Integrative and interactive analyses of host transcriptional response to COVID-19 and other respiratory viral infections
宿主对 COVID-19 和其他呼吸道病毒感染的转录反应的综合和交互式分析
基本信息
- 批准号:10372463
- 负责人:
- 金额:$ 7.77万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-05-05 至 2024-04-30
- 项目状态:已结题
- 来源:
- 关键词:2019-nCoVBioinformaticsBiologicalCOVID-19COVID-19 pandemicClinicalCodeCollaborationsCommunitiesComputer softwareComputing MethodologiesCoronavirusCustomDataData AnalysesData SetDatabasesDependenceDiseaseFamilyGene ExpressionGene Expression ProfileGene Expression ProfilingGene set enrichment analysisGenerationsGenesGenetic TranscriptionHumanImageImmune responseInfectionInfectious AgentInfluenzaIntuitionLaboratoriesLearning ModuleMachine LearningManualsMethodologyMethodsMiddle East Respiratory Syndrome CoronavirusOrganismPatientsPerformanceProcessProtocols documentationPublic HealthRNA ProcessingRNA analysisRaceReproducibilityRespiratory Tract InfectionsRhinovirusSARS coronavirusSARS-CoV-2 variantSelection CriteriaSequence AlignmentSeveritiesSoftware ToolsSpecific qualifier valueSymptomsTechnologyTimeTractionTranscriptUpdateVariantViral Respiratory Tract InfectionVirusVirus DiseasesVisualizationanalytical toolbasebiomedical scientistcoronavirus diseasedashboarddiagnostic strategydifferential expressionexperimental studygenetic signaturegenomic datagraphical user interfaceknowledge baselarge scale datamultiple datasetsnovelnovel coronaviruspandemic diseaseportabilityprototypepublic databasepublic repositoryrepositoryrespiratory virusresponsesexsoftware developmenttooltranscriptome sequencingtreatment strategyuser-friendlyvaccine developmentvaccine strategy
项目摘要
PROJECT SUMMARY
The current pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led
to global public health concerns. This novel coronavirus disease (COVID-19) shares similar clinical symptoms
with diseases caused by other viruses in the coronavirus family and other common respiratory viruses. When
an infectious agent replicates within a host organism, the host interacts with, and responds to the virus with
various mechanisms. Given the varying severity across patients and emergence of new SARS-CoV-2 variants,
there is an urgent need to understand how the host responds to COVID-19 and its variants. RNA sequencing
(RNA-seq) data that profile transcriptional response to SARS-CoV-2 and other respiratory viral infections are
available from public databases. Comparing the gene signatures across respiratory viruses will identify
similarities and differences of how the host responds to these infections. In particular, compendium analyses in
which multiple datasets are integrated bring great opportunities for generating novel biological hypotheses.
However, compendium analysis of RNA-seq data generated across different laboratories is an onerous task
given the different protocols, parameters, software and software versions used at the time of analyses.
This proposal focuses on the development of software tools to facilitate re-analyses of existing host-response
RNA-seq data to create a compendium of gene signatures using the same set of analytical tools and input
parameters. Our deliverables will include workflows with saved input files and parameters, fixed software
versions and dependencies that will facilitate reproducibility and collaboration. We will provide an accessible
graphical user interface that allows users to create custom signature sets by querying the data and if desired,
re-analyzing the data using one of our provided workflows or a workflow of their own choosing. Users will be
able to filter biological variables, perform cross species analysis, compare gene signatures to other gene set
repositories. In addition, we will create an accessible dashboard that will support the query, download,
visualization and reproducible analysis of gene expression data from SARS-CoV-2 and other common
respiratory viruses. Tools will be provided to allow the user to interactively visualize the data and inform the
choice of appropriate gene signatures. Not only will our software tools and dashboard provide an accessible
front end, we will also develop an easy-to-use, scalable and cloud-enabled backend that enables efficient
alignment of sequencing data. Our proposed project will empower biomedical scientists to experiment with
different computational methods, input parameters (including the alignment step) across multiple datasets and
respiratory viral infections. Thus, facilitating integrated and interactive analyses using datasets generated by
multiple laboratories to advance our understanding of host transcriptional response to COVID-19.
