QuantPridePep

This pipeline provides MS1 peptides intensities for PRIDE project using moFF.

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Minimum Requirements

Required java version :

• moFF should be installed separately on your machine.

• GNU parallel should be installed in your system

Required python libraries for moFF :

• Python 3.6+
• pandas > 0.23
• numpy > 1.15.0
• argparse > 1.2.1
• scipy 1.1.0
• scikit-learn > 0.19
• pymzML > 2.0.3
• brain-isotopic-distribution > 1.3.2
• pyteomics > 3.5

Required library for MSFilereader by Thermo

• mono library version 4.2.1

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Structure of the pipeline and its components

The pipeline for each raw file present in the project runs the following steps:

• from each Thermo raw file, ms2extraction.exe extracts retention time and mz data for all MS2 spectra recorded. As output, it creates .moff2scan files

• starting from .moff2scan, input file for moFF are created and stored as ms2feat_input

• run moFF on the ms2feat_input file

• merging data contained in .mgf.omega.csv with the result of moFF _moFF_result.txt. As output, it creates _omega_quant.txt files that contain the MS1 intensities for all the ionbot identification. At this step we also save the output of moFF on all the MS2 spectra recorded in the standard output file of moFF (_moff_result.txt)

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Output

The ouput file with peptide MS1 intensities and their quality measures are produced in two formats :

• .ms1_quant file (they are basically standard moFF output file)

• _omega_quant.txt file (where along with the original identified peptide information, the moFF quantitative data is added)

Set-up and running

• Be sure that Java and mono are installed in your environment.
• Clone and install moFF on your machine :

git clone -b master --single-branch https://github.com/compomics/moff your_path/moFF

• Clone and install the pipeline :

git clone -b master --single-branch https://github.com/compomics/QuantPridePep.git your_path/moFF_pipeline

Open the file launch_pipeline.py and with any text editor adjust on lines 184-1187 the absolute path of your your_path/moFF and your_path/moFF_pipeline.

Use python launch_pipeline.py -h

  --f       	       file contains a list of valid PXDxxx id
  --docker_run         flag to activate/deactivate docker setting
  --output_location    input folder e.g where all PXDxx folder are located
  --input_location     output folder e.g where to all the moFF result for each project

Running the pipeline with the following command:

python launch_pipeline.py -f list_PXD_file --docker_run 0 --input_location your_input_folder --output_location your_output_folder >> high_level_log_output.txt

The folder your_input_folder should contain all the project folders (PXDxxxx) where are located the mgf and the raw file of each project.

In the output location, the pipeline will create for each project an output folder (PXDxxxx_moFF) where are located all the results and also all the intermediate files used.

Exhaustive pipeline logs are written in moFF_pride_pipeline.log instead of a higher level log is printed in the standard output.

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Structure of the output folder and its content

This part should be re-writed --> ! For every project correctly quantified the output folder PXDxxxx_moFF should contains the following files and sub folders:

• *.moff2start : all the information parsed by the original mgf files
• *.moff2scan : all the ms2 events
• *.ms2feat_input : input files ready to be processed by moFF
• moFF_output/ : all the moFf result and log file are stored in this folder
• *_moff_result_ms2id.txt : result of moFF associated with the mgf data
• *_moff_result : simply the result of moFf for all the ms2 spectra founded in the Thermo raw file
• result/ : this folder contains all the .ms1_quant and the mzTab file with the MS1 quant information added to the identified peptied provided in mzTAb file of the project.
• log_moFF : log of the moFF tasks run by GNU parallel
• log_ms2scan : log of the MS2 extraction from the raw file produced by GNU parallel