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MSGF+ Pipeline

Workflow of the MSGF+ pipeline

MSGFPLUS Schema

Overview

Step Description Software Input Output
00 Extract reporter ion peaks from MS2 spectra and create Selected Ion Chromatograms for each MS/MS parent ion MASIC Thermo .Raw file _ReporterIons.txt and _SICStats.txt (plus several other files)
01 Convert Thermo .raw files to .mzML files (standard XML file format for MS data) MSConvert Thermo .Raw file .mzML file
02 Identify peptides using a fully tryptic search (for speed); these are used in Step 03 MS-GF+ .mzML file and .fasta file .mzid file
03a Use mass error histograms to in-silico re-calibrate the m/z values in the .mzML file mzrefiner filter in MSConvert .mzid file and .mzML file _FIXED.mzML
03b Plot the mass error histograms before and after in-silico recalibration PPMErrorCharter .mzid file and _FIXED.mzML file PNG files
04 Identify peptides using a partially tryptic search MS-GF+ _FIXED.mzML file and .fasta file .mzid file
05 Create a tab-separated value file listing peptide IDs from step 04 MzidToTSVConverter .mzid file .tsv file
06 Create tab-delimited files required for step 7; files contain peptide IDs, unique sequence info, and residue modification details PeptideHitResultsProcessor .tsv file _syn.txt file and several related files
07 Localize the position of Phosphorylation on S, T, and Y residues in phosphopeptides Ascore _syn.txt files, _FIXED.mzML file, .fasta file _syn_plus_ascore.txt file

Details

All the steps described here can be run in the containers available in the dockerfiles directory. Check the docker_readme.md to find out more.

The examples are based on the analysis of both protein global abundance and a post-translational modification (protein phosphorylation).

The commands available in every step are extracted from the WDL file. The variables between ${ } refers to the input, outputs, and parameter files. The names are self-explanatory. For example, ${raw_file} refers to a single raw file.

Step 00: MASIC

Extract reporter ion peaks from MS2 spectra and create Selected Ion Chromatograms for each MS/MS parent ion.

Container: prot-masic

mono /app/masic/MASIC_Console.exe \
/I:${raw_file} \
/P:${masic_parameter} \
/O:output_masic

Step 1: Convert .raw to .mzML files

Convert Thermo .raw files to .mzML files (standard XML file format for MS data)

  • Input: raw/*.raw
  • Output: msgfplus_input/*.mzML

Container: docker.io/chambm/pwiz-skyline-i-agree-to-the-vendor-licenses:3.0.22132-3ed3ab4

wine msconvert ${raw_file} \
--zlib \
--filter "peakPicking true 2-" \
-o output_msconvert

Step 2: Identify peptides using a fully tryptic search

Run on Docker MS-GF+ (prot-msgfplus) using the .mzML file from msconvert (step 1), get a .mzid file

  • Input directory/files:
    • *.mzML
    • sequence_db.fasta sequence db
    • MSGFPlus_Mods.txt config file
  • Output directory/file: msgfplus_output/*.mzid

Container: prot-msgfplus

java -Xmx4000M \
-jar /app/MSGFPlus.jar \
-s ${input_mzml} \
-o output_msgf_tryptic/${sample_id}.mzid \
-d ${seq_file_id}.fasta \
-conf ${msgf_tryptic_mzrefinery_parameter}

Step 3: Use mass error histograms to in-silico re-calibrate the m/z values

3A) mzrefiner filter in MSConvert

Use mass error histograms to in-silico re-calibrate the m/z values in the .mzML file. Run msconvert with the mzrefiner option to create a new .mzML file named _FIXED.mzML

Container: prot-msgfplus

wine msconvert ${input_mzml} \
-o output_msconvert_mzrefiner \
--outfile output_msconvert_mzrefiner/${output_name} \
--filter "mzRefiner ${input_mzid} thresholdValue=-1e-10 thresholdStep=10 maxSteps=2" \
--zlib

3B) PPMErrorCharter

Run PPMErrorCharter using the .mzid file from step 2a and the _FIXED.mzML file from step 3a

Container: prot-ppmerror

mono /app/PPMErrorCharterPython.exe \
-I:${input_mzid} \
-F:${input_fixed_mzml} \
-EValue:1E-10 \
-HistogramPlot:output_ppm_errorcharter/${sample_id}-histograms.png \
-MassErrorPlot:output_ppm_errorcharter/${sample_id}-masserrors.png \
-Python

Step 4: Protein Identification and Quantification

Identify peptides using a partially tryptic search

Run MS-GF+ using the _FIXED.mzml file from Step 3: That creates a .mzID file (called it Dataset_final.mzid).

Container: prot-msgfplus

java -Xmx4000M \
-jar /app/MSGFPlus.jar \
-s ${sample_id}.mzML \
-o output_msgf_identification/${sample_id}_final.mzid \
-d ${seq_file_id}.fasta \
-conf ${msgf_identification_parameter}

Step 5: MzidToTSVConverter

Create a tab-separated value file listing peptide IDs from step 04.

Run MzidToTSVConverter to convert Dataset_final.mzid to Dataset.tsv

Container: prot-mzid2tsv

mono /app/mzid2tsv/net462/MzidToTsvConverter.exe \
-mzid:${input_mzid_final} \
-tsv:output_mzidtotsvconverter/${output_name} \
-unroll -showDecoy

Step 6: PeptideHitResultsProcRunner

Create tab-delimited files required for step 7; files contain peptide IDs, unique sequence info, and residue modification details

Run PeptideHitResultsProcRunner using the .tsv file from step 5

Container: prot-phrp

mono /app/phrp/PeptideHitResultsProcRunner.exe \
-I:${input_tsv} \
-O:output_phrp \
-M:${phrp_parameter_m} \
-T:${phrp_parameter_t} \
-N:${phrp_parameter_n} \
-SynPvalue:${phrp_synpvalue} \
-SynProb:${phrp_synprob} \
-L:output_phrp/${phrp_logfile} \
-ProteinMods \
-F:${input_revcat_fasta}

Step 7: AScore (only PTMs)

Localize the position of Phosphorylation on S, T, and Y residues in phosphopeptides.

Inputs: _syn.txt, _FIXED.mzML, .fasta files

Container: prot-ascore

mono /app/ascore/AScore_Console.exe \
-T:msgfplus \
-F:${input_syn} \
-D:${input_fixed_mzml} \
-MS:${syn_ModSummary} \
-P:${ascore_parameter_p} \
-U:${seq_file_id}_syn_plus_ascore.txt \
-O:output_ascore \
-Fasta:${fasta_sequence_db} \
-L:output_ascore/${ascore_logfile}

Step 8: Relative Quantification

Step for processing TMT data proteomics data using PlexedPiper and generate reporter ion intensity and ratio tables.

Inputs:

  • MS/MS identifications from the MS-GF+ search engine
  • Reporter ion intensities extracted using MASIC
  • Tables outlining study design
    • table linking dataset to plexes
    • table linking reporter channels with sample names
    • table identifying reference within each plex

Script to run PlexedPiper: pp.R

Container: prot-plexedpiper

Rscript /app/pp.R \
-p ${proteomics_experiment} \
-i final_output_phrp \
-j final_output_masic \
-f ${fasta_sequence_db} \
-d ${sequence_db_name} \
-s study_design \
-o output_plexedpiper \
-n ${results_prefix} \
-c "${species}" \
-u ${unique_only} \
-r ${refine_prior}