ABI Innovation: Scalable and Agile Analysis of Mass Spectrometry Experiments

质谱法是一种多样化和通用的技术，用于复杂生物混合物中蛋白质、小分子和代谢物的高通量功能表征。该技术迅速发展，并产生越来越大的复杂性和规模的数据集。这种快速发展必须与为分析这些数据而开发的统计方法和工具的同样快速发展相匹配。理想情况下，新的统计方法应该利用R编程语言及其Bioconductor项目中实现的12,000多个包中的丰富资源。然而，技术限制现在阻碍了它们在质谱研究中的应用。作为回应，ROCKET项目建立了一种使能技术，用于在R中处理大型质谱数据集，并快速开发新算法，同时受益于其他科学领域的进步。它还提供了一个招聘和留住美国原住民学生的机会，让他们从事基于r的技术和研究，并帮助他们为STEM领域的职业生涯做好准备。ROCKET没有实现另一个数据处理管道，而是构建了一种支持技术来扩展R的可伸缩性，并简化复杂格式的大文件操作。首先，为了解决质谱社区的多样性，ROCKET支持按比例缩小分析（即，在相对便宜的硬件上处理大型数据文件，而不将它们完全加载到内存中），以及按比例扩大（即，在云或多处理器上执行工作流）。其次，ROCKET生成R和目标代码的有效混合，这些代码在后台为特定的部署平台编译。通过确保与质谱特定开放数据存储标准的兼容性，支持多种硬件场景，并生成优化代码，ROCKET能够开发通用分析方法。因此，ROCKET旨在为更广泛的生命科学家社区提供基于r的数据分析的民主化访问，并为基于r的大型数据集计算的新范式创建蓝图。该项目的结果将被记录下来，并在https://olgavitek-lab.ccis.northeastern.edu/This上公开发布。该奖项反映了美国国家科学基金会的法定使命，并通过基金会的智力价值和更广泛的影响审查标准进行评估，认为值得支持。

Mass spectrometry is a diverse and versatile technology for high-throughput functional characterization of proteins, small molecules and metabolites in complex biological mixtures. The technology rapidly evolves and generates datasets of an increasingly large complexity and size. This rapid evolution must be matched by an equally fast evolution of statistical methods and tools developed for analysis of these data. Ideally, new statistical methods should leverage the rich resources available from over 12,000 packages implemented in the R programming language and its Bioconductor project. However, technological limitations now hinder their adoption for mass spectrometric research. In response, the project ROCKET builds an enabling technology for working with large mass spectrometric datasets in R, and rapidly developing new algorithms, while benefiting from advancements in other areas of science. It also offers an opportunity of recruitment and retention of Native American students to work with R-based technology and research, and helps prepare them in a career in STEM.Instead of implementing yet another data processing pipeline, ROCKET builds an enabling technology for extending the scalability of R, and streamlining manipulations of large files in complex formats. First, to address the diversity of the mass spectrometric community, ROCKET supports scaling down analyses (i.e., working with large data files on relatively inexpensive hardware without fully loading them into memory), as well as scaling up (i.e., executing a workflow on a cloud or on a multiprocessor). Second, ROCKET generates an efficient mixture of R and target code which is compiled in the background for the particular deployment platform. By ensuring compatibility with mass spectrometry-specific open data storage standards, supporting multiple hardware scenarios, and generating optimized code, ROCKET enables the development of general analytical methods. Therefore, ROCKET aims to democratize access to R-based data analysis for a broader community of life scientists, and create a blueprint for a new paradigm for R-based computing with large datasets. The outcomes of the project will be documented and made publicly available at https://olgavitek-lab.ccis.northeastern.edu/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.