DIA-NN Proteomics Software Review — Features, Performance, and Tutorial

Introduction

DIA-NN (Data-Independent Acquisition by Neural Networks) has rapidly become the most popular open-source software for analyzing DIA mass spectrometry data. Developed by Vadim Demichev, DIA-NN combines neural network-based signal processing with innovative algorithms to deliver exceptional sensitivity, speed, and quantitative accuracy.

Since its initial release, DIA-NN has been cited in thousands of publications and adopted by proteomics labs worldwide. This review covers what makes DIA-NN stand out, how it performs compared to alternatives, and how to get started using it.

What Is DIA and Why Does It Need Special Software?

In Data-Independent Acquisition (DIA), the mass spectrometer systematically fragments all peptide ions within defined m/z windows, rather than selecting individual peptides (as in DDA). This produces highly multiplexed MS2 spectra where fragments from multiple peptides overlap.

The challenge: deconvolving these complex spectra to identify and quantify individual peptides. This requires specialized algorithms that can:

• Extract specific peptide signals from complex backgrounds
• Score identifications against predicted or empirical spectral libraries
• Provide accurate quantification from extracted ion chromatograms

DIA-NN excels at all three tasks.

Key Features of DIA-NN

1. Neural Network-Based Scoring

DIA-NN uses a deep neural network to score peptide-spectrum matches. The network learns to distinguish true identifications from false ones based on multiple features:

• Fragment ion intensity correlations
• Retention time accuracy
• Mass accuracy
• Chromatographic peak shape
• Isotope pattern matching

This ML-based scoring consistently outperforms traditional statistical approaches.

2. Library-Free Analysis

One of DIA-NN's most powerful features is library-free mode:

• Generates an in silico spectral library from your FASTA database
• Uses deep learning to predict peptide retention times and fragmentation patterns
• No need to build an experimental library from DDA runs
• Performance rivals or exceeds library-based analysis

This dramatically simplifies the DIA workflow and eliminates the need for additional DDA experiments.

3. Predicted Spectral Libraries

DIA-NN integrates with deep learning-based spectrum prediction:

• Predicts MS2 fragmentation patterns for every peptide in your database
• Predicts retention times with high accuracy
• Predictions are specific to your LC-MS setup (via calibration)

4. Match Between Runs (MBR)

Like MaxQuant's MBR for DDA, DIA-NN can transfer identifications between runs:

• Reduces missing values across large sample sets
• Uses RT alignment and conservative scoring to minimize false transfers
• Particularly valuable for clinical cohort studies

5. Speed

DIA-NN is remarkably fast:

• 100+ raw files per day on a standard workstation
• Parallelization across CPU cores
• Efficient memory management

6. Plexing Support

DIA-NN supports multiplexed DIA (plexDIA/mDIA):

• Analyzes samples labeled with mTRAQ or similar reagents
• Increases throughput 2-3x by analyzing multiple samples per injection
• Maintains quantitative accuracy despite multiplexing

Performance Benchmarks

Protein Identification

On standard whole-proteome DIA datasets:

Platform	Proteins Identified	Peptides Identified
DIA-NN (library-free)	8,000-9,000	80,000-100,000
DIA-NN (with library)	8,500-10,000	90,000-120,000
Spectronaut	8,000-9,500	85,000-110,000
OpenSWATH	6,000-7,500	60,000-80,000

Benchmarks on human cell line data with 60-min gradients on Orbitrap or timsTOF instruments

Quantitative Accuracy

• CV (Coefficient of Variation): Typically <10% for proteins quantified across replicates
• Dynamic range: Accurate quantification across 4+ orders of magnitude
• Ratio accuracy: Correctly recovers known spike-in ratios

Processing Speed

• Single file: 5-15 minutes depending on complexity
• 100 files: 8-16 hours
• Significantly faster than Spectronaut for large datasets

How to Use DIA-NN: Step-by-Step

Installation

1. Download from github.com/vdemichev/DiaNN
2. Extract to a folder
3. Run DiaNN.exe (Windows) — no installation needed
4. Linux version also available

Basic Library-Free Workflow

Step 1: Load Raw Files

• Click Add raw or drag-and-drop your .raw, .d, or .mzML files
• DIA-NN auto-detects the instrument type and DIA scheme

Step 2: Set FASTA Database

• Click Add FASTA and select your organism's proteome
• DIA-NN will generate a predicted spectral library automatically

Step 3: Configure Parameters

Essential settings:

• Precursor charge range: 2-4 (standard)
• Precursor m/z range: Match your DIA method (e.g., 400-800)
• Fragment m/z range: 200-1800 (standard)
• Missed cleavages: 1-2
• Peptide length: 7-30
• Precursor FDR: 1%

Modifications:

• Fixed: Carbamidomethyl (C) — if IAA was used
• Variable: Oxidation (M), Acetyl (N-term)

Quantification:

