Spectrolyzer M for LC-MS

Although we can observe the abundance and distribution of proteins by mass spectrometry, the visible consequences of the functional proteome changes are shown by the appearance of particular metabolites. Since these metabolome changes can be observed immediately as the response to a given factor, it is essential to identify metabolites effectively.

• Spectrolyzer M for LC-MS is based on the OpenMS software, and hence it is a complete preprocessing tool and does not require any other software like Matlab or R to help out with data processing.
• Using Spectrolyzer M, raw LC-MS data can be prepared for comprehensive statistical analyses, which in turn can result in the discovery of interesting molecules (biomarker candidates) by differentiating between different groups, e.g. patient samples and control samples.
• The discovery of interesting metabolites can be followed by METLIN
• database searches for molecules identification, with the help of batch search embedded in the software.

A software package for LC-MS-based label-free quantitative metabolics

Spectrolyzer M can perform all stages of processing required for analyzing large sets of LC-MS (HPLC-ESI MS, MS/MS) samples. The software contains the following preprocessing methods:

• signal processing,
• noise and baseline filtering,
• feature detection,
• map aligning,
• feature grouping.

Thereafter, Spectrolyzer is able to perform different analyses, e.g. build diagnostic models, detect features for biomarker candidates, and perform metabolites identification.

Integration with Open MS software

In collaboration with Professor Knut Reinert (from Free University of Berlin) and Professor Oliver Kohlbacher (from Eberhard Karls University of Tübingen) we have combined our Spectrolyzer software for multivariate statistics with OpenMS — a C++ library for LC-MS data management and analysis.

Statistical analysis

Using Spectrolyzer, raw LC-MS data can be prepared for comprehensive statistical analyses. This includes various one- and multi-dimensional data exploration tools as well as advanced multivariate statistical methods.

Biomarker discovery has been given special attention and we can boldly say that our software offers best biomarker discovery solutions on the market.

The popular chemometric methods can be applied to analyze your metabolomics data as well. This includes statistical projection techniques such as Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA).

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