state-of-the-art construction process. These resources, and any software we produce will be made available through our public website. With these methods and resources, we will obtain taxonomic profiles, investigate genes of interest and devise methods for linking those genes to the taxa in the profile. We will utilize real and synthetic metagenomes to perform validation of the methods, and establish statistical confidence metrics for our results.

项目摘要 对人类微生物组及其大量宿主相关生物的研究， 这对增进我们对人类健康和疾病的了解大有希望。与其 与基因组起源信息无关的片段化序列数据， 宏基因组学的一个特别挑战是如何提供可靠的功能， 注释和分类归属。 在这里，我们解决这些问题，利用现有的配置文件隐马尔可夫模型 （HALSTIC）的功能特征基因家族。而不是依赖于片段匹配来 全长基因或基因模型，我们将确定基因模型的哪些片段是 能够对功能和来源进行高质量的注释，并专注于这些。通过 这种方法，基因模型中具有低序列保守性的部分或具有 可变插入/间隙长度（倾向于低召回），或由以下组成的那些 多个基因家族和功能之间共享的序列（趋于低精度） 被系统地消除，从而增加了总体信噪比。高质量的细分市场， 这些模型（“迷你”障碍物）将成为我们的分析工具。我们希望通过这些方法， 一种强大的方法，将宏基因组学从组装优先策略的限制中解放出来， 从而提供获取复杂环境中众多低丰度物种信息的途径 生物样本 我们将使用细菌单拷贝基因作为分类标记，并将产生一个 这些基因的数据库从高质量的基因组。我们预计将确定~80个合适的标记 基因，决定了几千个基因组。对于这些基因中的每一个，我们将产生一个 对应的参考系统发育树。在生产这些资源的过程中， 现有的模型（TIGRFAM和Pfam拦截器）将根据当前的一套 参考基因组和一个恒定的、最先进的构建过程。这些资源，以及 我们制作的任何软件都将通过我们的公共网站提供。 有了这些方法和资源，我们将获得分类概况，研究基因， 并设计将这些基因与图谱中的分类群联系起来的方法。我们将利用 真实的和合成的宏基因组进行验证的方法，并建立统计 我们的结果的信心指标。