nf-encyclopedia: A Cloud-Ready Pipeline for Chromatogram Library Data-Independent Acquisition Proteomics Workflows

Area of Science:

• Proteomics
• Mass Spectrometry
• Bioinformatics

Background:

• Data-independent acquisition (DIA) mass spectrometry enables comprehensive proteome quantification.
• Limited open-source tools exist for DIA proteomics analysis, especially those utilizing gas phase fractionated (GPF) chromatogram libraries.
• Existing tools often lack robust peptide and protein detection and quantification capabilities.

Purpose of the Study:

• To introduce nf-encyclopedia, an open-source NextFlow pipeline for DIA proteomics data analysis.
• To enable the use of chromatogram libraries for enhanced peptide detection and quantification.
• To provide a reproducible and scalable solution for DIA proteomics workflows.

Main Methods:

• Developed nf-encyclopedia, a NextFlow pipeline integrating MSConvert, EncyclopeDIA, and MSstats.
• Analyzed DIA proteomics experiments with and without GPF chromatogram libraries.
• Benchmarked pipeline reproducibility on cloud and local platforms.
• Assessed scalability for large-scale experiments using cloud parallelization.

Main Results:

• nf-encyclopedia demonstrates reproducible peptide and protein quantification across different computational environments.
• Integration with MSstats improved protein-level quantitative performance compared to EncyclopeDIA alone.
• The pipeline successfully scaled to large experiments by leveraging cloud computing resources.

Conclusions:

• nf-encyclopedia offers a robust, open-source solution for DIA proteomics data analysis.
• The pipeline enhances quantification accuracy and reproducibility.
• It provides a scalable and accessible platform for researchers using DIA mass spectrometry.