• Quantification strategy: "Robust LC (high accuracy)" for most experiments
• Cross-run normalization: RT-dependent (recommended)
• MBR: Enable for cohort studies

Step 4: Run

• Click Run
• Monitor progress in the log window
• Output appears in the same folder as your raw files

Output Files

report.tsv — Main output with peptide and protein-level results:

• Protein.Group, Protein.Names, Genes
• Precursor.Quantity, Protein.Q.Value
• RT, Predicted.RT, Global.Q.Value

report.pg_matrix.tsv — Protein group quantity matrix (samples × proteins):

• Ready for downstream statistical analysis
• Log2 transform and analyze directly in R or Python

report.pr_matrix.tsv — Precursor-level quantity matrix

report.stats.tsv — Run-level statistics:

• Number of identifications per file
• Data quality metrics

Advanced: Command-Line Usage

DIA-NN can be run from the command line for batch processing:

diann.exe \
  --f sample1.raw --f sample2.raw \
  --fasta human.fasta \
  --lib "" \
  --threads 8 \
  --out report.tsv \
  --qvalue 0.01 \
  --matrices \
  --smart-profiling \
  --met-excision \
  --cut K*,R* \
  --missed-cleavages 2 \
  --min-pep-len 7 \
  --max-pep-len 30 \
  --min-pr-charge 2 \
  --max-pr-charge 4 \
  --unimod4

DIA-NN vs. Spectronaut vs. Other Tools

DIA-NN vs. Spectronaut

Feature	DIA-NN	Spectronaut
Cost	Free, open-source	Commercial (~$15K/year)
Speed	Faster	Slower for large datasets
Library-free	Excellent	Good
GUI	Functional	Polished, user-friendly
Visualization	Basic	Extensive built-in plots
Support	Community (GitHub)	Professional support
Accuracy	Comparable	Comparable
Single-cell	Supported	Supported

Verdict: DIA-NN offers comparable or superior performance to Spectronaut at no cost. Spectronaut has a better GUI and built-in visualization. For most academic labs, DIA-NN is the clear choice.

DIA-NN vs. OpenSWATH

OpenSWATH is another open-source DIA tool, but it typically identifies fewer proteins and requires more complex setup (PyProphet, msproteomicstools). DIA-NN has largely replaced OpenSWATH in most labs.

DIA-NN vs. MaxDIA

MaxQuant's DIA module (MaxDIA) was released later and generally shows lower performance than DIA-NN in benchmarks. MaxQuant remains the better choice for DDA data.

Tips for Best Results

Sample Preparation

• Clean samples produce better results than any software can fix
• Use consistent sample preparation across all samples
• Include QC samples to monitor instrument performance

Acquisition Method Optimization

• Window size and overlap significantly affect results — use narrow windows (4-8 m/z) if your instrument speed allows
• Gradient length: Longer gradients (90-120 min) generally yield more identifications
• Gas-phase fractionation: Can be used to build spectral libraries if needed

Analysis Tips

1. Start with library-free mode — it's simpler and often sufficient
2. Enable MBR for cohort studies to reduce missing values
3. Use the latest version — DIA-NN is actively developed with frequent improvements
4. Check the log file for warnings about mass calibration or RT alignment
5. Visualize your results — plot protein/peptide counts per file to identify outliers

Downstream Analysis

After DIA-NN processing:

• Load report.pg_matrix.tsv into R or Python
• Log2 transform protein quantities
• Filter proteins with too many missing values
• Normalize (median or quantile normalization)
• Impute remaining missing values
• Perform differential expression analysis (limma, t-test)

Common Issues and Solutions

Issue: Very few identifications

• Check that your DIA windows match the precursor m/z range settings
• Verify the FASTA database matches your organism
• Ensure mass accuracy settings are appropriate

Issue: High missing values

• Enable MBR
• Check for batch effects across runs
• Consider more stringent protein filtering

Issue: Poor quantitative reproducibility

• Check LC-MS stability (retention time drift?)
• Ensure samples are properly randomized across batches
• Use RT-dependent normalization

Conclusion

DIA-NN has earned its position as the leading DIA proteomics software through a combination of cutting-edge algorithms, exceptional performance, and zero cost. Its library-free mode has simplified the DIA workflow enormously, making advanced proteomics accessible to more labs.

Whether you're processing 10 samples or 10,000, DIA-NN delivers reliable protein identification and quantification. Combined with its active development and responsive community, it's an essential tool in any proteomics researcher's arsenal.

If you're still running DDA-only experiments, the combination of DIA acquisition and DIA-NN analysis might be the upgrade that transforms your research.

---

Related Reading

• 🔗 DIA vs DDA in Proteomics: A Comprehensive Comparison
• 🔗 How to Analyze Mass Spectrometry Data
• 🔗 How to Use MaxQuant Tutorial
• 🔗 Best Bioinformatics Tools 2026
• 💻 Best NVMe SSD 2026 Review