tools-only DIA Analysis - Usage Guide
Overview
Process data-independent acquisition (DIA) mass spectrometry data for comprehensive proteome quantification with fewer missing values than DDA.
Prerequisites
pip install pandas numpy
# CLI: DIA-NN (recommended), MSFragger-DIA, OpenSWATH
# Commercial: Spectronaut
Quick Start
Tell your AI agent what you want to do:
- "Run DIA-NN on my mzML files in library-free mode"
- "Process DIA data using a spectral library I built"
- "Load DIA-NN results and prepare for statistical analysis"
Example Prompts
Library-Free Analysis
"Run DIA-NN in library-free mode against the UniProt human FASTA with 1% FDR"
"Set up library-free DIA analysis with trypsin digestion and standard modifications"
"Process my DIA mzML files without a spectral library using deep learning prediction"
Library-Based Analysis
"Search my DIA data against the spectral library from my previous DDA experiments"
"Run DIA-NN with my Prosit-predicted library for targeted analysis"
"Use match-between-runs with my spectral library for improved coverage"
Parameter Configuration
"Configure DIA-NN with 10 ppm mass accuracy and 1 missed cleavage"
"Set up DIA analysis with phosphorylation as a variable modification"
"Enable two-pass analysis (reanalyse) for improved quantification"
Results Processing
"Load the DIA-NN report.tsv and create a protein abundance matrix"
"Filter DIA results to precursor q-value < 0.01 and protein q-value < 0.01"
"Compare protein identifications between library-free and library-based modes"
What the Agent Will Do
- Configure DIA-NN parameters (FASTA, library, tolerances)
- Run search in library-free or library-based mode
- Apply FDR filtering at precursor and protein level
- Export protein/precursor matrices
- Load results for downstream analysis
Library-Free vs Library-Based
| Mode | Description | Use When |
|---|---|---|
| Library-free | Deep learning predicts spectra | Quick analysis, no prior data |
| Library-based | Match to experimental spectra | Higher sensitivity for known targets |
| Hybrid | Predicted + empirical library | Best coverage for large studies |
Key Parameters
--qvalue 0.01- 1% FDR at precursor and protein level--reanalyse- Two-pass analysis for match-between-runs--smart-profiling- Improved quantification accuracy--min-fr-mz 200 --max-fr-mz 1800- Fragment ion range
Tips
- Library-free mode is often sufficient for discovery proteomics
- Use --reanalyse for better quantification across many samples
- DIA typically has fewer missing values than DDA
- Filter to 1% FDR at both precursor and protein levels
- For large cohorts, consider building a project-specific library