Percolator [1], [2] is a popular algorithm for statistical validation of shotgun proteomic data. Using the OpenMS TOPP tool PercolatorAdapter, Percolator can score the results from OpenSWATH and thus be used instead of PyProphet.

Contact and Support

OpenSWATH support in PercolatorAdapter is currently in development and must NOT be used in production environments. We would however be very grateful for testing of the tool and reporting of problems and bugs.

We provide support for PercolatorAdapter using the OpenMS support channels. Please address general questions to the open-ms-general mailing list.

Please address any general Percolator inquiries to the Percolator team.



PercolatorAdapter is available in the OpenMS development branch. To convert the results of PercolatorAdapter to an OpenSWATH TSV report, the SQLite-enabled PyProphet version is required. Please install these versions according to the instructions in the Binaries section.

After installation, PercolatorAdapter can be run on the OSW results using the following commands:

# Score on MS2-level
PercolatorAdapter -in_osw openswath_results.osw -out openswath_results.osw \
-osw_level ms2

# Score on MS1-level
PercolatorAdapter -in_osw openswath_results.osw -out openswath_results.osw \
-osw_level ms1

# Score on transition-level
PercolatorAdapter -in_osw openswath_results.osw -out openswath_results.osw \
-osw_level transition


If IPF should be applied after scoring, the following command can be used:

pyprophet ipf --in=merged.osw

Finally, we can export the results to a legacy OpenSWATH TSV report:

pyprophet export --in=merged.osw --out=legacy.tsv \


[1]Käll L, Canterbury JD, Weston J, Noble WS, MacCoss MJ. Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nat Methods. 2007 Nov;4(11):923-5. Epub 2007 Oct 21. PMID: 17952086
[2]The M, MacCoss MJ, Noble WS, Käll L. Fast and Accurate Protein False Discovery Rates on Large-Scale Proteomics Data Sets with Percolator 3.0. J Am Soc Mass Spectrom. 2016 Nov;27(11):1719-1727. Epub 2016 Aug 29. PMID: 27572102