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Core C: Integrated Cancer and Aging Bioinformatics

Core C: Integrated Cancer and Aging Bioinformatics
核心 C:综合癌症和衰老生物信息学
批准号:
10270685
负责人:
MAXIM NIKOLAIEVICH SHOKHIREV
金额:
$31.48万
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
未结题
起止时间:
2021-09-15 至 2026-08-31

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中文摘要
翻译
项目概要-核心C 了解肝脏衰老和衰老在肝癌发展中的作用将需要收集,QC, 分析和整合大量高通量数据集。综合衰老与癌症 生物信息学核心,核心C,将通过为PPG成员提供三项关键服务来实现这一目标。 第一项关键服务将建立日常活动所需的基础设施。这将包括招聘 并培训两名专门的博士级生物信息学分析师,他们将与PPG成员密切合作, 从数据管理和分析到自定义工具和管道实施, 演示文稿和科学写作。除了关键人员,核心也将获得所需的硬件 来存储回答特定问题所需的数据和生物信息学管道。这项服务的最后一个方面是 与核心A合作,向PPG成员传达并提供数据和分析。 第二项关键服务将是对PPG成员数据进行最先进的重点分析, 包括执行数据质量控制、生成干净量化数据集、识别 改变,例如对肝脏衰老和肿瘤发展至关重要的基因或途径,并汇编分析 转化为可以传播并与PPG成员共享的视觉演示。这项工作的重要内容 第二项关键服务将是教育和培训PPG成员的解释和限制, 分析,并提供共享服务器和教育资源和培训, PPG成员和学员进行一些自己的分析,并更加熟悉 他们的数据. 最后一项关键服务将是将这些数据集整合到衰老和肿瘤的整体视图中 在肝脏的发展。这将包括整合基因表达、蛋白质表达和代谢产物 使用基于网络的方法和统计方法进行丰度测量,目的是识别分子 随着年龄的增长而变化。此外,单细胞分析将结合起来,不仅揭示基因的变化, 表达,而且空间组织,T细胞受体多样性和单个细胞的染色质可及性 在肝脏中。最后,机器学习方法将用于将联合收割机数据集组合成肿瘤的预测模型。 预防性干预对肿瘤形成的可能影响。
英文摘要
PROJECT SUMMARY – CORE C Understanding aging in the liver and the role of aging in liver cancer development will require the collection, QC, analysis, and integration of a large collection of high-throughput datasets. The Integrative Aging and Cancer Bioinformatics Core, Core C, will achieve this goal by offering three key services for PPG members. The first key service will establish the infrastructure needed for day-to-day activities. This will include the hiring and training of two dedicated PhD-level bioinformatics analysts that will work closely with PPG members on all aspects of their projects from data curation and analysis to custom tool and pipeline implementation, data presentation, and scientific writing. In addition to key personnel, the core will also obtain the hardware needed to store data and bioinformatic pipelines required to answer specific questions. The final aspect of this service is to work with Core A to communicate and make available data and analyses to PPG members. The second key service will be to perform state-of-the-art focused analyses on PPG member data which will include performing data quality control, generation of cleaned quantified datasets, identifying features that are changing, such as genes or pathways central to liver aging and tumor development, and compiling the analyses into visual presentations that can be disseminated and shared with PPG members. An important part of this second key service will be to educate and train PPG members on the interpretation and limitations of the analyses and to provide access to shared servers and educational resources and trainings that would enable PPG members and trainees to perform some of their own analyses and become more intimately acquainted with their data. The final crucial service will be the integration of these datasets into a holistic view of aging and tumor development in the liver. This will include integrating gene expression, protein expression, and metabolite abundance measures using network-based and statistical approaches with the goal of identifying molecular mechanisms changing with age. Also, single-cell assays will be combined to reveal not only changes in gene expression, but also spatial organization, T-cell receptor diversity, and chromatin accessibility of individual cells in livers. Finally, machine learning approaches will be used to combine datasets into predictive models of tumor formation and possible effects of preventative interventions on tumor formation.
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Core C: Integrated Cancer and Aging Bioinformatics
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