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Proteomics_​LFQ

Combines peptide identifications from X!Tandem and Comet.
Uses normalized feature intensities for peptide quantification.
No smart protein inference, all possible proteins are reported for each peptide.

Example input data:
To download the data from the archive linked below, check the box on the folder and click on the download button (arrow) on the top left.
Download and unpack the "Example_Data" data into a directory "Example_data" right inside your KNIME workspace.

URL: Home of the OpenMS plugin https://www.openms.de/
URL: Example Data https://abibuilder.cs.uni-tuebingen.de/archive/openms/Tutorials/

Basic data analysis. Peptide quantities Proteomics Identification and Quantification using a Consensus ID approach between twosearch toolsCombines peptide identifications from X!Tandem and Comet.Uses normalized feature intensities for peptide quantification.No smart protein inference, all possible proteins are reported for each peptide. Seperate human spike-inpeptides from backgroundSelect intensitiesLogarithmizeLogarithmizeSelect intensitiesFeaturedetectionMap IDson featuresNormalizeintensitiesRTalignmentLink correspondingfeaturesacross runsIdentificationsConsensus identificationsIdentificationsmapped to featuresSpectra (*.mzML)Database (*.fasta)Plot intensity distributionPlot intensity distributionZipLoopStart ZipLoopEnd Row Splitter Column Filter R Snippet R Snippet Column Filter FeatureFinderCentroided IDMapper ConsensusMapNormalizer MapAlignerPoseClustering FeatureLinkerUnlabeledQT ID and Consensus ID MzTabExporter MzTabReader MzTabReader MzTabExporter IDConflictResolver File Exporter File Exporter File Exporter File Exporter File Importer File Importer Box Plot Box Plot Histogram Histogram Basic data analysis. Peptide quantities Proteomics Identification and Quantification using a Consensus ID approach between twosearch toolsCombines peptide identifications from X!Tandem and Comet.Uses normalized feature intensities for peptide quantification.No smart protein inference, all possible proteins are reported for each peptide. Seperate human spike-inpeptides from backgroundSelect intensitiesLogarithmizeLogarithmizeSelect intensitiesFeaturedetectionMap IDson featuresNormalizeintensitiesRTalignmentLink correspondingfeaturesacross runsIdentificationsConsensus identificationsIdentificationsmapped to featuresSpectra (*.mzML)Database (*.fasta)Plot intensity distributionPlot intensity distributionZipLoopStart ZipLoopEnd Row Splitter Column Filter R Snippet R Snippet Column Filter FeatureFinderCentroided IDMapper ConsensusMapNormalizer MapAlignerPoseClustering FeatureLinkerUnlabeledQT ID and Consensus ID MzTabExporter MzTabReader MzTabReader MzTabExporter IDConflictResolver File Exporter File Exporter File Exporter File Exporter File Importer File Importer Box Plot Box Plot Histogram Histogram

Nodes

Extensions

Links

Download

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

Download Workflow
Created by: walzer
Created at: 2014-08-25
On NodePit since: 2019-08-04
Last update: 2024-12-18
Created with KNIME version: v5.2.1
Tags: OpenMSMass spectrometryPeptide identificationProtein identificationProtein quantification

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