A cloud-based system for scalable, privacy-preserving, and interactive immune repertoire analysis in vaccination and infection

基于云的系统，用于疫苗接种和感染方面的可扩展、隐私保护和交互式免疫库分析

• 批准号: 9557448
• 负责人: Jun Xiao
• 金额: $ 30万
• 依托单位: IMMUDX, LLC
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-02-06 至 2020-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9557448
• 关键词: Address; Aging; Algorithm Design; Algorithms; Antibodies; Antibody Repertoire; Architecture; Autoimmune Diseases; B-Lymphocytes; Basic Science; Big Data; Biomedical Computing; Biomedical Research; Cataloging; Catalogs; Clinical; Clone Cells; Cloud Computing; Communities; Custom; Data; Data Analyses; Data Analytics; Data Security; Data Set; Data Sources; Development; Disease; Elderly; Environment; Foundations; Future; Galaxy; Generations; Goals; Grant; HIV; Health; Heart; Hypersensitivity; Imagery; Immune; Immune response; Immunization; Immunoglobulin Somatic Hypermutation; Immunologist; Individual; Infant; Infection; Influenza; Influenza vaccination; Informatics; Ingestion; Institutional Policy; Internet; Investigation; Knowledge; Length; Malaria; Malignant Neoplasms; Measures; Methods; Modernization; Ohio; Ownership; Performance; Phase; Privacy; Privatization; Process; Receptor Cell; Receptors, Antigen, B-Cell; Research; Research Infrastructure; Research Personnel; Running; Services; Small Business Innovation Research Grant; Software Tools; Solid; System; T-Cell Receptor; T-cell receptor repertoire; Technology; Testing; Universities; Ursidae Family; Vaccination; Vaccines; Variant; Visual; adaptive immune response; adaptive immunity; austin; base; cancer cell; cloud based; cohort; computer infrastructure; computerized data processing; cost; data format; data integration; data sharing; deep sequencing; design; disease diagnosis; immune health; insight; next generation sequencing; outcome forecast; parallel processing; programs; scale up; skills; success; tool; web services; young adult

PROJECT SUMMARY We propose to build a cloud-based integrated solution for scalable, customizable, privacy-preserving, and interactive antibody repertoire analysis. Immune repertoire sequencing (IR-seq) has become a useful tool in both basic research and clinical settings. As the heart of the adaptive immunity to infection and many vaccines, the abundance and diversity composition of the B cell receptor (BCR) and its dynamic changes in health and diseases bear information of how to evaluate immune health, perform disease diagnosis and prognosis, and measure vaccination effect. However, there is a computational bottleneck for large scale antibody lineage construction, a lack of decomposable pipeline modules that preserve privacy and ownership, and a missing gap for interactive linage analysis and visualization. In this Phase I grant, we will (1) break the bottlenecks of pipeline processing and scale up the core algorithms to handle large sequence data sets; (2) protect private data and proprietary processing algorithms with modularized pipeline, integrated cloud-local processing, and data perturbation methods; 3) develop end-to-end web services for pipeline composition and interactive analysis visualization in a cloud-based deployment solution. Existing commercial efforts are mostly focusing on cancer related IR-seq analysis aiming to trace the disappearing of cancer cells after therapy, which solely focus on cataloging sequence species and abundance. This kind of analysis is much simpler and easier, compared to analyzing IR-seq data in infection and vaccination. Providing insights on host immune responses is a much more challenging but much needed task. Once the pipeline is built, it can be readily adapted to analyze cancer IR-seq data. Also, existing algorithms and optimizations that have been developed for other big data analysis can be further developed and applied to the IR-seq data analysis. We will use a publically available BCR repertoire data on an influenza vaccination cohort and a TCR repertoire data on an aging cohort to test the feasibility of the project. The long term goal of this proposal is to build cloud based accessible and customizable services for experts as well as non-specialists. We aim to provide an integrated solution for the ingestion, processing, analysis, exploration and visualization, interpretation and sharing of data generated by deep sequencing of full length antibody and TCR repertoire. The success of this Phase I SBIR will provide a solid foundation for the product launch of a commercial cloud based solution in Phase II, during which we will continue our investigation on big data security and privacy to facilitate compliance with institutional policies and design and develop programmable APIs and tools to facilitate integrations with more third party modules and services.

项目总结 我们建议构建基于云的集成解决方案，以实现可扩展、可定制、隐私保护和 交互式抗体库分析。免疫谱系测序(IR-SEQ)已成为这两个领域的有用工具 基础研究和临床环境。作为对感染和许多疫苗的适应性免疫的核心， 健康人群B细胞受体(BCR)的丰度和多样性组成及其动态变化 疾病承载着如何评估免疫健康、进行疾病诊断和预后的信息，以及 衡量疫苗接种效果。然而，大规模抗体谱系的计算存在瓶颈。 结构，缺乏保护隐私和所有权的可分解管道模块，以及缺失的缺口 用于交互式线性分析和可视化。在这一阶段，我们将(1)打破管道的瓶颈 处理和扩展核心算法，以处理大型序列数据集；(2)保护私有数据和 具有模块化管道、集成的云本地处理和数据的专有处理算法 3)开发用于管道组合和交互分析的端到端Web服务 基于云的部署解决方案中的可视化。现有的商业努力大多集中在癌症上 相关的IR-SEQ分析，旨在追踪治疗后癌细胞的消失，仅专注于 编目序列、物种和丰度。与之相比，这种分析更简单、更容易 分析感染和疫苗接种的IR-SEQ数据。提供关于宿主免疫反应的见解要多得多 这是一项极具挑战性但亟需完成的任务。一旦管道建成，它就可以很容易地被改装成分析癌症的IR-SEQ 数据。此外，为其他大数据分析开发的现有算法和优化可以 进一步发展并应用于IR-SEQ数据分析。我们将使用公开提供的BCR曲目数据 关于流感疫苗接种队列和关于老龄化队列的TCR曲目数据，以测试 项目。该计划的长期目标是构建基于云的可访问和可定制的服务， 有专家也有非专家。我们的目标是为摄取、加工、 分析、探索和可视化、解释和共享Full的深度测序生成的数据 长度抗体和TCR谱系。这一阶段SBIR的成功将为 在第二阶段推出基于商业云的解决方案的产品，在此期间我们将继续调查 关于大数据安全和隐私，以促进遵守机构政策以及设计和开发 可编程API和工具，可促进与更多第三方模块和服务的集成。