The LINCS DCIC Engagement Plan with the CFDE

LINCS DCIC 与 CFDE 的合作计划

• 批准号: 10468520
• 负责人: Avi Ma'ayan
• 金额: $ 67.64万
• 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/10468520
• 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.

推动将通过与CFDE合作来解决的科学问题，以及为什么它没有 但可行 基于集成网络的蜂窝签名库（LINCS）程序（1）收集了大量数据 从被数千个单个小分子干扰的人类细胞，以及敲除，敲除， 和单个基因的过度表达。对不同的受干扰的人细胞集合（n>50）进行了分析。 在用一系列组学测定干扰之前和之后，所述组学测定包括转录组学，蛋白质组学， 表观基因组学、细胞活力和不同时间点的成像以及小分子的应用 在不同的浓度。预计总共将产生和提供200多万个签名 在LINCS计划正式结束时，作为社区查询和重用的资源 （6/2020）。这样的资源可以用于无限的应用，例如，研究分子 疾病的机制，重新利用现有的药物，预测副作用和适应症的临床前小 分子，将小分子与它们可能直接或间接影响的目标相关联， 重建细胞信号和基因调控网络，了解所有可能的细胞 状态响应所有人类细胞的所有可能的扰动，以及更多的应用和使用 例LINCS资源的这种利用已经在进行，但可以通过以下方式显著增强： 由LINCS数据协调和集成中心（DCIC）领导的持续努力， 与CFDE和其他CF DCC在未来3年。 到目前为止，LINCS联盟发表的约400篇出版物已被约6,000篇其他论文引用， 展示了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 已被超过100万独立用户访问，目前每月约有30，000名独立用户（基于Google 分析）。这些强大的使用统计数据证明了LINCS资源的价值及其在以下方面的潜力： 对药物发现和整个生物医学研究界产生长期影响。的 LINCS DCIC开发了基于Web的资源，以支持联合访问、直观查询和 结合其他相关数据对LINCS数据进行综合分析和可视化。实现这一 LINCS DCIC还处理了来自其他相关资源的许多额外的外部数据类型， 与LINCS数据集成，包括来自其他共同基金项目的数据，如GTEx、表观基因组学 路线图和IMPC。然而，这样的数据集成工作几乎没有考虑到 社区标准，以确保其长期的可查找性，可访问性，互操作性和可重用性 （2）第二节。我们参与了NIH数据共享试点项目联盟（DCPPC）和 共同基金数据生态系统（CFDE）教会了我们许多关于如何更好地实现数据的经验教训 通过采用社区标准实现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，以及我们用于与LINCS集成的其他数据， 这些转换将实现改进的公平性，进一步支持更复杂的用例。 此外，通过与其他CF DCC直接交互，我们将实现LINCS数据的直接集成 与其他CF产生的资源。我们的计划是开发一个交互式的基于网络的数据可视化 该组件将使用户能够将RNA-seq样本（患者，单细胞或签名）投影到一个 基于其转录组学数据分析的低维空间。这种可视化将与 描述每个样本的元数据以及自动识别的聚类、富集分析 每个样本或聚类的结果，以及来自LINCS资源的药物和小分子的预测。 例如，这一基于网络的交互式数据可视化组件将能够协助KidsFirst门户网站 包括医生在内的用户为正确亚型的患者开出最合适的治疗处方， 以及随着时间的推移追踪患者以监测他们对治疗的反应，为以下方面提供决策支持 如有必要，应尽早改变疗程。 最后，通过将所有LINCS资源通过CNODES迁移到云环境中，我们将确保 LINCS资源被长期存档，确保最大程度的重用，并使应用程序 目前甚至无法想象或可能。