The LINCS DCIC Engagement Plan with the CFDE

LINCS DCIC 与 CFDE 的合作计划

• 批准号: 10444350
• 负责人: Avi Ma'ayan
• 金额: $ 21万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-23 至 2022-09-22
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10444350
• 关键词: LINCS; DCIC; Engagement; Plan; CFDE

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.

驱动科学问题，通过与CFDE互动以及为什么没有解决的科学问题 但是很可行 基于集成网络的蜂窝签名（LINC）程序的库（1）收集了大量数据 来自成千上万个单个小分子以及敲除，敲低的人类细胞 和单基因的过表达。介绍了扰动的人类细胞（n> 50）的各种集合。 在扰动之前和之后，具有一系列OMICS分析，包括转录组学，蛋白质组学， 表观基因组学，细胞活力和成像在不同时间点以及应用小分子的位置 以不同的浓度。总共预计将产生和提供超过200万个签名 作为社区的查询和重复使用的资源，当时Lincs计划正式结束 （6/2020）。这样的资源可用于研究分子的无限应用 疾病的机制，重新利用现有药物，预测临床前小的副作用和适应症 分子，将小分子与可能直接和间接影响的靶标相关联 重建细胞信号传导和基因调节网络，了解所有可能的细胞的全球空间 各国应对所有人类细胞的所有可能扰动，以及更多的应用和使用 案例。 Lincs资源的这种利用已经在发生，但可以通过 Lincs数据协调和集成中心（DCIC）通过互动而继续努力 在未来3年中，CFDE和其他CF DCC。 到目前为止，林克斯财团制作的约400份出版物已被约6,000篇论文引用 证明该计划对研究社区的影响很大。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 > 100万唯一用户已经访问，目前每月约有30,000个唯一用户（基于Google 分析）。这些强大的用法统计数据表明了Lincs资源的价值及其潜力 对药物发现以及一般的生物医学研究界产生持久影响。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数据的整合分析和可视化与其他相关数据相结合。实现这一目标 Lincs DCIC还处理了许多其他相关资源的其他外部数据类型 与Lincs数据集成，包括来自其他常见基金计划的数据，例如GTEX，表观基因组学 路线图和IMPC。但是，实现了此类数据集成工作，几乎没有考虑 社区标准，以确保其长期可及性，可访问性，互操作性和可重复性 （公平）（2）。我们参与NIH数据CONSONS PILOT项目财团（DCPPC）和 通用基金数据生态系统（CFDE）教会了我们有关如何更好地实现数据的许多教训 通过采用社区标准来协调以实现Lincs的长期可持续性 资源。因此，通过与CFDE互动，遵守CFDE将确定的要求， 我们将能够重新处理lincs数据，以及与lincs集成的其他数据， 可以提高公平性的转换，从而进一步实现更复杂的用例。 此外，通过直接与其他CF DCC进行交互，我们将启用Lincs数据的直接集成 与其他CF生成的资源。我们的计划是开发一个基于交互式网络的数据可视化 将使用户能够将RNA-seq样品（患者，单细胞或签名）投射到一个组件中 根据其转录组学数据分析降低维空间。这种可视化将链接到 描述每个样品以及自动识别簇的元数据，富集分析 每个样品或簇的结果，以及来自Lincs资源的药物和小分子的预测。 例如，基于网络的交互式数据可视化组件将启用，例如，协助KidsFirst Portal 包括医生在内的用户为患者的正确亚型开出最合适的治疗剂 随着时间的流逝，请跟踪患者以监控他们对治疗的反应，从而可以支持 如有必要，尽早更改治疗课程。 最后，通过通过大步将所有Lincs资源转移到云环境中，我们将确保 长期存档了Lincs资源，以确保最大的再利用和启用应用程序 目前甚至没有想象或可能。