The LINCS DCIC Engagement Plan with the CFDE
LINCS DCIC 与 CFDE 的合作计划
基本信息
- 批准号:10837964
- 负责人:
- 金额:$ 102.89万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-09-23 至 2024-09-22
- 项目状态:已结题
- 来源:
- 关键词:AddressAdoptionAffectArchivesAutomobile DrivingBiological AssayBiomedical ResearchCell SurvivalCellsCollectionCommunitiesComplexDataData CommonsDatabasesDimensionsDiseaseEducational process of instructingEnsureEnvironmentFAIR principlesFundingGenesGenotype-Tissue Expression ProjectHumanImageIntuitionKnock-outLibrariesLinkMetadataMolecularMonitorNetwork-basedOnline SystemsPaperPatientsPharmaceutical PreparationsPhysiciansPilot ProjectsProcessProteomicsPublicationsResearchResourcesSamplingSignal TransductionTherapeuticTimeUnited States National Institutes of HealthVisitVisualizationcomputing resourcesdata ecosystemdata harmonizationdata integrationdata visualizationdrug discoveryepigenomicsgene regulatory networkhuman dataimprovedknock-downonline resourceoverexpressionpatient subsetspre-clinicalprogramsresponseside effectsmall moleculesmall molecule librariesstatisticstooltranscriptome sequencingtranscriptomicstreatment response
项目摘要
Driving scientific questions that will be addressed by engaging with the CFDE and why it has not
yet been feasible
The Library of Integrated Network-based Cellular Signatures (LINCS) program (1) collected massive data
from human cells perturbed by thousands of single small molecules as well as knockouts, knockdowns,
and over-expression of single genes. The diverse collections of perturbed human cells (n>50) were profiled
before and after the perturbations with an array of omics assays that include transcriptomics, proteomics,
epigenomics, cell viability, and imaging at different time points and where the small molecules were applied
in different concentrations. Altogether, over 2 million signatures are expected to be produced and provided
as a resource for the community for query and reuse at the time when the LINCS program officially ends
(6/2020). Such a resource can be used for limitless applications, for example, to study molecular
mechanisms of disease, repurpose existing drugs, predict side effects and indications for pre-clinical small
molecules, associate small molecules with the targets that they likely affect directly and indirectly,
reconstruct cell signaling and gene regulatory networks, understand the global space of all possible cellular
states in response to all possible perturbations of all human cells, and many more applications and use
cases. This utilization of LINCS resources is already happening but can be significantly enhanced via
continued efforts led by the LINCS Data Coordination and Integration Center (DCIC) through interactions
with the CFDE and other CF DCCs in the next 3 years.
So far, the ~400 publications produced by the LINCS consortium have been cited by ~6,000 other papers,
demonstrating the high impact of the program on the research community. In particular, the computational
resources developed by the LINCS DCIC have been very successful. These tools and databases were
already visited by >1 million unique users, with currently ~30,000 unique users per month (based on Google
Analytics). These strong usage statistics demonstrate the value of LINCS resources and their potential for
making long-lasting impact on drug discovery, and the biomedical research community in general. The
LINCS DCIC developed web-based resources to enable the federated access, intuitive querying, and
integrative analysis and visualization of the LINCS data combined with other relevant data. To achieve this
the LINCS DCIC also processed many additional external data types from other relevant resources to be
integrated with LINCS data including data from other Common Fund programs such as GTEx, Epigenomics
Roadmap, and IMPC. However, such data integration efforts were achieved with little consideration of
community standards to ensure their long term findability, accessibility, interoperability and reusability
(FAIR) (2). Our involvement with the NIH Data Commons Pilot Project Consortium (DCPPC) and the
Common Fund Data Ecosystem (CFDE) taught us many lessons on how to better achieve data
harmonization via the adoption of community standards to achieve long term sustainability of LINCS
resources. Hence, by interacting with the CFDE, adhering to the requirements that the CFDE will establish,
we will be able to reprocess the LINCS data, and the other data we use to integrate with LINCS, with
transformations that will enable improved FAIRness, further enabling more complex use cases.
In addition, by interacting directly with other CF DCCs we will enable the direct integration of LINCS data
with other CF generated resources. Our plan is to develop an interactive web-based data visualization
component that will enable users to project RNA-seq samples (patients, single cells, or signatures) into a
lower dimensional space based on their transcriptomics data profiling. Such visualization will be linked to
the metadata describing each sample, as well as automatically identified clusters, enrichment analysis
results for each sample or cluster, and predictions of drugs and small molecules from the LINCS resource.
This interactive web-based data visualization component will enable, for example, assisting KidsFirst portal
users, including physicians, to prescribe the most appropriate therapeutics to the right subtype of patients,
as well as trace patients over time to monitor their response to treatment enable decision support for
changing treatment course early, if necessary.
Finally, by moving all LINCS resources into a cloud environment through STRIDES, we will ensure that
LINCS resources are archived for the long term ensuring maximal reuse and enabling applications that are
currently not even imagined or possible.
推动科学问题将通过与CFDE合作来解决,以及为什么它没有这样做
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)
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Avi Ma'ayan其他文献
Avi Ma'ayan的其他文献
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{{ truncateString('Avi Ma'ayan', 18)}}的其他基金
ARCHS4: Massive Mining of Publicly Available RNA Sequencing Data
ARCHS4:大规模挖掘公开的 RNA 测序数据
- 批准号:
10693339 - 财政年份:2022
- 资助金额:
$ 102.89万 - 项目类别:
Proteogenomic translator for cancer biomarker discovery towards precision medicine
用于癌症生物标志物发现和精准医学的蛋白质基因组翻译
- 批准号:
10442088 - 财政年份:2022
- 资助金额:
$ 102.89万 - 项目类别:
ARCHS4: Massive Mining of Publicly Available RNA Sequencing Data
ARCHS4:大规模挖掘公开的 RNA 测序数据
- 批准号:
10527721 - 财政年份:2022
- 资助金额:
$ 102.89万 - 项目类别:
ARCHS4: Massive Mining of Publicly Available RNA Sequencing Data
ARCHS4:大规模挖掘公开的 RNA 测序数据
- 批准号:
10814654 - 财政年份:2022
- 资助金额:
$ 102.89万 - 项目类别:
Proteogenomic translator for cancer biomarker discovery towards precision medicine
用于癌症生物标志物发现和精准医学的蛋白质基因组翻译
- 批准号:
10655588 - 财政年份:2022
- 资助金额:
$ 102.89万 - 项目类别:
The LINCS DCIC Engagement Plan with the CFDE
LINCS DCIC 与 CFDE 的合作计划
- 批准号:
10468520 - 财政年份:2020
- 资助金额:
$ 102.89万 - 项目类别:
The LINCS DCIC Engagement Plan with the CFDE
LINCS DCIC 与 CFDE 的合作计划
- 批准号:
10444350 - 财政年份:2020
- 资助金额:
$ 102.89万 - 项目类别:
The LINCS DCIC Engagement Plan with the CFDE
LINCS DCIC 与 CFDE 的合作计划
- 批准号:
10682935 - 财政年份:2020
- 资助金额:
$ 102.89万 - 项目类别:
Knowledge Management Center for Illuminating the Druggable Genome
阐明可药物基因组的知识管理中心
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10560469 - 财政年份:2018
- 资助金额:
$ 102.89万 - 项目类别:
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