Automated and Reproducible Data Analysis For Immunopeptidomics ##Description MHCquant is a bioinformatics analysis pipeline used for quantitative processing of data dependant (DDA) peptidomics data. It was specifically designed to analyse immunopeptidomics data, which deals with the analysis of affinity purified, unspecifically cleaved peptides that have recently been discussed intensively in the context of cancer vaccines. (Bassani-Sternberg et al., 2016 - https://www.nature.com/articles/ncomms13404) The workflow is based on the OpenMS C++ and Fred2.0 Immunonodes framework for computational mass spectrometry. RAW files (mzML) serve as inputs and a database search (Comet) is performed based on a given customized fasta protein database from vcf. FDR rescoring is applied using Percolator 3.0 based on a competitive target-decoy approach (reversed decoys). For label free quantification all input files undergo identification based retention time alignment (MapAlignerIdentification), and targeted feature extraction matching ids between runs (FeatureFinderIdentification). Ultimately MHC affinity predictions can be run in parallel and compared with the mass spectrometry search output. A concise test data set is available at the KohlbacherLab github account - https://github.com/KohlbacherLab/MHCquant-test-datasets. ##Input Mass Spectrometry Raw Data (mzML) Annotated Variant Calling Files (vcf) HLA Typing (table) ##Output Search Results (mzTab and txt) Affinity Prediction Results (table) ##Reference: Bichmann L. et al, Journal of Proteome Research, 22 Oct 2019, 18(11):3876-3884 ##Report Issues: Contact us on GitHub - https://github.com/OpenMS/OpenMS ##Funded by: deNBI - https://www.denbi.de/
Used extensions & nodes
Created with KNIME Analytics Platform version 4.0.2 Note: Not all extensions may be displayed.
By using or downloading the workflow, you agree to our terms and conditions.
Discussions are currently not available, please try again later.