DIA-NN, Spectronaut, MaxQuant or FragPipe? Choosing proteomics search software
If you have raw files and a question, the choice of search engine is the next decision. A practical comparison from a laboratory that uses all of them.
Match the engine to the data type
| Software | Best for | Notes |
|---|---|---|
| DIA-NN | DIA, library-free or library-based; large cohorts; Astral-class data | Open source; neural-network scoring; fast; predicted libraries (Demichev et al., Nat Methods 2020) |
| Spectronaut | DIA with sophisticated QC and directDIA; integrated statistics | Commercial licence; strong reporting and visualisation (Bruderer et al., Mol Cell Proteomics 2015) |
| MaxQuant | DDA label-free and SILAC; TMT reporter quantification; DIA via MaxDIA | Free; MaxLFQ; long-standing standard for DDA (Sinitcyn et al., Nat Biotechnol 2021) |
| FragPipe / MSFragger | DDA, TMT, open and semi-specific searches, glyco; DIA via MSFragger-DIA | Free; very fast; strong for PTM-open searches (Yu et al., Nat Commun 2023) |
| Proteome Discoverer | Vendor-integrated DDA/TMT pipelines; ptmRS localisation | Commercial; convenient on Thermo data |
If the concepts behind DIA are new to your group, the tutorial by Ludwig and colleagues (Mol Syst Biol 2018) remains the clearest open-access introduction to how DIA data are acquired and why library choice matters.
Questions that decide it
- Acquisition mode. DIA data belong in DIA-NN or Spectronaut; DDA and TMT data belong in MaxQuant, FragPipe or Proteome Discoverer. Mixing them is the commonest mistake.
- Library. For DIA, decide between a predicted (in silico) library, a sample-specific DDA library, or a hybrid. Predicted libraries are now competitive for human and mouse; organisms with poor models still benefit from an empirical library.
- Reproducibility. Open-source engines let a reviewer re-run the search. If the result will be published, record the version, the FASTA release and every non-default parameter.
- Downstream statistics. Whatever the engine, differential analysis belongs in a model-based framework (limma, MSstats or equivalent) with explicit handling of missing values, not in a spreadsheet t-test.
- Localisation. For PTM work, insist on site-localisation scores in the output; not every engine reports them by default.
What we do
DIA-NN is our default for DIA, with Spectronaut used when its QC and directDIA features justify the licence for a project. DDA and TMT go through MaxQuant, FragPipe or Proteome Discoverer depending on the modification and quantification scheme. Every search is delivered with its parameter file and the code used for statistics and figures.
Have data from another facility that was never properly analysed? We reanalyse raw files from any instrument.
References
- Ludwig et al. Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial. Mol Syst Biol 2018. Open access PubMed · DOI · Full text (PMC)
- Demichev V et al. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat Methods 2020;17:41-44. Open access PubMed · DOI · Full text (PMC)
- Bruderer et al. Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen-treated three-dimensional liver microtissues. Mol Cell Proteomics 2015. Open access PubMed · DOI · Full text (PMC)
- Sinitcyn et al. MaxDIA enables library-based and library-free data-independent acquisition proteomics. Nat Biotechnol 2021. Open access PubMed · DOI · Full text (PMC)
- Yu et al. Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform. Nat Commun 2023. Open access PubMed · DOI · Full text (PMC)
Citations verified against PubMed records; “Open access” marks papers whose full text is freely available via PubMed Central or the journal.
Written by the omicslink proteomics team. Reviewed November 2026.
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