Visualization and differential peptide pattern analysis from LC/MS datasets.

Julia Löcherbach, Lars Linsen, Matthias Berth, and Jörg Bernhardt

Decodon GmbH, Greifswald
Department of Mathematics and Computer Science, Ernst-Moritz-Arndt-Universität Greifswald
Department of Microbiology, Ernst-Moritz-Arndt-Universität Greifswald


Differential proteome analysis reveals the main players in disease 
development or environmental adaptation of living systems on the final 
level of gene expression. Besides 2D gel electrophoresis that is 
commonly used in the lab currently gel-free techniques like 
(multidimensional) liquid chromatography and subsequent MS or tandem MS 
analysis are establishing in the laboratory practice.

While 2D gel electrophoresis requires posterior protein spot preparation 
and identification, the MS-coupled chromatography approaches are able to 
execute sample separation and peak identification simultanously during 
the same experimental setup. The high amount of collected data needs to 
be interpreted by sophisticated data base search algorithms that need 
high performance computer systems and high storage capacity.

We present an interactive visualization tool for the exploration of 
LC/MS data in a 3D space, which allows for the understanding of the data 
in its entirety and a detailed analysis of regions of interest. For 
efficiency purposes we apply an adaptive and peak-preserving multiresolution 
data resampling that allows us to handle and display the 
LC/MS data in interactive time on a commondity desktop computer system.

The software for visualization and analysis of LC/MS data we 
introduce with our experimental tool can compensate for typical 
chromatography artifacts like variations in retention time. Furthermore 
problems with the sample separation are maybe indicated to prevent 
misinterpretations of data with insufficient quality.

Additionally we show how the innovative data processing algorithms may 
generate differential expression patterns over two LC/MS datasets from 
differentially treated samples or from datasets derived from 
multidimensional LC-fractions.