Label-free quantitative analysis

Proteios SE has functionality for label-free quantification using precursor intensities for quantification. The workflow is outlined below. The second tutorial at also includes the workflow.

  1. Upload peak lists and import these to the hits report. MGF files can be used for identification only. mzML files can be used both for identification and quantification. It is possible to import MGF files in this step, and then use mzML files with the same name (apart from the suffix) for the feature detection (see below).
  2. Perform the identification searches using the peak lists that were registered in the previous step and import the results to the hits report
  3. Create a combined hits report to obtain combined FDR values (q-values)for the peptide identifications
  4. Perform feature detection using msInspect or OpenMS on mzML or mzXML files. This can be done directly from Proteios SE, and then the features are imported directly - in this case skip the next step.
  5. Upload the .peptides.tsv files, and the mzML or mzXML files used for feature finding. In the case of mzML files, they can be used both for the peptide identification and the quantification. Check all the .peptides.tsv files and extensions->import file[s]->msInspect feature importer (or another importer, for example tab feature importer if other feature detection software was used).
  6. Optionally Open up the Feature report and mine features that have been imported(PROJECT_NAME->reports->Features)
  7. Match features with identifications. This is most easily done from the project overview (PROJECT_NAME->Overview), by pressing 'Match Features and Hits' under Report Tools. Alternatively: Press the 'Match-Feature-Hits' button at the bottom of the Feature report (See the step above).
    • When the job is done, MS/MS identifications in the Hits report will contain precursor quantity values, and the features will have sequence information where available. There is a log file generated with information about the matching of features and MS/MS identifications.
  8. Optionally, but advised! Align features and propagate identifications between files: Run the 'Propagate Feature Identitites' plug-in, which can be started from the project overview or from the bottom of the Feature report. This will run the alignment method described in Sandin et al 2013.
    • When the alignment job is finished you can check the log file to assess the quality of the alignment.
  9. A quantitative peptide report can then be generated by selecting 'Create Feature Report' at the bottom of the Feature Report.
    • The generated report is tab separated and can be used for differential expression anaysis in external software. The report contains headers with sample name, fraction and replicate information where applicable. If you which to conduct differential expression analysis at the peptide level, the following steps are not needed.
  10. Optionally run Proteios Protein Assembly
    • During the protein assembly process, the generated proteins will get a precursor intensity which is the sum of the precursor intensities of the included peptides. Note that which peptides will be included is based on the settings selected for protein assembly.
  11. Run Hits comparison report (with quantitative option checked) to generate report where peptides or proteins (Protein Assembly required for the latter) are compared between sample groups based on precursor quantities.

After these steps, results can be exported from the hits table or feature table for further analysis. It is also possible to compare two sample groups using the hits comparison report.

In addition, there are old plugins that use msInspect alignement output as input, and will compare groups of samples. It is possible to generate inclusion lists using these plugins, but they are not maintained.

Last modified 2 years ago Last modified on Apr 10, 2015, 4:17:43 PM