Apus enables automated end-to-end analysis and quality control of large-cohort proteomics data.

Translating large-cohort proteomics data into biomedical insights requires timely, scalable, and reproducible data analysis. Here, we present Apus, an end-to-end solution built on a distributed private cloud architecture that automates the entire data analysis workflow, covering real-time data aggregation, parallelized protein identification, high-throughput quantification, comprehensive quality control (QC), and machine learning-driven bioinformatics analysis. We demonstrate the utility of this workflow on a longitudinal experiment, with automated identification and QC in parallel with data generation, enabling real-time monitoring of experimental stability and proactive alerts for potential issues. In a clinical case study, we reanalyzed a 222-run cohort, quantified 6,844 proteins, and identified candidate biomarkers that showed promising discriminatory performance using integrated machine learning methods. Apus provides an automated, user friendly, and robust solution for critical bottlenecks in large-cohort proteomics.