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：
Avi Ma'ayan
金额：
$ 102.89万
依托单位：
ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
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：
https://reporter.nih.gov/project-details/10837964
关键词：
Address Adoption Affect Archives Automobile Driving Biological Assay Biomedical Research Cell Survival Cells Collection Communities Complex Data Data Commons Databases Dimensions Disease Educational process of instructing Ensure Environment FAIR principles Funding Genes Genotype-Tissue Expression Project Human Image Intuition Knock-out Libraries Link Metadata Molecular Monitor Network-based Online Systems Paper Patients Pharmaceutical Preparations Physicians Pilot Projects Process Proteomics Publications Research Resources Sampling Signal Transduction Therapeutic Time United States National Institutes of Health Visit Visualization computing resources data ecosystem data harmonization data integration data visualization drug discovery epigenomics gene regulatory network human data improved knock-down online resource overexpression patient subsets pre-clinical programs response side effect small molecule small molecule libraries statistics tool transcriptome sequencing transcriptomics treatment 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.

推动通过与 CFDE 合作来解决的科学问题以及为什么它没有这样做尚未可行基于集成网络的蜂窝签名库（LINCS）计划（1）收集了大量数据来自受到数千个单个小分子以及基因敲除、敲除、以及单个基因的过度表达。对受干扰的人类细胞 (n>50) 的不同集合进行了分析在扰动之前和之后进行一系列组学分析，包括转录组学、蛋白质组学、表观基因组学、细胞活力以及不同时间点和小分子应用位置的成像不同浓度。预计总共将产生和提供超过 200 万个签名作为 LINCS 计划正式结束时社区查询和重用的资源（6/2020）。这样的资源可用于无限的应用，例如，研究分子疾病机制，重新利用现有药物，预测临床前小药物的副作用和适应症分子，将小分子与它们可能直接和间接影响的目标联系起来，重建细胞信号和基因调控网络，了解所有可能的细胞的全局空间响应所有人类细胞所有可能的扰动的状态，以及更多的应用和使用案例。 LINCS 资源的利用已经发生，但可以通过以下方式显着增强： LINCS 数据协调与集成中心 (DCIC) 通过互动继续努力未来 3 年内与 CFDE 和其他 CF DCC 合作。到目前为止，LINCS 联盟出版的约 400 篇出版物已被约 6,000 篇其他论文引用，展示该计划对研究界的巨大影响。特别是，计算 LINCS DCIC 开发的资源非常成功。这些工具和数据库是已经有超过 100 万唯一用户访问过，目前每月约有 30,000 个唯一用户（基于 Google 分析）。这些强大的使用统计数据证明了 LINCS 资源的价值及其潜力对药物发现和整个生物医学研究界产生长期影响。这 LINCS DCIC 开发了基于 Web 的资源，以实现联合访问、直观查询和 LINCS 数据与其他相关数据相结合的综合分析和可视化。为了实现这一目标 LINCS DCIC 还处理了来自其他相关资源的许多其他外部数据类型与 LINCS 数据集成，包括来自其他共同基金计划（例如 GTEx、Epigenomics）的数据路线图和 IMPC。然而，这种数据集成工作是在很少考虑到的情况下实现的社区标准，以确保其长期可查找性、可访问性、互操作性和可重用性（公平）（2）。我们参与 NIH 数据共享试点项目联盟 (DCPPC) 和共同基金数据生态系统（CFDE）给了我们很多关于如何更好地获取数据的经验教训通过采用社区标准进行协调，以实现 LINCS 的长期可持续性资源。因此，通过与 CFDE 互动，遵守 CFDE 将制定的要求，我们将能够重新处理 LINCS 数据以及用于与 LINCS 集成的其他数据，转换将提高公平性，进一步实现更复杂的用例。此外，通过与其他 CF DCC 直接交互，我们将能够直接集成 LINCS 数据与其他 CF 生成的资源。我们的计划是开发一个基于网络的交互式数据可视化该组件将使用户能够将 RNA-seq 样本（患者、单细胞或特征）投影到基于转录组学数据分析的低维空间。此类可视化将链接到描述每个样本的元数据，以及自动识别的簇、富集分析每个样本或簇的结果，以及来自 LINCS 资源的药物和小分子的预测。例如，这个基于网络的交互式数据可视化组件将能够协助 KidsFirst 门户用户，包括医生，为正确的患者亚型开出最合适的治疗方法，以及随着时间的推移跟踪患者以监测他们对治疗的反应，从而为决策提供支持如有必要，尽早改变疗程。最后，通过STRIDES将所有LINCS资源转移到云环境中，我们将确保 LINCS 资源长期存档，确保最大程度的重用并支持以下应用程序目前甚至无法想象或不可能。