项目摘要
目前由严重急性呼吸道综合征冠状病毒2(SARS-CoV-2)引起的大流行导致了
全球公共卫生问题。这种新型冠状病毒病(COVID-19)具有类似的临床症状
冠状病毒家族中的其他病毒和其他常见呼吸道病毒引起的疾病。当
感染原在宿主生物体内复制,宿主与病毒相互作用,并对病毒作出反应,
各种机制。鉴于不同患者的严重程度不同以及新的SARS-CoV-2变体的出现,
迫切需要了解宿主如何应对COVID-19及其变体。RNA测序
(RNA-seq)数据,分析对SARS-CoV-2和其他呼吸道病毒感染的转录反应,
可从公共数据库获得。通过比较呼吸道病毒的基因特征,
宿主对这些感染反应的相似性和差异。特别是,
多个数据集的整合为产生新的生物学假说带来了巨大的机会。
然而,对不同实验室生成的RNA-seq数据进行概要分析是一项繁重的任务
考虑到分析时使用的不同方案、参数、软件和软件版本。
本提案侧重于开发软件工具,以促进对现有宿主反应的重新分析
RNA-seq数据使用相同的分析工具和输入创建基因签名概要
参数我们的交付成果将包括保存输入文件和参数的工作流程,固定软件
版本和依赖关系,这将促进可重复性和协作。我们将提供一个可访问的
图形用户界面,允许用户通过查询数据创建自定义签名集,如果需要,
使用我们提供的工作流程或他们自己选择的工作流程重新分析数据。用户将
能够过滤生物变量,执行跨物种分析,将基因签名与其他基因集进行比较
储存库。此外,我们将创建一个可访问的仪表板,支持查询,下载,
SARS-CoV-2和其他常见病毒的基因表达数据的可视化和可重复性分析
呼吸道病毒将提供工具,使用户能够以交互方式可视化数据,并告知
选择合适的基因标签。我们的软件工具和仪表板不仅提供了一个可访问的
前端,我们还将开发一个易于使用,可扩展和支持云的后端,
测序数据的比对。我们提出的项目将使生物医学科学家能够进行实验,
不同的计算方法,跨多个数据集的输入参数(包括对齐步骤),
呼吸道病毒感染因此,促进综合和互动的分析,使用数据集产生的,
多个实验室,以促进我们对宿主对COVID-19的转录反应的理解。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Ka Yee Yeung-Rhee其他文献
Ka Yee Yeung-Rhee的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Ka Yee Yeung-Rhee', 18)}}的其他基金
Integrative and interactive analyses of host transcriptional response to COVID-19 and other respiratory viral infections
宿主对 COVID-19 和其他呼吸道病毒感染的转录反应的综合和交互式分析
- 批准号:
10618134 - 财政年份:2022
- 资助金额:
$ 7.77万 - 项目类别:
Prediction and Network Construction Using High-throughput Data
利用高通量数据进行预测和网络构建
- 批准号:
7918948 - 财政年份:2009
- 资助金额:
$ 7.77万 - 项目类别:
Prediction and Network Construction Using High-throughput Data
利用高通量数据进行预测和网络构建
- 批准号:
7681282 - 财政年份:2008
- 资助金额:
$ 7.77万 - 项目类别:
Prediction and Network Construction Using High-throughput Data
利用高通量数据进行预测和网络构建
- 批准号:
8104045 - 财政年份:2008
- 资助金额:
$ 7.77万 - 项目类别:
Prediction and Network Construction Using High-throughput Data
利用高通量数据进行预测和网络构建
- 批准号:
8294758 - 财政年份:2008
- 资助金额:
$ 7.77万 - 项目类别:
Prediction and Network Construction Using High-throughput Data
利用高通量数据进行预测和网络构建
- 批准号:
7533087 - 财政年份:2008
- 资助金额:
$ 7.77万 - 项目类别:
Prediction and Network Construction Using High-throughput Data
利用高通量数据进行预测和网络构建
- 批准号:
8470899 - 财政年份:2008
- 资助金额:
$ 7.77万 - 项目类别:
Prediction and Network Construction Using High-throughput Data
利用高通量数据进行预测和网络构建
- 批准号:
7884325 - 财政年份:2008
- 资助金额:
$ 7.77万 - 项目类别:
Improved Pattern Recognition for Functional Genomics
改进功能基因组学的模式识别
- 批准号:
6765680 - 财政年份:2004
- 资助金额:
$ 7.77万 - 项目类别:
Improved Pattern Recognition for Functional Genomics
改进功能基因组学的模式识别
- 批准号:
7061736 - 财政年份:2004
- 资助金额:
$ 7.77万 - 项目类别:
相似海外基金
