# JointAnalysis
docker pull meiyulab/jointanalysis:1.0.0

# OpenSwath
docker pull ghcr.io/openms/openms-executables:3.1.0

# Pyprophet
docker pull pyprophet/pyprophet:2.2.5

# MRGD
docker pull meiyulab/mrgd:1.0.0

First, please execute the following command in your terminal (PowerShell, if your machine is based on Windows system) to clone the Diamond repository from my GitHub to your own machine.

git clone https://github.com/yachliu/JointAnalysis.git

(1) download the example MS data. Provided here are the two raw files, please visit Raw01, Raw02 respectively, download and store them in the /JointAnalysis/data/rawdata folder. This step will take some minutes. Then you need to convert the .raw data to .mzML data using the MSConvertGUI application from ProteoWizard. You can refer to ConvertRawToMzML.

(2) The library file, irt file, windows file and database file have been stored in the /JointAnalysis/data/ folder. Note that the library file and the irt file are in a compressed format, so execute the following commands to decompress them.

cd /path/to/JointAnalysis/data
gunzip ./library.pqp.gz
gunzip ./irt.tsv.gz

After all the data is ready, an example tree structure diagram of the /JointAnalysis/data folder is as follows:

JointAnalysis is containerized by Docker into an image, the installation tutorial of Docker is described in the Docker documentation (both for Linux and Windows). On your machine, please start a Terminal (PowerShell) session and then execute the following command within the console:

docker pull meiyulab/jointanalysis:1.0.0

This will take a few minutes to pull the Diamond image from Docker Hub to your machine. You can check whether the image meiyulab/jointanalysis:1.0.0 is successfully pulled by executing docker images, and if successfully, it will appear in the images list.

Create a container (named JointAnalysis_test) based on the image meiyulab/analysis:1.0.0 and simultaneously mount the local folder /path/to/JointAnalysis to the folder /path/to/JointAnalysis (in the container) by running the following command in your terminal:

docker run -it --name JointAnalysis_test -v /path/to/JointAnalysis:/path/to/JointAnalysis meiyulab/jointanalysis:1.0.0 bash

Please change /path/to/JointAnalysis to your own path. The path on your machine should be exactly the same as the path in the container.

After the above command is executed, you will enter the container. Please switch to the folder /path/to/JointAnalysis by executing cd /path/to/JointAnalysis in your terminal.

Note: Type in exit and press Enter, or hit Ctrl+D to exit the container. To re-enter the container after exiting, please follow the commands below :

docker start JointAnalysis_test
docker exec -it JointAnalysis_test bash

The Nextflow script is saved as a pipeline.nf file in the JointAnalysis folder. JointAnalysis's execution commands are as follows.

Execute the following command in your terminal to start the analysis of MS data by providing an assay library:

nextflow run /path/to/JointAnalysis/pipeline.nf  --rawData "/path/to/JointAnalysis/data/rawdata/" --library "/path/to/JointAnalysis/data/library.pqp" --tr_irt "/path/to/JointAnalysis/data/irt.tsv" --swath_windows_file "/path/to/JointAnalysis/data/win.tsv" --outputDir "/path/to/JointAnalysis/results"

Please change /path/to/JointAnalysis to your own path. Also the filename.

Note: The --outputDir parameter specifies the storage location of the data processing intermediate results. The final peptide identification results are saved in the outdir folder, named jointAnalysis_results.tsv. Please refer to the Help Message section or execute nextflow run /path/to/JointAnalysis/pipeline.nf --help in the container to view the detailed information of parameter passing.

nextflow run /path/to/JointAnalysis/pipeline.nf --help

nextflow run /path/to/JointAnalysis/pipeline.nf --rawData "" --library "" --tr_irt "" --swath_windows_file "" --outputDir "" <Options> <Functions>

Note: We process the MS data on a machine with a 64-core CPU and 256G memory. The greater the number of parallel data processing, the higher the memory and CPU resources consumed. If the memory is insufficient, you can appropriately reduce the number of parallel data processing.

Please cite this.