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MHCquant

Workflow

MHCquant

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/

FDR calculation and Percolator refinement based on combined replicates and 5% threshold Input VCF containing mutations Consensus feature linking and ID conflictresolving between replicates Feature detection based onidentifications (FFid) Input replicate MZML files for coprocessing Comet database and decoy search MapAlignment based on identifications of all replicates Input allotypes Combine fasta database and mutations from VCF Output Predictions TSV Output MzTab Output CSV Input proteome fasta database Node 2Node 96Node 97Node 98Node 101Node 103Node 106Node 107Node 110Node 119Node 121Node 123Node 132Node 139Node 140Node 172Node 182Node 183Node 188Node 189Node 191Node 192Node 193Node 194Node 195Node 197Node 198Node 201Node 205Node 208Node 225 Input File MapAlignerIdentification ZipLoopStart CometAdapter PeptideIndexer IDMerger PSMFeatureExtractor ZipLoopStart ZipLoopEnd FeatureLinkerUnlabeledKD Input File IDFilter MapRTTransformer FalseDiscoveryRate ZipLoopEnd ZipLoopEnd ZipLoopStart Input File Variants2Proteins DecoyDatabase MzTabExporter TextExporter Input Files FeatureFinderIdentification IDConflictResolver Output Folder Output Folder EpitopePredicton Parse MzTab Output File PercolatorAdapter IDFilter Subset PSMs MapRTTransformer Combine fastaand VCF FDR calculation and Percolator refinement based on combined replicates and 5% threshold Input VCF containing mutations Consensus feature linking and ID conflictresolving between replicates Feature detection based onidentifications (FFid) Input replicate MZML files for coprocessing Comet database and decoy search MapAlignment based on identifications of all replicates Input allotypes Combine fasta database and mutations from VCF Output Predictions TSV Output MzTab Output CSV Input proteome fasta database Node 2Node 96Node 97Node 98Node 101Node 103Node 106Node 107Node 110Node 119Node 121Node 123Node 132Node 139Node 140Node 172Node 182Node 183Node 188Node 189Node 191Node 192Node 193Node 194Node 195Node 197Node 198Node 201Node 205Node 208Node 225Input File MapAlignerIdentification ZipLoopStart CometAdapter PeptideIndexer IDMerger PSMFeatureExtractor ZipLoopStart ZipLoopEnd FeatureLinkerUnlabeledKD Input File IDFilter MapRTTransformer FalseDiscoveryRate ZipLoopEnd ZipLoopEnd ZipLoopStart Input File Variants2Proteins DecoyDatabase MzTabExporter TextExporter Input Files FeatureFinderIdentification IDConflictResolver Output Folder Output Folder EpitopePredicton Parse MzTab Output File PercolatorAdapter IDFilter Subset PSMs MapRTTransformer Combine fastaand VCF

Download

Get this workflow from the following link: Download

Nodes

MHCquant consists of the following 82 nodes(s):

Plugins

MHCquant contains nodes provided by the following 7 plugin(s):