Collaborative Research: IIBR: Innovation: Bioinformatics: Linking Chemical and Biological Space: Deep Learning and Experimentation for Property-Controlled Molecule Generation
合作研究:IIBR:创新:生物信息学:连接化学和生物空间:属性控制分子生成的深度学习和实验
- 批准号:
2318829 - 财政年份:2023
- 资助金额:
$ 7.77万 - 项目类别:
Continuing Grant
Analysis of biological small molecule mixtures using multiple modes of mass spectrometric fragmentation coupled with new bioinformatics workflows
使用多种质谱裂解模式结合新的生物信息学工作流程分析生物小分子混合物
- 批准号:
BB/X019802/1 - 财政年份:2023
- 资助金额:
$ 7.77万 - 项目类别:
Research Grant
Collaborative Research: IIBR: Innovation: Bioinformatics: Linking Chemical and Biological Space: Deep Learning and Experimentation for Property-Controlled Molecule Generation
合作研究:IIBR:创新:生物信息学:连接化学和生物空间:属性控制分子生成的深度学习和实验
- 批准号:
2318830 - 财政年份:2023
- 资助金额:
$ 7.77万 - 项目类别:
Continuing Grant
Collaborative Research: IIBR: Innovation: Bioinformatics: Linking Chemical and Biological Space: Deep Learning and Experimentation for Property-Controlled Molecule Generation
合作研究:IIBR:创新:生物信息学:连接化学和生物空间:属性控制分子生成的深度学习和实验
- 批准号:
2318831 - 财政年份:2023
- 资助金额:
$ 7.77万 - 项目类别:
Continuing Grant
Bioinformatics-powered genetic characterization of the impact of biological systems on Alzheimer's disease and neurodegeneration
基于生物信息学的生物系统对阿尔茨海默病和神经退行性疾病影响的遗传表征
- 批准号:
484699 - 财政年份:2022
- 资助金额:
$ 7.77万 - 项目类别:
Operating Grants
REU Site: Bioinformatics Research and Interdisciplinary Training Experience in Analysis and Interpretation of Information-Rich Biological Data Sets (REU-BRITE)
REU网站:信息丰富的生物数据集分析和解释的生物信息学研究和跨学科培训经验(REU-BRITE)
- 批准号:
1949968 - 财政年份:2020
- 资助金额:
$ 7.77万 - 项目类别:
Standard Grant
REU Site: Bioinformatics Research and Interdisciplinary Training Experience in Analysis and Interpretation of Information-Rich Biological Data Sets (REU-BRITE)
REU网站:信息丰富的生物数据集分析和解释的生物信息学研究和跨学科培训经验(REU-BRITE)
- 批准号:
1559829 - 财政年份:2016
- 资助金额:
$ 7.77万 - 项目类别:
Continuing Grant
Bioinformatics Tools to Design and Optimize Biological Sensor Systems
用于设计和优化生物传感器系统的生物信息学工具
- 批准号:
416848-2011 - 财政年份:2011
- 资助金额:
$ 7.77万 - 项目类别:
University Undergraduate Student Research Awards
ABI Development: bioKepler: A Comprehensive Bioinformatics Scientific Workflow Module for Distributed Analysis of Large-Scale Biological Data
ABI 开发:bioKepler:用于大规模生物数据分布式分析的综合生物信息学科学工作流程模块
- 批准号:
1062565 - 财政年份:2011
- 资助金额:
$ 7.77万 - 项目类别:
Continuing Grant
Bioinformatics-based hypothesis generation with biological validation for plant stress biology
基于生物信息学的假设生成和植物逆境生物学的生物验证
- 批准号:
261818-2006 - 财政年份:2010
- 资助金额:
$ 7.77万 - 项目类别:
Discovery Grants Program - Individual