CAREER: Computational tools for analyzing and interpreting DNA methylation

职业：用于分析和解释 DNA 甲基化的计算工具

• 批准号: 2144534
• 负责人: Vicky Yao
• 金额: $ 79.42万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144534&HistoricalAwards=false
• 关键词: CAREER; Computational; tools; analyzing; interpreting

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).DNA methylation is a critical biological process that plays such an important role in gene regulation that it has often been referred to as the “fifth base of DNA.” In addition to playing an important role during development and throughout aging, aberrations in DNA methylation have been discovered to cause disease, including cancer and neurological disorders. Yet much is still unknown regarding where DNA methylation changes occur and which genes they impact. This, coupled with its dynamic nature, which can vary across regions of the body, throughout the life cycle, and in response to the environment, makes accurately identifying the association of specific methylation patterns to biological phenomena of interest and interpreting their downstream impacts challenging. The project will result in a suite of new tools to enable analysis of methylation sites across experimental technologies, analyze methylation data in the context of different tissues and cell types, and predict the downstream impacts of methylation sites that are changing across conditions. These tools will enable researchers to better interpret their methylation data in light of existing knowledge, which can in turn result in improved understanding of fundamental biological processes (e.g., development, aging). In addition to making all methods available as open-source software, databases of predictions and interactive visualizations will be developed and accessible online. By doing so, biological researchers with no programming experience can use a query-based system to easily make and explore predictions in the context of their own data. This project also has a local outreach component to help increase the diversity and persistence of underrepresented minorities in STEM by engaging high school biology teachers and community college students with newly designed data driven curricula as well as research opportunities.The research will adapt cutting edge deep learning methods used for imputation in other domains to increase the coverage of DNA methylation platforms that profile 10% of all sites, saving time and resources; use a hierarchical framework that mirrors natural tissue and cell type dependencies to find location-specific methylation hallmarks; and incorporate known protein-protein interactions to associate CpG sites with biological function, beyond existing methods which primarily capture proximal regulatory relationships. In addition to the methodological contributions of this work, the project is inherently interdisciplinary and will lay the groundwork for understanding fundamental biological questions such as how methylation regulates and maintains tissue specificity. The tools and results from this project can be found at: https://cs.rice.edu/~vy/.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项的全部或部分资金来源于《2021 年美国救援计划法案》（公法 117-2）。DNA 甲基化是一个关键的生物过程，在基因调控中发挥着如此重要的作用，因此通常被称为“DNA 的第五碱基”。 DNA 甲基化异常除了在发育和整个衰老过程中发挥重要作用外，还被发现会导致疾病，包括癌症和神经系统疾病。然而，关于 DNA 甲基化变化发生在何处以及它们影响哪些基因，仍然有很多未知之处。再加上它的动态性质，在整个生命周期中，身体各部位的变化以及对环境的反应都会有所不同，使得准确识别特定甲基化模式与感兴趣的生物现象的关联并解释其下游影响具有挑战性。该项目将产生一套新工具，能够跨实验技术分析甲基化位点，分析不同组织和细胞类型背景下的甲基化数据，并预测随条件变化的甲基化位点的下游影响。这些工具将使研究人员能够根据现有知识更好地解释甲基化数据，从而提高对基本生物过程（例如发育、衰老）的理解。除了将所有方法作为开源软件提供外，还将开发预测和交互式可视化数据库并在线访问。通过这样做，没有编程经验的生物研究人员可以使用基于查询的系统在自己的数据背景下轻松做出和探索预测。该项目还有一个本地推广部分，通过让高中生物教师和社区学院学生参与新设计的数据驱动课程和研究机会，帮助增加 STEM 中代表性不足的少数群体的多样性和持久性。该研究将采用用于其他领域插补的尖端深度学习方法，以增加 DNA 甲基化平台的覆盖范围，该平台可描述所有站点的 10%，从而节省时间和资源；使用反映自然组织和细胞类型依赖性的分层框架来查找特定位置的甲基化标志；并结合已知的蛋白质-蛋白质相互作用，将 CpG 位点与生物功能联系起来，超越主要捕获近端调控关系的现有方法。除了这项工作的方法论贡献之外，该项目本质上是跨学科的，将为理解基本的生物学问题（例如甲基化如何调节和维持组织特异性）奠定基础。该项目的工具和结果可在以下网址找到：https://cs.rice.edu/~vy/。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。