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Robust Relational Metabolite Database

SimMet database includes 66,512 metabolite species from various biological sources such as Humans, Yeast and E. coli. Additional biological information such as metabolite common name, systematic name, mass, composition and links to the other databases are also made available for easy reference.

To assure accurate identification of metabolites using MS/MS data, SimMet database contains 15,920 metabolites with structure specific diagnostic ions verified from over 60,000 experimental MS/MS spectra.

Support for Multiple File Formats

SimMet accepts experimental mass spectrometry data in standard file formats such as mzData and mzXML. It can also read data directly from Bruker's native data files (.baf, .yep and .fid), and Thermo™ Scientific's native data files (.raw).



LC-MS and MS/MS Data Processing

SimMet supports robust LC-MS data processing techniques such as peak detection, picking and identification. The data is automatically processed in batch mode to generate tables of detected metabolites for the retention time range specified. The tool automatically tabulates all the m/z values, abundance, isotope level, charge state and MS/MS data. SimMet automatically aligns detected compounds with the corresponding MS/MS spectra for accurate metabolite identification based on exact mass search as well as precursor and product ions matching.

In addition, SimMet aligns metabolites across biological samples based on retention time, charge state and abundance to facilitate quantitative, qualitative and differential metabolomics. The program displays overlay of chromatograms of aligned compounds.

High Throughput MS and MS/MS Data Analysis

SimMet enables metabolite identification by matching observed metabolite precursor ions and product ions against known metabolite structures/fragments available in the SimMet database. SimMet offers an intuitive user interface to perform such analysis with ease besides displaying the identified 2D structure, fragment ions, annotated MS or MS/MS spectra and other information of identified metabolite in the same workspace.

SimMet analyzes MS/MS data of 10,000 scans in a single search run. High resolution accurate mass data with error tolerance between 1-50 parts per million (ppm) and 0.1 to 2000 millidaltons (mDa) can be analyzed. The results are sorted using an innovative ranking algorithm that maps the degree of proximity of theoretical metabolites with experimental data.

Differential Metabolomics for Biological Samples

The Principal Component Analysis (PCA) for metabolites in SimMet provides a visual representation of the relationship between biological samples and metabolites. Researchers can easily analyze similarities and differences amongst various biological samples. Principal Component Analysis (PCA) generates two 2-D plots, loading and score plot. In order to pinpoint probable biomarkers, the 2-D plots are supported with confidence ellipses such as correlation loadings and Hotelling's T2 ellipses.

Quantitative Metabolomics

SimMet performs quantitative data analysis of metabolites present in samples using calibration curves constructed from dilution series and internal standards. SimMet predicts the amount of analyte in samples using the fitted line plot methodology for regression.

Mass Spectra Annotation with Identified Metabolite

SimMet can annotate mass spectra with the metabolites identified from MS and MS/MS data. This helps in interpreting mass spectra by highlighting the experimental m/z values that match those of theoretical metabolite structures from the product database. Each matched peak can be annotated with the metabolite fragment. The annotated spectra can be exported as an image (.JPEG or .PNG) to MS PowerPoint to facilitate information sharing amongst research groups.

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