This repository holds the source code for GradientOptimizer, a lightweight graphical user interface for calculating nonlinear gradients in Reversed-Phase Liquid Chromatography experiments. The software allows to calculate three types of nonlinear gradients: in silico-optimized, MS1-optimized, and gradients based on a custom retention time distribution.
The only requirement is to have java 1.7 (or higher) installed. You can download java here
Please download the file GradientOptimizer.jar here
If you calculate an in silico-optimized gradient or a gradient based on a custom retention time distribution, and you don't have tremendously large input files, you can just double-click the file GradientOptimizer.jar. The same is valid if you calculate an MS1-optimized gradient and you use a relattively small .mzML file as input (max a few hundred Mb).
If you calculate an MS1-optimized gradient and you have a large .mzML file as input, please run GradientOptimizer by opening a terminal window, navigating to the folder where you saved GradientOptimizer.jar, then typing:
java -Xmx2g -jar GradientOptimizer.jar
Note that 2g corresponds to allocating to GradientOptimizer a maximum of 2Gb RAM memory. Depending on the amount of memory available on your computer, and the size of your .mzML file, replace 2 with a reasonable value.
To open a terminal window:
- Ubuntu - press Ctrl+Alt+T
- OSX - navigate to /Applications/Utilities and choose Terminal
- Windows - follow the instructions given here
A series of tutorial videos illustrating how to calculate each type of nonlinear gradient is available at the links indicated below:
- in silico-optimized gradient
- MS1-optimized gradient
- Nonlinear gradient based on a custom retention time distribution
When calculating the MS1-optimized gradient, please pay special attention to the following aspects:
- Make sure to allocate sufficient memory when starting up the software (see Installation Instructions above)
- If possible, use centroided data. This will speed up the calculations.
- Use a reasonable intensity threshold. Note that a too low intensity threshold will probably lead to many noise peaks. Usually, the minimum intensity of a peak that is selected for fragmentation is a good choice.
For more information about nonlinear gradients, please check:
- Optimized nonlinear gradients for reversed-phase liquid chromatography in shotgun proteomics. Moruz L, Pichler P, Stranzl T, Mechtler K, Käll L. In Analytical Chemistry. 2013 Aug;85(16):7777-85. Pubmed
For information about the retention time prediction method Elude, please check:
Chromatographic retention time prediction for posttranslationally modified peptides. Moruz L, Staes A, Foster JM, Hatzou M, Timmerman E, Martens L, Käll L. In Proteomics. 2012 Apr;12(8):1151-9. PubMed
Training, selection, and robust calibration of retention time models for targeted proteomics. Moruz L, Tomazela D, Käll L. In J Proteome Res. 2010 Oct 1;9(10):5209-16. PubMed
If you experience any problems with running GradientOptimizer, or you have any questions related the use of the software, please contact Lukas Käll, firstname.lastname@example.org
Luminita Moruz, 1.03.2014