Tracking Evolution and Spread of Viral Genomes by Geospatial Observation Error

通过地理空间观测误差追踪病毒基因组的进化和传播

• 批准号: 9249484
• 负责人: GRACIELA GONZALEZ HERNANDEZ
• 金额: $ 46.1万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2020-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9249484
• 关键词: Animals; Area; Award; Back; China; Computer software; County; Data; Data Sources; Databases; Deposition; Development; Diffusion; Disease; Environmental Health; Evaluation; Evolution; Funding; Genbank; Genetic Variation; Genome; Geography; Goals; Gold; Hantavirus; Health; Human; Imagery; Influenza; Knowledge; Link; Literature; Location; Manuals; Metadata; Methods; Modeling; Molecular Epidemiology; National Institute of Allergy and Infectious Disease; Natural Language Processing; Nucleotides; Population Genetics; Public Health; Publications; RNA Viruses; Rabies; Records; Research; Research Infrastructure; Research Personnel; Resources; Risk; Running; Science; Source; Surveillance Modeling; System; Time; Trees; United States National Institutes of Health; Viral; Viral Genome; Virus; Work; Zoonoses; improved; information model; interest; journal article; molecular sequence database; pathogen; population health; programs; public health relevance; simulation; surveillance data; tool; web portal

 DESCRIPTION (provided by applicant): Tracking evolutionary changes in viral genomes and their spread often requires the use of data deposited in public databases such as GenBank, the Influenza Research Database (IRD), or the Virus Pathogen Resource (ViPR). GenBank provides an abundance of available viral sequence data for phylogeography. Sequences and their metadata can be downloaded and imported into software applications that generate phylogeographic trees and models for surveillance. IRD and ViPR are NIH/NIAID funded programs that import data from GenBank but contain additional data sources, visualization, and search tools for their users. Tracking evolutionary changes and spread also requires the geospatial assignment of taxa, which is often obtained from GenBank metadata. Unfortunately, geospatial metadata such as host location is often uncertain in GenBank entries, with only 36% containing a precise location such as a county, town, or region within a state. For example, information such as China or USA was indicated instead of Beijing or Bedford, NH. While town or county might be included in the corresponding journal article, this valuable information is not available for immediate use unless it is extracted and then linked back to the appropriate sequence. The goal of our work is to enable health agencies and other researchers to automatically generate phylogeographic models that incorporate enhanced geospatial data for better estimates of virus spread. This proposal focuses on developing and applying information extraction and statistical phylogeography approaches to enhance models that track evolutionary changes in viral genomes and their spread. We propose a framework that uses natural language processing (NLP) for the automatic extraction of relevant geospatial data from the literature, and assigns a confidence between such geospatial mentions and the GenBank record. We will then use these locations and the estimates as observation error in the creation of phylogeographic models of zoonotic virus spread. We hypothesize that a combined NLP-phylogeography infrastructure that produces models that include observation error in the geospatial assignment of taxa will be closer to a gold standard than phylogeographic models that do not include them. Our research will extend phylogeography and zoonotic surveillance by: creating a NLP infrastructure that will improve the level of detail of geospatial data for phylogeography of zoonotic viruses (Aim 1), develop phylogeographic models using the estimates from Aim 1 as observation error (Aim 2), and evaluating our approach by comparing the models it produces to models that do not account for observation error in the geospatial assignment of taxa (Aim 3). We will allow users to generate enhanced models and view results on a web portal accessible via a LinkOut feature from GenBank, IRD, and ViPR. The addition of more precise geospatial information in building such models could enable health agencies to better target areas that represent the greatest public health risk.

 描述(由申请人提供)：追踪病毒基因组的进化变化及其传播通常需要使用存放在公共数据库中的数据，如GenBank、流感研究数据库(IRD)或病毒病原体资源(ViPR)。GenBank为系统地理学提供了丰富的病毒序列数据。可以下载序列及其元数据，并将其导入软件应用程序，以生成用于监测的系统地理树和模型。IRD和ViPR是由NIH/NIAID资助的项目，它们从GenBank导入数据，但包含针对其用户的其他数据源、可视化和搜索工具。跟踪进化变化和传播还需要对分类群进行地理空间分配，这通常是从GenBank元数据获得的。遗憾的是，地理空间元数据(如宿主位置)在GenBank条目中通常是不确定的，只有36%包含一个州内的县、镇或地区等精确位置。例如，显示的是中国或美国等信息，而不是北京或北卡罗来纳州贝德福德。虽然城镇或县可能包含在相应的期刊文章中，但除非将其提取并链接回适当的顺序，否则无法立即使用这些有价值的信息。我们工作的目标是使卫生机构和其他研究人员能够自动生成包含增强的地理空间数据的系统地理模型，以便更好地估计病毒的传播。这项建议侧重于开发和应用信息提取和统计系统地理方法，以增强跟踪病毒基因组及其传播的进化变化的模型。我们提出了一个框架，使用自然语言处理(NLP)从文献中自动提取相关的地理空间数据，并在这些地理空间提及和GenBank记录之间分配置信度。然后，我们将使用这些位置和估计作为观察误差来创建人畜共患病毒传播的系统地理模型。我们假设，与不包括观测误差的系统地理模型相比，一个组合的NLP-系统地理基础设施产生的模型在分类群的地理空间分配中包括观测误差，将更接近黄金标准。我们的研究将通过以下方式扩展系统地理学和人畜共患病监测：创建一个NLP基础设施，将提高人畜共患病病毒系统地理学的地理空间数据的详细程度(目标1)，使用目标1的估计作为观测误差(目标2)来开发系统地理模型，并通过将其产生的模型与不考虑分类群地理空间分配中的观测误差的模型(目标3)进行比较来评估我们的方法。我们将允许用户在可通过GenBank、IRD和ViPR的LinkOut功能访问的门户网站上生成增强的模型和查看结果。在建立这种模型时加入更精确的地理空间信息，可以使卫生机构能够更好地针对代表最大公共健康风险的地区。