Abstract

To date, the processing of high-throughput Mass Spectrometry (MS) data is primarily accomplished using serial algorithms. Developing new methods to process MS data is an active area of research [1], but there is no single strategy that focuses on scalability of MS-based methods [2]. MS is a diverse and versatile technology for highthroughput functional characterization of proteins, small molecules, and metabolites in complex biological mixtures. In the recent years, the technology has rapidly evolved and is now capable of generating increasingly large (multiple terabytes per experiment)[1] and complex (multiple species/microbiome/high-dimensional) data sets [3]. This rapid advances in MS instrumentation must be matched by equally fast and rapid evolution of scalable methods developed for the analysis of these complex data sets. Ideally, the new methods should leverage the rich heterogeneous computational …