Peak integration software

The data extraction of the raw mass spec data files yielded information that could loaded into a relational database and manipulated without resorting to BLOB manipulation. Once in the database the information was examined and appropriate QC limits were imposed. Peaks were identified using Metabolon’s proprietary peak integration software, and component parts were stored in a separate and specifically designed complex data structure.

Mass spectral data were loaded into a relational database and peaks were identified using Metabolon’s peak integration software [63 (link)]. Compounds were identified by comparison to library entries of purified standards based on the combination of retention time and mass spectra. Data were normalized to correct for variation resulting from instrument inter-day tuning differences. Each compound was corrected by registering the medians to equal 1.00 in run-day blocks and normalizing each data point proportionately. Missing values, assumed to be below the limit of detection of the instrument, were imputed with the observed minimum after normalization.

Instrument variability was determined to be 4% by calculating the median relative standard deviations (RSD) for the internal standards that were added to each sample prior to injection into the mass spectrometers. Total process variability was determined to be 11% by calculating the median RSD for all endogenous metabolites present in 100% of samples, which are technical replicates of pooled samples.
The raw mass spec data files were loaded into an inhouse relational database without BLOB manipulation. Peaks were identified using Metabolon’s proprietary peak integration software. Metabolites were identified based on combination of mass and retention time that matched with Metabolon’s in-house library consisting of purified standards and recurrent unknown entities [50 (link)]. Quality check and curation were performed by Metabolon using proprietary methods which included confirmation of consistency of peak identification among the various samples and standards. Library matches for each compound for each sample were assessed to ensure accurate identification of metabolites and to remove system artifacts, mis-assignments, and background noise.

Mass spectral data were loaded into a relational database, where QC limits were imposed. Peaks were identified using Metabolon’s peak integration software [17 (link)]. Compounds were identified by comparison to library entries of purified standards or recurrent unknown entities based on the combination of chromatographic properties and mass spectra. Data normalization was performed for studies spanning multiple days to correct for variation resulting from instrument inter-day tuning differences. Each compound was corrected by registering the medians to equal 1.00 in run-day blocks and normalizing each data point proportionately. Missing values, assumed to be below the limit of detection of the instrument, were imputed with the observed minimum after normalization.

Peaks were identified using Metabolon's proprietary peak integration software. Compounds were identified by comparison to library entries of purified standards or recurrent unknown entities. Identification of known chemical entities was based on comparison to metabolomic library entries of purified standards. As of this writing, more than 2,600 commercially available purified standard compounds had been identified and registered into LIMS for distribution to both the LC and GC platforms for determination of their analytical characteristics. The combination of chromatographic properties and mass spectra gave an indication of a match to the specific compound or an isobaric entity. Metabolon data analysts use proprietary visualization and interpretation software to confirm the consistency of peak identification among the various samples. Library matches for each compound were checked for each sample and corrected if necessary.

The data extraction of the raw mass spec data files yielded information that could be loaded into a relational database and manipulated without resorting to BLOB manipulation. Once in the database, the information was examined and appropriate QC limits were imposed. Peaks were identified using Metabolon’s proprietary peak integration software, and component parts were stored in a separate, specifically designed complex data structure.

The data extraction of the raw mass spec data files yielded information that was loaded into a relational database and manipulated without resorting to BLOB manipulation. Once in the database, the information was examined, and appropriate QC limits were imposed. Peaks were identified using Metabolon’s proprietary peak integration software, and component parts were stored in a separate and specifically designed complex data structure.