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
基本信息
- 批准号:10701081
- 负责人:
- 金额:$ 58.37万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:2019-nCoVAddressAgeAgreementAlgorithmsBase SequenceCOVID-19COVID-19 pandemicClinicalClinical DataCollaborationsCommunicable DiseasesCoronavirusDataData AnalysesData SetEpidemiologyEvolutionFundingGenbankGenderGeneticGenotypeGeographyGoalsHealthInternationalInterventionJointsJournalsKnowledgeLinkLocationManualsMetadataMethodsModelingNatural Language ProcessingOntologyOutcomePatient CarePatientsPeer ReviewPerformancePhylogenetic AnalysisPopulationPopulation GroupPopulations at RiskPrintingProbabilityPublic HealthPublicationsPublishingRaceRecordsRelative RisksReportingResearchResearch PersonnelResolutionResourcesRiskSARS coronavirusScientistSequence AnalysisSeveritiesSpecific qualifier valueSystemTestingTextUnified Medical Language SystemUnited States National Institutes of HealthUpdateViralViral GenomeVirusWorkclinical phenotypecohortcomorbiditycoronavirus diseasedashboarddata sharingdeep learningdemographicsfield studygenomic epidemiologyheuristicsimprovedinsightnovelpandemic diseasepopulation healthpreventpublic databasepublic repositoryresidenceresponsesecondary analysissextext searchingtransmission processtrendvirus characteristic
项目摘要
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.
项目摘要
为了应对COVID-19大流行,科学家们发表了超过10万篇研究文章
并提供了超过八十万个病毒基因组序列。这些序列,沿着它们的
元数据,可用于了解病毒的演变和传播及其对公共卫生的影响,
基因组流行病学研究。然而,这些序列记录通常不包含患者元数据
例如人口统计学、临床严重程度或合并症,使研究人员无法揭示
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6万份记录和来自GenBank的6万份记录,
性别在基因库记录中的比例不到1%,在GISAID中,26%包括性别,24%包括年龄。为
在其他领域,缺失数据的数量甚至更加明显,没有任何资源提供有关
患者的种族和仅GISAID在不到5%的记录中指定严重程度(即ICU)。至地址缺失
研究人员可以利用与新序列相关的出版物,但是,
序列记录通常从不使用到出版物的链接进行更新。从我们分析的记录来看,
GISAID中有3.4%(748 K)和GenBank中< 1%(117 K)与出版物有链接。这极大地阻碍了
这些序列的二次数据分析,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。该提案的目的是加强
基因组流行病学和人群健康的COVID-19的框架,
在GenBank和GISAID等公共数据库中丰富SARS-CoV-2核酸序列元数据,
相关发布文章中的元数据。我们将纳入来自患者第一线临床医生的意见,
在大流行期间的护理,并建立在我们的NIH资助的工作(R 01 AI 117011),其中使用自然语言
使用序列记录的对应的已发布的地理元数据来丰富序列记录的地理元数据
文章.我们在病毒传播地理模型中使用了这些数据,并展示了使用丰富的
用于模拟病毒进化和传播的元数据。SARS-CoV-2序列的可用性,与完整的
COVID-19文章和预印本,为元数据丰富和科学发现提供了机会
超越了我们之前的工作。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
从出版物和(2)获得关键的流行病学见解不同的患者人口统计学使用我们丰富的
SARS-CoV-2序列数据集。我们将利用我们先前由NIH资助的联合工作,
使用丰富的基因组流行病学元数据,以提高我们对SARS-CoV-2进化的理解,
在不同的人群中传播。我们将通过GeoBoost 2数据传播丰富的数据
仪表板、GenBank LinkOut和i2 b2平台。后者将更直接地允许与
4CE联盟共享的COVID特定临床数据。
项目成果
期刊论文数量(5)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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GRACIELA GONZALEZ HERNANDEZ其他文献
GRACIELA GONZALEZ HERNANDEZ的其他文献
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{{ truncateString('GRACIELA GONZALEZ HERNANDEZ', 18)}}的其他基金
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:
10681068 - 财政年份:2022
- 资助金额:
$ 58.37万 - 项目类别:
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:
10390667 - 财政年份:2021
- 资助金额:
$ 58.37万 - 项目类别:
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 - 财政年份:2016
- 资助金额:
$ 58.37万 - 项目类别:
Text Processing and Geospatial Uncertainty for Phylogeography of Zoonotic Viruses
人畜共患病毒系统发育地理学的文本处理和地理空间不确定性
- 批准号:
8698542 - 财政年份:2013
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$ 58.37万 - 项目类别:
Mining Social Network Postings for Mentions of Potential Adverse Drug Reactions
挖掘社交网络帖子中提及潜在药物不良反应的内容
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8222740 - 财政年份:2012
- 资助金额:
$ 58.37万 - 项目类别:
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