DIAproteomics

DIAproteomics processes data-independent acquisition (DIA) mass spectrometry datasets to enable quantitative proteomics and peptidomics analyses.

Key Features:

• Automated processing: Provides automated, high-throughput handling of large-scale proteomics and peptidomics DIA datasets.
• Workflow implementation (Nextflow): Implemented in Nextflow to enable scalable execution across computational infrastructures.
• OpenSwathWorkflow integration: Uses OpenSwathWorkflow for DIA spectral library searches.
• PyProphet integration: Uses PyProphet to assess false discovery rates.
• Spectral library generation (from DDA): Supports creation of spectral libraries from existing data-dependent acquisition (DDA) data.
• Retention time and chromatogram alignment: Performs retention time and chromatogram alignment to support consistent quantitative comparison.
• Statistical post-processing and outputs: Produces annotated tables, diagnostic visualizations, and computes fold-changes across pairwise conditions predefined in the experimental design.

Scientific Applications:

• Quantitative proteomics: Quantitative analysis of proteins from DIA mass spectrometry datasets.
• Peptidomics: Analysis of peptidomics datasets acquired by DIA mass spectrometry.
• Large-scale biomedical DIA studies: Processing and comparative quantification of large-scale DIA datasets for biomedical research requiring precise and reproducible protein quantification.

Methodology:

Implemented in Nextflow; integrates OpenSwathWorkflow for DIA spectral library searches and PyProphet for false discovery rate assessment; supports spectral library generation from DDA data, retention time and chromatogram alignment, and statistical post-processing to produce annotated tables, diagnostic visualizations, and fold-change calculations across pairwise experimental conditions.