Data analysis tool for comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry.

Data processing and identification of unknown compounds in comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GC×GC/TOFMS) analysis is a major challenge, particularly when large sample sets are analyzed. Herein, we present a method for efficient treatment of large data sets produced by GC×GC/TOFMS implemented as a freely available open source software package, Guineu. To handle large data sets and to efficiently utilize all the features available in the vendor software (baseline correction, mass spectral deconvolution, peak picking, integration, library search, and signal-to-noise filtering), data preprocessed by instrument software are used as a starting point for further processing. Our software affords alignment of the data, normalization, data filtering, and utilization of retention indexes in the verification of identification as well as a novel tool for automated group-type identification of the compounds. Herein, different features of the software are studied in detail and the performance of the system is verified by the analysis of a large set of standard samples as well as of a large set of authentic biological samples, including the control samples. The quantitative features of our GC×GC/TOFMS methodology are also studied to further demonstrate the method performance and the experimental results confirm the reliability of the developed procedure. The methodology has already been successfully used for the analysis of several thousand samples in the field of metabolomics.

An integrative approach for biological data mining and visualisation.

The emergence of systems biology necessitates development of platforms to organise and interpret plentitude of biological data. We present a system to integrate data across multiple bioinformatics databases and enable mining across various conceptual levels of biological information. The results are represented as complex networks. Context dependent mining of these networks is achieved by use of distances. Our approach is demonstrated with three applications: full metabolic network retrieval with network topology study, exploration of properties and relationships of a set of selected proteins, and combined visualisation and exploration of gene expression data with related pathways and ontologies.

MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data.

SUMMARY: New additional methods are presented for processing and visualizing mass spectrometry based molecular profile data, implemented as part of the recently introduced MZmine software. They include new features and extensions such as support for mzXML data format, capability to perform batch processing for large number of files, support for parallel processing, new methods for calculating peak areas using post-alignment peak picking algorithm and implementation of Sammon's mapping and curvilinear distance analysis for data visualization and exploratory analysis. AVAILABILITY: MZmine is available under GNU Public license from http://mzmine.sourceforge.net/.