ABI Innovation: Scalable and Agile Analysis of Mass Spectrometry Experiments

质谱学是一种多种多样、用途广泛的技术，用于对复杂生物混合物中的蛋白质、小分子和代谢物进行高通量功能表征。该技术迅速发展，并生成越来越大的复杂性和大小的数据集。这一快速演变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演变相匹配。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多个包提供的丰富资源。然而，技术上的限制现在阻碍了它们在质谱学研究中的应用。作为回应，火箭项目建立了一项使能技术，用于处理R中的大型质谱学数据集，并迅速开发新的算法，同时受益于其他科学领域的进步。它还为招收和留住美洲原住民学生提供了从事基于R的技术和研究的机会，并帮助他们在STEM的职业生涯中做好准备。Rocket没有实施另一条数据处理管道，而是构建了一种使能技术，用于扩展R的可扩展性，并简化复杂格式的大文件操作。首先，为了解决质谱学社区的多样性，Rocket支持缩减分析(即，在相对便宜的硬件上处理大型数据文件，而无需将其完全加载到内存中)，以及放大(即，在云或多处理器上执行工作流)。其次，Rocket生成针对特定部署平台在后台编译的R和目标代码的高效混合。通过确保与特定于质谱学的开放数据存储标准的兼容性、支持多种硬件方案以及生成优化的代码，Rocket能够开发通用的分析方法。因此，火箭的目标是为更广泛的生命科学家社区普及基于R的数据分析，并为使用大数据集的基于R的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上公开提供，该奖项反映了美国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。

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.