• Spectrolyzer P for LC-MS
• A software package for LC-MS-based label-free quantitative proteomics
• Integration with Open MS software
• Statistical analysis
• Pipeline for LC-MS data preprocessing
• Examples
• Videos
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• 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

For more details see: Spectrolyzer™ for LC MS product brochure

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.

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. 

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. 

1. Noise Filtering (Smoothing)
   1. Noise Filtering (Smoothing) — The Noise Filtering (or Smoothing) is used to reduce the noise present in raw LC-MS spectra. The analysis is performed spectra-wise, i.e. the noise is filtered for each RT (spectra scan) separately. Two approaches (i.e. noise filters) are implemented:  Savitzky-Golay filter and Gaussian filter. For each filter, parameters controlling an amount of smoothing are available.
2. Baseline Filtering
   1. Baseline Filtering — The Baseline Filtering is used to remove the underlying baseline from raw LC-MS spectra. The analysis is performed spectra-wise, i.e. the baseline is filtered for each RT (spectra scan) separately. The baseline correction algorithm is based on a selected non-linear morphological filter.
3. Peak Picking
   1. Peak Picking — The Peak Picking is performed in order to extract peaks from raw LC-MS data. The analysis is performed spectra-wise, i.e. peaks are detected for each RT (spectra scan) separately. To detect mass peaks in raw data the continuous wavelet transform (CWT) algorithm is used. The algorithm is intended for low or medium resolution data.
4. Normalization
   1. Normalization — The Normalization of the peak intensities is often required to enable meaningful comparison  between samples. Two approaches to normalization are typically used, i.e. one can either normalize LC-MS spectra by dividing through the TIC (Total Ion Current) or normalize to max intensity of one.
5. Peak Data Feature Finding
   1. Peak Data Feature Finding — The purpose of the Feature Finding is to detect two-dimensional features in LC-MS data. By feature, we understand a peptide in a MS sample that reveals a characteristic isotope distribution. Thus, in contrast to peak picking, algorithm searches explicitly for peptides which can be recognized by their isotopic pattern. Centroided algorithm is used to detect features. Algorithm identifies interesting regions by calculating a score for each peak. One of the advantages of the algorithm is its speed due to the reduced amount of data after Peak Picking.
6. Map Aligning
   1. Map Aligning — In typical LC-MS analysis pipeline, combination of results from multiple experiments is often required. The goal of the Map Alignment analysis is to transform different LC-MS feature maps to a common retention time axis. It corrects shifted and scaled retention times, which may result from changes of the chromatography.
7. Feature Grouping
   1. Feature Grouping — The Feature Grouping analysis links features from different LC-MS maps into one consensus map. Groups of corresponding features have to be found in order to quantify differences across maps. These groups are represented by consensus features, which contain information about the constituting features in the maps as well as average position and intensity. Feature grouping algorithm intended for label-free quantitation is used. Analysis takes several input feature maps and returns consensus feature map. Feature grouping yields common set of features shared by transformed LC-MS spectra. Such data are easily transformed into expression profile matrix, i.e. a format suitable for further statistical data analysis.

Don't just take our word for it, download Spectrolyzer today and see for yourself. Your data holds much more than what your current software is showing you!

Do you need more information? Want to ask about a specific feature or work on a project with us? Send us your contact information and we'll get in touch immediately.