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.
####System Requirements 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, email@example.com
Luminita Moruz, 1.03.2014