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Protein label-free quantification

Quantification of protein levels from mass-spectrometry experiments

This workflow performs a quantification of proteins from (label-free) mass-spectrometry data. The final results are relative comparisons of protein levels across conditions to check for significant changes.
It uses components for the OpenMS plugin for compactness (green) and provides interactive configuration as well as exploration of the results using the R packages PTXQC, MSstats together with JavaScript nodes (blue).

Load an experimental designMore information can be found in the OpenMS documentation, see the Linkssection in the workflow description. Visual ConfigurationRight-click -> Open interactive view to configure the most important settings for the rest of the workflow. Input filesSpecify a folder with all the mzML files of the experiment.Specify a protein database in fasta format Actual per-input-file processing workflow for spectrum identification. This part should not require any user interaction! Visualization of results Performs feature finding, map alignment,peptide quantification, protein inference and rudimentaryprotein quantification and provides various outputs. This workflow performs a full label-free quantification of proteins from mass-spectrometry data. The final results are relative comparisons of protein levels across conditions to check for significant changes.It uses components for the OpenMS plugin for compactness (green) and provides interactive configuration as well as exploration of the results using the R packages PTXQC, MSstats together with JavaScript nodes (blue).Download the data from the Links in the workflow description and follow the notes along the workflow. For more information see the workflow metadata. Find it here: View -> Description CAUTION: This node needs pandoc to beinstalled and visible inside the R node. Configurepandoc path if necessary. Select a few proteins of interest in the table orthe volcano plot, to be displayed in more detailin the next node. Read sequencesfor visualizationMSstats-provided QC plotsIterate overspectrum filesCollect IDsUsing PTXQCPerform more sophisticatedprotein quantification usingMSstats 3.xCalculate groupcomparisons of interest(currently all pairwise comparisons)Read output mzTabsections into differenttablesRuns peptidesearch engine(s)Rescoring withPercolator or IDPEPPerforms ConsensusIDif multiple search engines were chosenPSM-level FDR filteringbefore exportFASTA Reader R View (Workspace) Load existingexperimental design ProteomicsLFQ File Cells to Port ZipLoopStart ZipLoopEnd Quality ControlReport Load into R R to Table VolcanoPlot(plotly) MzTabReader Append decoydatabase URI to Port URI to Port Search EngineCombination PSM Rescoring Consensus ID FDR Filtering Visualizeselected proteins Load input spectra Load input database URI to Port URI Port toVariable Choose searchengines & settings Configure rescoringprocedures ConfigureConsensusID ProteomicsLFQconfiguration Set non-standardPandoc path Load an experimental designMore information can be found in the OpenMS documentation, see the Linkssection in the workflow description. Visual ConfigurationRight-click -> Open interactive view to configure the most important settings for the rest of the workflow. Input filesSpecify a folder with all the mzML files of the experiment.Specify a protein database in fasta format Actual per-input-file processing workflow for spectrum identification. This part should not require any user interaction! Visualization of results Performs feature finding, map alignment,peptide quantification, protein inference and rudimentaryprotein quantification and provides various outputs. This workflow performs a full label-free quantification of proteins from mass-spectrometry data. The final results are relative comparisons of protein levels across conditions to check for significant changes.It uses components for the OpenMS plugin for compactness (green) and provides interactive configuration as well as exploration of the results using the R packages PTXQC, MSstats together with JavaScript nodes (blue).Download the data from the Links in the workflow description and follow the notes along the workflow. For more information see the workflow metadata. Find it here: View -> Description CAUTION: This node needs pandoc to beinstalled and visible inside the R node. Configurepandoc path if necessary. Select a few proteins of interest in the table orthe volcano plot, to be displayed in more detailin the next node. Read sequencesfor visualizationMSstats-provided QC plotsIterate overspectrum filesCollect IDsUsing PTXQCPerform more sophisticatedprotein quantification usingMSstats 3.xCalculate groupcomparisons of interest(currently all pairwise comparisons)Read output mzTabsections into differenttablesRuns peptidesearch engine(s)Rescoring withPercolator or IDPEPPerforms ConsensusIDif multiple search engines were chosenPSM-level FDR filteringbefore exportFASTA Reader R View (Workspace) Load existingexperimental design ProteomicsLFQ File Cells to Port ZipLoopStart ZipLoopEnd Quality ControlReport Load into R R to Table VolcanoPlot(plotly) MzTabReader Append decoydatabase URI to Port URI to Port Search EngineCombination PSM Rescoring Consensus ID FDR Filtering Visualizeselected proteins Load input spectra Load input database URI to Port URI Port toVariable Choose searchengines & settings Configure rescoringprocedures ConfigureConsensusID ProteomicsLFQconfiguration Set non-standardPandoc path

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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Created by: pfeuffer
Created at: 2016-02-10
On NodePit since: 2022-01-11
Last update: 2024-11-17
Created with KNIME version: v4.4.1
Tags: mass-spectrometryOpenMSproteomicsdifferential expression

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