Collaborative Research: BeeHive: A Cross-Problem Benchmarking Framework for Network Biology

合作研究：BeeHive：网络生物学的跨问题基准框架

• 批准号: 2233968
• 负责人: Anthony Gitter
• 金额: $ 70.44万
• 依托单位: Morgridge Institute for Research, Inc.
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2233968&HistoricalAwards=false
• 关键词: Collaborative; Research; BeeHive; Cross; Problem

Many important aspects of biology involve relationships between the molecules within cells. For example, a medicine may turn off a diseased protein, or protein may activate an important gene. These individual relationships organize into larger biological networks. Many computational methods aim to predict these types of network relationships and which relationships control essential biological processes. This project will establish a computational framework called BeeHive to support running and comparing modern computational tools for studying biological networks. BeeHive will make it considerably easier to analyze biological data with these methods and evaluate their strengths and weaknesses. The framework will automatically update a website that tests top methods on a variety of biological use cases, which will provide important benchmarking and assessments for the network biology scientific community. The project will showcase BeeHive with biological applications in gene regulation, protein signaling, and chemical target networks. BeeHive will be used in undergraduate research experiences through a Summer Research Institute across the three project sites. The project will develop BeeHive, a general platform for multiple types of network biology workflows. BeeHive will provide a shared framework and modular components that implement common elements of network biology analyses including installation of algorithms, data pre-processing, cross-validation methods, and network visualization. The BeeHive infrastructure will enable running many network algorithms at scale from a single interface. This strategy will support rigorous benchmarking of network algorithms and greatly simplify testing multiple algorithms on a new biological dataset. The project will apply BeeHive to three representativeapplications, namely gene regulatory network inference, pathway reconstruction, and small molecule-protein target prediction applications. These problems are important in the genomics and bioinformatics research communities due to recent computational and biotechnological advances, such as graph neural networks and single-cell RNA-sequencing. Key components of BeeHive will include a modular and general purpose Python package that can be reused, a template Snakemake workflow to execute the shared steps of network biology analysis from data pre-processing through network visualization, a framework for continuous benchmarking that uses concepts from continuous integration in software engineering, Docker containers for tens of existing network biology algorithms, and datasets spanning yeast, mouse, human, and plants. Core objectives of BeeHive include advancing computational infrastructure for network biology analysis and benchmarking as well as creating an active and growing scientific community to create rigorous and standardized benchmarking frameworks and contribute methods and datasets to BeeHive. In the long term, the project will broadly generalize to other aspects of network biology and can catalyze analogous efforts in other domains in systems and computational biology. This project will train graduate students and create a Summer Research Institute that hosts six undergraduate researchers per year across the three project sites. Recruitment for the Summer Research Institute will emphasize broadening participation of students from historically marginalized groups. Results from this project will be available at https://bioinformatics.cs.vt.edu/~murali/beehive.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.

生物学的许多重要方面涉及细胞内分子之间的关系。例如，一种药物可能会关闭一种患病的蛋白质，或者蛋白质可能会激活一个重要的基因。这些个体关系组织成更大的生物网络。许多计算方法旨在预测这些类型的网络关系，以及哪些关系控制基本的生物过程。该项目将建立一个名为beehive的计算框架，以支持运行和比较用于研究生物网络的现代计算工具。蜂巢将使使用这些方法分析生物数据并评估其优势和劣势变得更加容易。该框架将自动更新一个网站，该网站测试各种生物用例的顶级方法，这将为网络生物科学界提供重要的基准和评估。该项目将展示蜂箱在基因调控、蛋白质信号和化学靶标网络方面的生物学应用。蜂巢将通过夏季研究所在三个项目地点用于本科生的研究体验。该项目将开发Beehive，这是一个用于多种类型的网络生物学工作流的通用平台。Beehive将提供一个共享框架和模块化组件，实现网络生物学分析的常见元素，包括安装算法、数据预处理、交叉验证方法和网络可视化。蜂窝基础设施将使许多网络算法能够从单个界面大规模运行。该策略将支持网络算法的严格基准测试，并极大地简化在新的生物数据集上测试多个算法。该项目将把蜂巢应用于三个具有代表性的应用，即基因调控网络推理、路径重建和小分子蛋白质靶标预测应用。由于最近的计算和生物技术的进步，如图形神经网络和单细胞RNA测序，这些问题在基因组学和生物信息学研究领域非常重要。BeeHave的关键组件将包括一个可重复使用的模块化和通用的Python包，一个用于执行从数据预处理到网络可视化的共享网络生物学分析步骤的模板Snakemake工作流，一个使用软件工程中持续集成概念的持续基准框架，用于数十个现有网络生物学算法的Docker容器，以及跨越酵母、鼠标、人类和植物的数据集。Beehive的核心目标包括推进网络生物学分析和基准测试的计算基础设施，以及创建一个活跃和不断增长的科学社区，以创建严格和标准化的基准测试框架，并为Beehive贡献方法和数据集。从长远来看，该项目将广泛推广到网络生物学的其他方面，并可以在系统和计算生物学的其他领域催化类似的努力。该项目将培养研究生，并创建一个暑期研究所，每年接待三个项目地点的六名本科生研究人员。暑期研究所的招聘将强调扩大来自历史上被边缘化群体的学生的参与。该项目的结果将在https://bioinformatics.cs.vt.edu/~murali/beehive.This上公布，该奖项反映了国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。