Automated Collision Cross Section calculation software for ion mobility spectrometry-mass spectrometry
Automated Collision Cross Section calculation software for ion mobility-mass spectrometry
AutoCCS supports the following platforms and methods:
conda
environment (Recommended)Please install conda and create an environment from an environment.yml file. More details about managing the conda environment can be found on the Managing environments.
conda env create -f environment.yml
And then activate the new conda environment autoccs
.
conda activate autoccs
pip
Install Python 3.7 (or newer) [link] and use pip
as follows to install dependencies.
pip install -r requirements.txt
In this tutorial, we demonstrated the CCS determination using AutoCCS for the Agilent tune-mix samples in three different platforms: stepped-field DTIMS-MS, single-field RapidFire-DTIMS-MS, SLIM-based IMS.
Demo dataset is publicly available at MassIVE (Accession: MSV000085979)
For readability purposes, the input parameters are split over multiple lines. When using the command line, all parameters must be included as a single line.
python -u autoCCS.py
--target_list_file data/SteppedField-DTIMS/TargetList.csv
--config_file data/SteppedField-DTIMS/autoCCS_config.xml
--framemeta_files "data/SteppedField-DTIMS/ImsMetadata/*.txt"
--feature_files "data/SteppedField-DTIMS/Features_csv/*.csv"
--output_dir data/SteppedField-DTIMS/Results/
--threshold_n_fields 5
--mode multi &> "data/SteppedField-DTIMS/LogFiles/multi.log"
python autoCCS.py
--config_file data/SingleField-RapidFire-DTIMS/autoCCS_config.xml
--framemeta_files "data/SingleField-RapidFire-DTIMS/ImsMetadata/*.txt"
--feature_files "data/SingleField-RapidFire-DTIMS/Features_csv/*.csv"
--calibrant_file data/SingleField-RapidFire-DTIMS/TuneMix-CCS.txt
--output_dir data/SingleField-RapidFire-DTIMS/Results/
--tunemix_sample_type AgilentTuneMix
--sample_meta data/SingleField-RapidFire-DTIMS/Datasets.csv
--colname_for_sample_type SampleType
--colname_for_filename RawFileName
--colname_for_ionization IonPolarity
--degree 1
--single_mode batch
--mode single &> data/SingleField-RapidFire-DTIMS/LogFiles/single.log
python -u autoCCS.py
--config_file data/SLIM-IMS/autoCCS_config.xml
--feature_files "data/SLIM-IMS/Features_csv/*.csv"
--output_dir data/SLIM-IMS/Results/
--mode single
--calibrant_file data/SLIM-IMS/TuneMix-CCS_POS.txt
--sample_meta data/SLIM-IMS/Datasets.csv
--tunemix_sample_type AgilentTuneMix
--colname_for_sample_type SampleType
--colname_for_filename RawFileName
--colname_for_ionization IonPolarity
--single_mode batch
--degree 2
--calib_method poly
--ppm 150 &> data/SLIM-IMS/LogFiles/slim.log
Users are allowed to apply high-order polynomial functions: quadratic (--degree 2
), cubic (--degree 3
), quartic (--degree 4
), and so on.
--degree 3 # for cubic
Also, it allows users to apply non-linear regression based on the linearized power function.
--calib_method power
JY Lee, A Bilbao, CR Conant, KJ Bloodsworth, DJ Orton, M Zhou, … & TO Metz (2021). AutoCCS: Automated collision cross section calculation software for ion mobility spectrometry-mass spectrometry. Bioinformatics. DOI: 10.1093/bioinformatics/btab429, PMID 28763190
Written by Joon-Yong Lee for the Department of Energy (PNNL, Richland, WA)\
Copyright 2020, Battelle Memorial Institute. All Rights Reserved.\
E-mail: proteomics@pnnl.gov\
Website: https://github.com/PNNL-Comp-Mass-Spec/AutoCCS or https://www.pnnl.gov/integrative-omics
AutoCCS is licensed under the BSD 2-Clause License; License