Phylogenetic and computational methods for accurate and efficient analyses of large-scale metagenomics datasets

用于准确有效分析大规模宏基因组数据集的系统发育和计算方法

• 批准号: 10542443
• 负责人: Lenore Pipes
• 金额: $ 10.8万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10542443
• 关键词: 2019-nCoV; Algorithms; Area; Bayesian Analysis; Bayesian Method; Biodiversity; Bioinformatics; Botany; COVID-19; COVID-19 detection; COVID-19 monitoring; COVID-19 surveillance; Classification; Communities; Complex Mixtures; Computer software; Computing Methodologies; Coronavirus; Custom; DNA; Data; Data Set; Data Storage and Retrieval; Databases; Development; Disease; Disease Outbreaks; Early Diagnosis; Ecosystem; Environment; Environmental Monitoring; Eye; Forensic Medicine; Genetic Materials; Genome; Genomics; Genotype; Goals; Growth; Human Microbiome; Individual; Life; Location; Metagenomics; Methods; Microbe; Microscopic; Modeling; Monitor; Nucleotides; Organism; Pathogen detection; Performance; Phylogenetic Analysis; Phylogeny; Population Sizes; Postdoctoral Fellow; Procedures; Property; Public Health; Publishing; Reproducibility; Research; Research Design; Research Personnel; Research Proposals; Sampling; Soil; Speed; Systems Biology; Taxonomy; Terrorism; Time; Trees; Update; Validation; Variant; Viral; Virus; Water; Zoology; combat; computer science; expectation; experimental study; in silico; insight; microbiome analysis; next generation sequencing; novel; open source; pandemic disease; performance tests; public repository; simulation; statistics; surveillance data; tool; transmission process; viral detection; wastewater monitoring; wastewater samples; wastewater surveillance; web portal; whole genome

Project Summary/Abstract The overall goal of this project is to use approaches from statistics and computer science to solve significant chal- lenges in the analysis of metabarcode and metagenomics data. Metagenomics, the study of combined genomes of organisms present in a single community, is an emerging highly interdisciplinary field that combines genomics, bioinformatics, systems biology, among other areas. Metagenomics has many applications to public health es- pecially in the areas of pathogen detection, human microbiome analysis, and biodiversity monitoring. The larger objective of this proposal is to leverage the use of the open source software, tronko, a fast approximate likelihood phylogenetic placement method that I developed for taxonomic classification, which is the first phylogenetic place- ment method that truly enables the use of large-scale reference databases and next generation sequencing data desired as queries. Tronko will be used to solve fundamental problems in analyses of metabarcode and metage- nomic data in addition to developing an application to analyses of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequences that will greatly enhance the utility of environmental monitoring of SARS-CoV-2. The specific aims of this proposal are to (1) solve an important theoretical problem by applying a rigorous species delineation to assignment, (2) to apply tronko to solve an important practical problem of estimating the compo- sition of SARS-CoV-2 lineages in wastewater surveillance samples, and (3) to develop a rapid custom reference database builder for analyzing metabarcode and metagenomics data. For Aim 1, different phylogenetic groups have different variability in different parts of the tree, therefore, I plan to use Bayesian methods to estimate effec- tive population sizes locally to establish appropriate cut-off thresholds for species assignments in different parts of the phylogeny. Current methods use arbitrary thresholds for delineation of taxonomic groups and this method would provide an elegant solution to a long-standing limitation in species classification. For Aim 2, SARS-CoV-2 monitoring of wastewater is an effective strategy for early detection of outbreaks. I plan to build a pipeline, and subsequently a web portal for researchers, that uses tronko to first detect the virus within a wastewater sample then subsequently uses an expectation-maximization algorithm to estimate the proportions of viral strains. This aim would greatly aid public health researchers in assessing and managing the pandemic since no established methods are currently available for this type of analysis. For Aim 3, current custom reference database builders require weeks if not months of consecutive computational time in addition to access to a large amount of data storage. I propose to build a method which can be completed within a day. The method will perform in silico amplification of primers and subsequently use the amplified fragments in a kmer-based approach for identifying relevant sequences within a nucleotide database with utilization both across a network connection and a local database. Execution of these aims will solve important theoretical, practical, and computational problems in the field of metagenomics.

项目摘要/摘要 该项目的总体目标是使用统计和计算机科学的方法来解决重要的chal- 分析Metagabarcode和Metagenomics数据的长度。宏基因组学，联合基因组的研究 在一个社区中存在的生物是一个新兴的高度跨学科领域，结合了基因组学， 生物信息学，系统生物学等。宏基因组学在公共卫生中有许多应用 部分是在病原体检测，人类微生物组分析和生物多样性监测的领域。较大 该建议的目的是利用开源软件Tronko的使用，很可能 我为分类学分类而开发的系统发育方法，这是第一个系统发育的地方 - 真正能够使用大规模参考数据库和下一代测序数据的方法方法 希望作为查询。 Tronko将用于解决元法和元模型分析中的基本问题 - 除了开发用于分析严重急性呼吸综合征冠状病毒的应用程序之外 2（SARS-COV-2）序列将大大增强SARS-COV-2环境监测的效用。这 该提案的特定目的是通过应用严格的物种来解决重要的理论问题 分配的描述，（2）应用Tronko来解决一个重要的实际问题，以估算组合 在废水监视样品中的SARS-COV-2谱系中的地点，以及（3）开发快速自定义参考 数据库构建器，用于分析元码和元基因组学数据。对于AIM 1，不同的系统发育组 因此，我计划使用贝叶斯方法来估计效率 - 本地人口规模以建立不同部分的物种分配的适当截止阈值 系统发育。当前方法使用任意阈值来描述分类群，此方法 将为物种分类的长期限制提供优雅的解决方案。对于AIM 2，SARS-COV-2 对废水的监测是早期发现暴发的有效策略。我打算建造一条管道，并 随后，使用Tronko的研究人员的Web门户网站首次检测在废水样品中的病毒 然后随后使用预期最大化算法来估计病毒菌株的比例。这 AIM将极大地帮助公共卫生研究人员评估和管理大流行，因为没有建立 目前可用于此类分析。对于AIM 3，当前自定义参考数据库构建器 除了访问大量数据外，还需要数周的连续计算时间（如果不是数月的连续计算时间） 贮存。我建议建立一种可以在一天内完成的方法。该方法将在计算机中执行 底漆的扩增，然后在基于kmer的方法中使用放大片段来识别 核苷数据库中的相关序列在网络连接和局部都具有利用率 数据库。这些目标的执行将解决重要的理论，实用和计算问题 宏基因组学领域。