A Parallelization Strategy for the Time Efficient Analysis of Thousands of LC/MS Runs in High-Performance Computing Environment

Abstract

Combining robust proteomics instrumentation with high-throughput enabling liquid chromatography (LC) systems (e.g., timsTOF Pro and the Evosep One system, respectively) enabled mapping the proteomes of 1000s of samples. Fragpipe is one of the few computational protein identification and quantification frameworks that allows for the time-efficient analysis of such large data sets. However, it requires large amounts of computational power and data storage space that leave even state-of-the-art workstations underpowered when it comes to the analysis of proteomics data sets with 1000s of LC mass spectrometry runs. To address this issue, we developed and optimized a Fragpipe-based analysis strategy for a high-performance computing environment and analyzed 3348 plasma samples (6.4 TB) that were longitudinally collected from hospitalized COVID-19 patients under the auspice of the Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) study. Our parallelization strategy reduced the total runtime by ∼90% from 116 (theoretical) days to just 9 days in the high-performance computing environment. All code is open-source and can be deployed in any Simple Linux Utility for Resource Management (SLURM) high-performance computing environment, enabling the analysis of large-scale high-throughput proteomics studies.

Keywords: Fragpipe; HPC; SLURM; parallelization; proteomics; timsTOF.

Figure 1.

Schematic workflow that runs Fragpipe on a HPC with parallelized components to decrease total run time. For each computational tool CPU and RAM usage (node with 180GB RAM and 96 CPU cores), increase of disk usage and the required time is shown.

Figure 2.

Time requirements (minutes) to re-write 3348 BDF’s into the MSFragger mzBIN format for the four combinations of serial compared to parallelization (x 20) and Network storage against node storage.