Enriching SARS-CoV-2 sequence data in public repositories with information extracted from full text articles

利用从全文文章中提取的信息丰富公共存储库中的 SARS-CoV-2 序列数据

• 批准号: 10701081
• 负责人: GRACIELA GONZALEZ HERNANDEZ
• 金额: $ 58.37万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-17 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10701081
• 关键词: 2019-nCoV; Address; Age; Agreement; Algorithms; Base Sequence; COVID-19; COVID-19 pandemic; Clinical; Clinical Data; Collaborations; Communicable Diseases; Coronavirus; Data; Data Analyses; Data Set; Epidemiology; Evolution; Funding; Genbank; Gender; Genetic; Genotype; Geography; Goals; Health; International; Intervention; Joints; Journals; Knowledge; Link; Location; Manuals; Metadata; Methods; Modeling; Natural Language Processing; Ontology; Outcome; Patient Care; Patients; Peer Review; Performance; Phylogenetic Analysis; Population; Population Group; Populations at Risk; Printing; Probability; Public Health; Publications; Publishing; Race; Records; Relative Risks; Reporting; Research; Research Personnel; Resolution; Resources; Risk; SARS coronavirus; Scientist; Sequence Analysis; Severities; Specific qualifier value; System; Testing; Text; Unified Medical Language System; United States National Institutes of Health; Update; Viral; Viral Genome; Virus; Work; clinical phenotype; cohort; comorbidity; coronavirus disease; dashboard; data sharing; deep learning; demographics; field study; genomic epidemiology; heuristics; improved; insight; novel; pandemic disease; population health; prevent; public database; public repository; residence; response; secondary analysis; sex; text searching; transmission process; trend; virus characteristic

Project Summary In response to the COVID-19 pandemic, scientists have published over one hundred thousand research articles and made available over eight hundred thousand virus genome sequences. These sequences, along with their metadata, can be used to understand virus evolution and spread and their implications for public health, a field of study called genomic epidemiology. However, these sequence records do not typically contain patient metadata such as demographics, clinical severity, or comorbidities, preventing researchers from uncovering trends in population health. To understand the severity of the problem, we analyzed nearly 748 thousand SARS-CoV-2 records from GISAID and 60 thousand from GenBank for the presence of patient metadata finding age and gender were represented in < 1% of GenBank records and in GISAID, 26% included sex, and 24% had age. For other fields, the amount of missing data is even more pronounced, with neither resource providing information on a patient's race and only GISAID specifying severity (i.e. ICU) in less than 5% of records. To address missing virus metadata, researchers could utilize the publication associated with the new sequences, however, the virus sequence record is often never updated with a link to the publication. From the set of records that we analyzed, 3.4% (of 748K) in GISAID and < 1% (of 117K) in GenBank had a link to a publication. This greatly hinders secondary data analysis of these sequences and limits the ability to use them at scale to uncover associations between the viral genome, transmission risk, and health outcomes. The goal of this proposal is to enhance genomic epidemiology and population health of COVID-19 with a framework to continuously and automatically enrich SARS-CoV-2 nucleic acid sequence metadata in public databases such as GenBank and GISAID with metadata in associated published articles. We will incorporate input from clinicians at the front-line of patient care during the pandemic and build on our NIH funded work (R01AI117011), which used Natural Language Processing (NLP) to enrich the geographic metadata of a sequence record using its corresponding published article. We have used these data in virus phylogeographic models and shown the benefit of using enriched metadata for modeling virus evolution and spread. Theavailability of SARS-CoV-2 sequences, paired withfull- text COVID-19 articles and preprints, presents an opportunity for metadata enrichment and scientific discovery beyond our prior work. Our specific aims are to: (1) enrich SARS-CoV-2 sequence metadata using text extracted from publications and (2) derive key epidemiologic insights for different patient demographics using our enriched SARS-CoV-2 sequence dataset. We will leverage our prior joint work funded by the NIH to enable the secondary use of enriched metadata for genomic epidemiology to improve our understanding of SARS-CoV-2 evolution and spread among different population groups. We will disseminate the enriched data through our GeoBoost2 data dashboard, GenBank LinkOut and the i2b2 platform. The latter will more immediately allow integration with COVID-specific clinical data shared by the 4CE Consortium.

项目概要 为应对 COVID-19 大流行，科学家发表了超过十万篇研究文章 并提供了超过八十万个病毒基因组序列。这些序列以及它们的 元数据可用于了解病毒的进化和传播及其对公共卫生的影响，这是一个领域 研究称为基因组流行病学。然而，这些序列记录通常不包含患者元数据 例如人口统计、临床严重程度或合并症，阻止研究人员发现趋势 人口健康。为了了解问题的严重性，我们分析了近 74.8 万个 SARS-CoV-2 来自 GISAID 的记录和来自 GenBank 的 6 万条记录，用于查找年龄和年龄的患者元数据的存在 GenBank 记录中的性别比例不到 1%，而在 GISAID 中，26% 包含性别，24% 包含年龄。为了 在其他领域，缺失的数据量甚至更加明显，两种资源都没有提供相关信息 患者的种族，并且仅 GISAID 在不到 5% 的记录中指定了严重程度（即 ICU）。解决失踪问题 病毒元数据，研究人员可以利用与新序列相关的出版物，但是，该病毒 序列记录通常不会通过出版物的链接进行更新。从我们分析的一组记录来看， GISAID 中的 3.4%（共 748K）和 GenBank 中的 < 1%（共 117K）有出版物链接。这极大地阻碍了 对这些序列进行二次数据分析并限制了大规模使用它们来揭示关联的能力 病毒基因组、传播风险和健康结果之间的关系。该提案的目标是增强 COVID-19 的基因组流行病学和人口健康，具有连续、自动的框架 丰富公共数据库（例如 GenBank 和 GISAID）中的 SARS-CoV-2 核酸序列元数据 相关已发表文章中的元数据。我们将吸收患者前线临床医生的意见 大流行期间的护理，并以我们 NIH 资助的工作 (R01AI117011) 为基础，该工作使用自然语言 处理（NLP），使用其相应的已发布序列来丰富序列记录的地理元数据 文章。我们在病毒系统发育地理学模型中使用了这些数据，并展示了使用丰富的 用于建模病毒进化和传播的元数据。 SARS-CoV-2 序列的可用性，与全配对 文本 COVID-19 文章和预印本，为元数据丰富和科学发现提供了机会 超出了我们之前的工作。我们的具体目标是：(1) 使用提取的文本丰富 SARS-CoV-2 序列元数据 从出版物中获取；（2）利用我们丰富的数据，得出不同患者人口统计数据的关键流行病学见解 SARS-CoV-2 序列数据集。我们将利用我们之前由 NIH 资助的联合工作来实现二级 使用丰富的基因组流行病学元数据来提高我们对 SARS-CoV-2 进化的理解 在不同人群中传播。我们将通过 GeoBoost2 数据传播丰富的数据 仪表板、GenBank LinkOut 和 i2b2 平台。后者将更立即地允许与 4CE 联盟共享的新冠肺炎特定临床数据。