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PyProphet export (version
This file needs to be in OSW format (--in)
(--transition_quantification / --no-transition_quantification)
(--peptide / --no-peptide)
(--protein / --no-protein)
Needs to have columns with Filename, Condition, BioReplicate, Run
As an approximation, the q-values of multiple runs are averaged and supplied as argument FFT. Numeric from 0 to 1.
An m_score cutoff achieving and FDR smaller fdr_target will be selected. Calculated as FDR = decoys*FFT/targets
Number of measurements within at least one condition that have to pass the mscore threshold for this transition.
Maximum number of highest intense peptides to filter the data on.
Number of minimal number of peptide IDs associated with a protein ID in order to be kept in the dataset.

What it does

PyProphet: Semi-supervised learning and scoring of OpenSWATH results.

Export tabular (tsv) tables. 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.

Optional SWATH2stats output. SWATH2stats is intended to transform SWATH data from the OpenSWATH software into a format readable by other statistics packages while performing filtering, annotation and FDR estimation.

Study desing file for SWATH2stats

  • Tabular file with columns that are named: Filename, Condition, BioReplicate, Run.

  • The Filename should be part or the same as the original filenames used in OpenSWATH workflow

  • The Condition will be used for statistical analysis. In case multiple conditions are of interest for statistical analysis (e.g. diagnosis and age), this tool has to be run multiple times as SWATH2stats can only handle one condition at a time

  • The BioReplicate is corresponds to the biological replicate

  • The Run is the number of the MS run in which the sample was measured

  • Example for one replicate per patient

      Filename       Condition    BioReplicate     Run
    healthy1.mzml     healthy         1             1
    healthy2.mzml     healthy         2             2
    diseased1.mzml    diseased        3             3
    diseased2.mzml    diseased        4             4
  • Example for two replicates per patient

      Filename       Condition    BioReplicate     Run
    healthy1.mzml     healthy         1             1
    healthy2.mzml     healthy         1             2
    diseased1.mzml    diseased        2             3
    diseased2.mzml    diseased        2             4

PyProphet is a Python re-implementation of the mProphet algorithm (Reiter 2010 Nature Methods) optimized for SWATH-MS data acquired by data-independent acquisition (DIA). The algorithm was originally published in (Telemann 2014 Bioinformatics) and has since been extended to support new data types and analysis modes (Rosenberger 2017, Nature biotechnology and Nature methods).

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