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: 2233969
• 负责人: Anna Ritz
• 金额: $ 9.25万
• 依托单位: Reed College
• 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=2233969&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 representative applications, 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，药物可能会关闭患病的蛋白质，或者蛋白质可能会激活重要的基因。这些个人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生物学过程。该项目将建立一个称为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。 Beehive将使使用这些方法分析生物学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，该网站测试有关各种生物用例的最佳方法，该方法将为网络生物学科学界提供重要的基准测试和评估。该项目将在基因调节，蛋白质信号和化学靶网络中使用生物学应用来展示蜂巢。 Beehive将通过三个项目地点的夏季研究所在本科研究经验中使用。该项目将开发Beehive，这是一种通用平台，用于多种网络生物学工作流程。 Beehive将提供共享的框架和模块化组件，以实现网络生物学分析的共同元素，包括安装算法，数据预处理，交叉验证方法和网络可视化。 Beehive基础架构将使单个接口大规模运行许多网络算法。该策略将支持网络算法的严格基准测试，并在新的生物学数据集中大大简化测试多种算法。该项目将应用于三种代表性应用，即基因调节网络推断，途径重建和小分子 - 蛋白质目标预测应用。由于最近的计算和生物技术进步，这些问题在基因组学和生物信息学研究群落中很重要，例如图神经网络和单细胞RNA-sequing。 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 植物。 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架构，以及创建一个积极而不断发展的科学界，以创建严格和标准化的基准测试框架，并为Behive提供贡献方法和数据集。从长远来看，该项目将广泛地概括为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中催化类似的努力。该项目将培训研究生，并创建一个夏季研究所，该研究所每年在三个项目地点举办六名本科研究人员。夏季研究所的招聘将强调从历史边缘化群体中扩大学生的参与。该项目的结果将在https://bioinformatics.cs.vt.edu/~mmurali/beehive..this奖中获得，反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估标准通过评估来获得支持的。