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Spectrolyzer was developed out of the need for scientists and researchers to analyse and easily extract valuable information from mass spectrometry datasets in proteomics.

Spectrolyzer P for LC-MS

Spectrolyzer for LC-MS is based on 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, raw LC-MS data can be prepared for comprehensive statistical analysis, which in turn can result in the discovery of interesting peptides (biomarker candidates) by differentiating between different groups, e.g. patient samples and control samples.

The discovery of interesting peptides can be followed by database searches for protein identification using MS/MS data, with the help of either Mascot or X!Tandem

A Software package for LC-MS-based label-free quantitative proteomics

Spectrolyzer can perform all stages of processing required for analyzing large sets of LC-MS (HPLC-ESI MS, MS/MS) samples. For preprocessing Spectrolyzer enables performing signal processing, noise and baseline filtering, feature detection, map aligning and feature grouping. Thereafter, Spectrolyzer is able to perform different analyses, e.g. build diagnostic models, detect features for biomarker candidates, and perform protein 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 analysis. This includes various one- and multi-dimensional data exploration tools as well as advanced multivariate statistical methods. Special emphasis is given to methods supporting biomarker discovery.

Discover how scientists have used our cloud based
in their research.

”Our main strong point is discovering biomarkers within your dataset. With just a few clicks, your data is analyzed and the valuable information is extracted and visualized, allowing you to quickly reveal biological findings.”

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