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

项目摘要 为应对新冠肺炎疫情，科学家发表了10多万篇研究论文 并提供了超过80万个病毒基因组序列。这些序列以及它们的 元数据，可以用来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 这项研究被称为基因组流行病学。但是，这些序列记录通常不包含患者元数据 例如人口统计学、临床严重性或合并症，使研究人员无法发现 人口健康。为了了解问题的严重性，我们分析了近74.8万例SARS-CoV-2 来自GISAID的记录和来自GenBank的6万条记录，用于发现年龄和 性别出现在1%的GenBank记录和GISAID中，26%包括性别，24%包括年龄。为 在其他字段中，丢失的数据量甚至更加明显，两个资源都没有提供关于 在不到5%的记录中，只有患者的种族和仅GISAID指定严重程度(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，然而，病毒 序列记录通常不会使用指向出版物的链接进行更新。从我们分析的一组记录来看， GISAID中3.4%(748K)和GenBank中1%(117K)有出版物链接。这极大地阻碍了 对这些序列的二次数据分析，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。这项提议的目标是加强 新冠肺炎基因组流行病学与人群健康的连续自动分析框架 丰富GenBank和GISAID等公共数据库中的SARS-CoV-2核酸序列元数据 关联已发布文章中的元数据。我们将把临床医生的意见纳入患者的一线 在大流行期间提供护理，并在NIH资助的使用自然语言的工作(R01AI117011)的基础上再接再厉 处理(NLP)，以使用其相应发布的序列记录的地理元数据来丰富其地理元数据 文章。我们已经在病毒系统地理模型中使用了这些数据，并显示了使用富集化的好处。 用于模拟病毒进化和传播的元数据。SARS-CoV-2序列的可用性，与全序列配对- 文本新冠肺炎文章和预印本，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)利用提取的文本丰富SARS-CoV-2序列元数据 从出版物和(2)获取针对不同患者人群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美国国立卫生研究院资助的联合工作，使次要的 利用丰富的基因组流行病学元数据来提高我们对SARS-CoV-2进化和 在不同的人群中传播。我们将通过我们的GeoBoost2数据传播丰富的数据 Dashboard、GenBank LinkOut和i2b2平台。后者将更直接地允许与 由4CE联盟共享的CoVID特定临床数据。

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.