Modern science often requires the execution of large-scale, multi-stage simulation and data analysis pipelines to enable the study of complex systems. The amount of computation and data involved in these pipelines requires scalable workflow management systems that are able to reliably and efficiently coordinate and automate data movement and task execution on distributed computational resources: campus clusters, national cyberinfrastructures, and commercial and academic clouds. This paper describes the design, development and evolution of the Pegasus Workflow Management System, which maps abstract workflow descriptions onto distributed computing infrastructures. Pegasus has been used for more than twelve years by scientists in a wide variety of domains, including astronomy, seismology, bioinformatics, physics and others. This paper provides an integrated view of the Pegasus system, showing its capabilities that have been developed over time in response to application needs and to the evolution of the scientific computing platforms. The paper describes how Pegasus achieves reliable, scalable workflow execution across a wide variety of computing infrastructures.

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Top-down mass spectrometry (MS)-based proteomics enable a comprehensive analysis of proteoforms with molecular specificity to achieve a proteome-wide understanding of protein functions. However, the lack of a universal software for top-down proteomics is becoming increasingly recognized as a major barrier, especially for newcomers. Here, we have developed MASH Explorer, a universal, comprehensive, and user-friendly software environment for top-down proteomics. MASH Explorer integrates multiple spectral deconvolution and database search algorithms into a single, universal platform which can process top-down proteomics data from various vendor formats, for the first time. It addresses the urgent need in the rapidly growing top-down proteomics community and is freely available to all users worldwide. With the critical need and tremendous support from the community, we envision that this MASH Explorer software package will play an integral role in advancing top-down proteomics to realize its full potential for biomedical research.

Metabolomicsthe endpoint of the omics cascadeis increasingly recognized as a preferred method for understanding the ultimate responses of biological systems to stress. Flow injection electrospray (FIE) mass spectrometry (MS) has advantages for untargeted metabolic fingerprinting due to its simplicity and capability for high-throughput screening but requires a high-resolution mass spectrometer to resolve metabolite features. In this study, we developed and validated a high-throughput and highly reproducible metabolomics platform integrating FIE with ultrahigh-resolution Fourier transform ion cyclotron resonance (FTICR) MS for analysis of both polar and nonpolar metabolite features from plasma samples. FIE-FTICR MS enables high-throughput detection of hundreds of metabolite features in a single mass spectrum without a front-end separation step. Using plasma samples from genetically identical obese mice with or without type 2 diabetes (T2D), we validated the intra and intersample reproducibility of our method and its robustness for simultaneously detecting alterations in both polar and nonpolar metabolite features. Only 5 min is needed to acquire an ultra-high resolution mass spectrum in either a positive or negative ionization mode. Approximately 1000 metabolic features were reproducibly detected and annotated in each mouse plasma group. For significantly altered and highly abundant metabolite features, targeted tandem MS (MS/MS) analyses can be applied to confirm their identity. With this integrated platform, we successfully detected over 300 statistically significant metabolic features in T2D mouse plasma as compared to controls and identified new T2D biomarker candidates. This FIE-FTICR MS-based method is of high throughput and highly reproducible with great promise for metabolomics studies toward a better understanding and diagnosis of human diseases.

Top-down mass spectrometry (MS) is a powerful tool for the identification and comprehensive characterization of proteoforms arising from alternative splicing, sequence variation, and post-translational modifications. However, the complex data set generated from top-down MS experiments requires multiple sequential data processing steps to successfully interpret the data for identifying and characterizing proteoforms. One critical step is the deconvolution of the complex isotopic distribution that arises from naturally occurring isotopes. Multiple algorithms are currently available to deconvolute top-down mass spectra, resulting in different deconvoluted peak lists with varied accuracy compared to true positive annotations. In this study, we have designed a machine learning strategy that can process and combine the peak lists from different deconvolution results. By optimizing clustering results, deconvolution results from THRASH, TopFD, MS-Deconv, and SNAP algorithms were combined into consensus peak lists at various thresholds using either a simple voting ensemble method or a random forest machine learning algorithm. For the random forest algorithm, which had better predictive performance, the consensus peak lists on average could achieve a recall value (true positive rate) of 0.60 and a precision value (positive predictive value) of 0.78. It outperforms the single best algorithm, which achieved a recall value of only 0.47 and a precision value of 0.58. This machine learning strategy enhanced the accuracy and confidence in protein identification during database searches by accelerating the detection of true positive peaks while filtering out false positive peaks. Thus, this method shows promise in enhancing proteoform identification and characterization for high-throughput data analysis in top-down proteomics.