The LINCS DCIC Engagement Plan with the CFDE

LINCS DCIC 与 CFDE 的合作计划

• 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
• 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

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接触来解决的科学问题，以及为什么它没有 但却是可行的 综合网络蜂窝签名库(LINCS)计划(1)收集了大量数据 从被数以千计的单个小分子以及敲除、 以及单基因的过度表达。各种不同的受干扰的人类细胞(n&gt；50)的收集被描绘出来 在扰动前后进行了一系列组学分析，包括转录组学、蛋白质组学、 不同时间点和小分子应用的表观基因组学、细胞活力和成像 以不同的浓度。预计总共将产生和提供200多万个签名 作为Lincs计划正式结束时供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 疾病机制，重新调整现有药物的用途，预测副作用和临床前小分子药物的适应症 分子，将小分子与它们可能直接或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 状态，以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 案子。Lincs资源的这种利用已经在发生，但可以通过以下方式显著提高 LINCS数据协调和集成中心(DCIC)通过互动继续努力 在未来3年与CFDE和其他CF DCC合作。 到目前为止，Lincs联盟出版的~400篇出版物已被~6000篇其他论文引用， 展示了该计划对研究界的高度影响。尤其是计算性的 Lincs DCIC开发的资源非常成功。这些工具和数据库是 已有100万独立用户访问，目前每月约有30,000独立用户(基于Google 分析)。这些强大的使用统计数据表明了Lincs资源的价值及其潜在的 对药物发现和整个生物医学研究界产生长期影响。这个 Lincs DCIC开发了基于Web的资源，以实现联合访问、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。要做到这一点 Lincs DCIC还处理了来自其他相关资源的许多额外外部数据类型，以 与LINCS数据集成，包括来自其他共同基金计划的数据，如GTEx、表观基因组学 路线图和IMPC。然而，这样的数据集成工作是在很少考虑的情况下实现的 确保其长期可查找性、可访问性、互操作性和可重用性的社区标准 (公平)(2)。我们与NIH数据共享试点项目联盟(DCPPC)和 共同基金数据生态系统(CFDE)在如何更好地获取数据方面给我们上了很多课 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将确定的要求， 我们将能够重新处理Lincs数据，以及我们用来与Lincs集成的其他数据， 转型将提高公平性，进一步支持更复杂的用例。 此外，通过与其他CFDCC直接交互，我们将实现LINCS数据的直接集成 与其他CF生成的资源一起使用。我们的计划是开发基于Web的交互式数据可视化 组件，使用户能够将rna-seq样本(患者、单个细胞或签名)投影到 更低维度的空间基于他们的转录组数据剖析。这样的可视化将链接到 描述每个样本的元数据，以及自动识别的集群、富集化分析 每个样本或集群的结果，以及来自Lincs资源的药物和小分子的预测。 例如，这一基于Web的交互式数据可视化组件将支持帮助儿童第一门户 用户，包括医生，为正确的亚型患者开出最合适的疗法， 以及随着时间的推移跟踪患者以监控他们对治疗的反应，从而实现决策支持 如有必要，尽早改变疗程。 最后，通过STRADS将所有Lincs资源移动到云环境中，我们将确保 LINCS资源长期存档，确保最大限度地重复使用，并支持 目前甚至是想象不到或可能的。