App-GC

Data preprocessing pipeline for GC- and GCxGC-MS raw data

Description

App-GC is R-based pipeline that integrates open-source software to preprocess GC- and GCxGC-MS data. There are four main steps to process data with App-GC, 1) peak detection, 2) peak export, 3) peak alignment, and 4) peak annotation. App-GC contains various methods to dect peaks including CentWave, MatchedFilter, and eRah for GC-MS peak detection, and msPeak-NEB and msPeakG-Del1 for GcxGC peak detection. For peak alignment, there are two methods, i.e. GCalignR, and eRah-aign, for GC-MS data and one method, i.e. mSPA for GCxGC data. The final output from the pipeline is in single CSV text file, which is ready for subsequence analysis.

Installation and requirement

Manual installation (tested with Ubuntu 20.04) (not recommend)

Install R library (r-base=3.6.1) using anaconda and BiocManager

• anaconda

conda install -c r r-base=3.6.1
conda install -c bioconda bioconductor-metams
pip uninstall netCDF4 #in case libnetcdf error
conda install -c conda-forge "libnetcdf=4.6.2"
conda install -c r r-shiny
conda install -c conda-forge r-shinythemes
conda install -c conda-forge r-shinyfiles
conda install -c conda-forge r-shinyjs
conda install -c conda-forge r-plotly
conda install -c conda-forge r-htmlwidgets

• BiocManager library

install.packages("BiocManager")
BiocManager::install("xcms")
BiocManager::install("CAMERA")
BiocManager::install("GCalignR")
BiocManager::install("filesstrings")
BiocManager::install("msm")
BiocManager::install("pracma")
BiocManager::install("doSNOW")
BiocManager::install("erah")

• Download MSPepSearch tool and put it in working folder https://chemdata.nist.gov/dokuwiki/doku.php?id=peptidew:mspepsearch

e.g. B:\App-GC_1_0_docker\Working\2019_02_22_MSPepSearch_x32\MSPepSearch.exe

Installation using Docker (Recommend)

• 1. Clone App-GC script from github and move to App-GC folder

git clone [email protected]:asangphukieo/App-GC.git
cd App-GC

• 2. Pull app-gc docker containing (~8GB)

docker pull asangphukieo/app-gc

• 3. Run App-GC in commandline version

docker run -p 3838:3838 -v B:\App-GC_1_0_docker\App-GC\:/srv/shiny-server/ -v B:\App-GC_1_0_docker\Working:/var/log/shiny-server/ -it asangphukieo/app-gc:latest bash

or

• 3. Run App-GC in GUI version (Linux)

sudo docker run -p 3838:3838 -v /home/user/Downloads/App-GC/:/srv/shiny-server/ -v /home/user/Downloads/App-GC/Working:/var/log/shiny-server/ -it asangphukieo/app-gc:latest exec shiny-server 2>&1

• 3. Run App-GC in GUI version (Window)

docker run -p 3838:3838 -v B:\App-GC_1_0_docker\App-GC\:/srv/shiny-server/ -v B:\App-GC_1_0_docker\Working:/var/log/shiny-server/ -it asangphukieo/app-gc:latest exec shiny-server 2>&1

• Then, for GUI version go to your browser and type http://localhost:3838/App-GC_1.0/ to open the GUI.

Set up NIST library folder (comercial library)

put NIST library in working folder (mainlib and replib)

e.g. in window system B:\App-GC_1_0_docker\Working\NIST_library

Set up other library folder (non-comercial library)

Soon.

Usage

All parameters for the pipeline are in "parameter_config.yml" file. In the terminal screen, user can run the pipeline by single command as below

Rscript run_App-GC.R parameter_config.yml

Input

Now, App-GC supports only CDF file format. For other types, you might use open source software e.g. Openchrom (https://lablicate.com/platform/openchrom) to convert the file to CDF format.

License

MSPepSearch program may be redistributed without restriction. (https://chemdata.nist.gov/dokuwiki/doku.php?id=peptidew:mspepsearch#restrictions_and_disclaimers)

Contact us

If you have any enquiries to analyze your data, please contact us. We are happy to recieve any suggestions or comments.