This description represents the new SQLite-based workflow that is currently in development. This version includes the IPF  and large-scale data set optimizations . You can alternatively follow the instructions for the PyProphet Legacy Workflow.
As the new workflow is still in development, ensure that all data is processed by the latest
OpenMS/develop and PyProphet versions.
Contact and Support¶
We provide support for PyProphet on the GitHub repository.
OSW output files according to section Integrated OpenSWATH Workflow. PyProphet is then applied to the one or several such SQLite-based reports. Several different commands can be run to consecutively to do the analysis:
pyprophet --help pyprophet merge --help
This command provides an overview of all available commands to manipulate OSW input files. Further instructions are available for the individual commands.
pyprophet merge --out=merged.osw \ --subsample_ratio=1 *.osw
In most scenarios, more than a single DIA / SWATH-MS run was acquired and the samples should be compared qualitatively and/or quantitatively with the OpenSWATH workflow. After individual processing with OpenSWATH and the identical spectral library, the files can be merged by PyProphet.
This command will merge and optionally subsample multiple files. Please note that the experiment-wide context on peptide query-level is applied to merged files, whereas the run-specific context is used with separate OSW files .
If semi-supervised learning is too slow, or the run-specific context is required, create an additional merged file with a smaller
subsample_ratio. The model will be stored in the output and can be applied to the full file(s).
pyprophet score --in=merged.osw --level=ms2
The main command will conduct semi-supervised learning and error-rate estimation in a fully automated fashion.
--help will show the full selection of parameters to adjust the process. The default parameters are recommended for SCIEX TripleTOF 5600/6600 instrument data, but can be adjusted in other scenarios.
When using the IPF extension, the parameter
--level can be set to
transition. If MS1 or transition-level data should be scored, the command is executed three times, e.g.:
pyprophet score --in=merged.osw --level=ms1 \ score --in=merged.osw --level=ms2 \ score --in=merged.osw --level=transition
The scoring steps on MS1 and transition-level have some dependencies on the MS2 peak group signals. The parameter
--ipf_max_peakgroup_rank specifies how many peak group candidates should be assessed in IPF. For example, if this parameter is set to 1, only the top scoring peak group will be investigated. In some scenarios, a set of peptide query parameters might detect several peak groups of different peptidoforms that should be independently identified. If the parameter is set to 3, the top 3 peak groups are investigated. Note that for higher values (or very generic applications), it might be a better option to disable the PyProphet assumption of a single best peak group per peptide query. This can be conducted by setting
feature_id and will change the assumption that all high scoring peak groups are potential peptide signals.
Importantly, PyProphet will store all results in the input OSW files. This can be changed by specifying
--out. However, since all steps are non-destructive, this is not necessary.
If IPF should be applied after scoring, the following command can be used:
pyprophet ipf --in=merged.osw
To adjust the IPF-specific parameters, please consult
pyprophet ipf --help. If MS1 or MS2 precursor data should not be used, e.g. due to poor instrument performance, this can be disabled by setting
--no-ipf_ms2_scoring. The experimental setting
--ipf_grouped_fdr can be used in case of extremly heterogeneous spectral library, e.g. containing mostly unmodified peptides that are mainly detect and peptidoforms with various potential site-localizations, which are mostly not detectable. This parameter will estimate the FDR independently group according to number of site-localizations.
Several thresholds (–ipf_max_precursor_pep,`–ipf_max_peakgroup_pep`,` –ipf_max_precursor_peakgroup_pep`,`–ipf_max_transition_pep`) are defined for IPF to exclude very poor signals. When disabled, the error model still works, but sensitivity is reduced. Tweaking of these parameters should only be conducted with a reference data set.
Contexts & FDR¶
To conduct peptide inference in run-specific, experiment-wide and global contexts, the following command can be applied:
pyprophet peptide --in=merged.osw --context=run-specific \ peptide --in=merged.osw --context=experiment-wide \ peptide --in=merged.osw --context=global
This will generate individual PDF reports and store the scores in a non-redundant fashion in the OSW file.
Analogously, this can be conducted on protein-level as well:
pyprophet protein --in=merged.osw --context=run-specific \ protein --in=merged.osw --context=experiment-wide \ protein --in=merged.osw --context=global
Finally, we can export the results to legacy OpenSWATH TSV report:
pyprophet export --in=merged.osw --out=legacy.tsv
By default, both peptide- and transition-level quantification is reported, which is necessary for requantification or
SWATH2stats. If peptide and protein inference in the global context was conducted, the results will be filtered to 1% FDR by default. Further details can be found by
pyprophet export --help.
By default, IPF results will be used if available. This can be disabled by setting
--no-ipf. The IPF results require different properties for TRIC. Please ensure that you want to analyze the results in the context of IPF, else, use the
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