An open source tool which is a flexible and accurate method for pre-processing very large numbers of GC-MS samples within hours. A novel strategy was developed to iteratively correct and update retention time indices for searching and identifying metabolites. TargetSearch includes a graphical user interface to allow easy use by those unfamiliar with R. It allows fast and accurate data pre-processing for GC-MS experiments and overcomes the sample number limitations and manual curation requirements of existing software.
A software tool for the alignment of large GC-MS-based metabolite profiling experiments into statistically accessible data matrices. The matrix generation is directed by co-analysis of retention index (RI) marker substances within each chromatogram and the simultaneous in-parallel analysis of mixtures of reference compounds is recommended. In addition, we offer automated extraction of quantitative data from predefined mass fragments, time groups of mass fragments or clusters of intensity-correlated mass fragments. TagFinder is freely available for academic use.
A web-based tool that objectively quantifies every metabolite peak detected in a set of samples and aligns peaks across multiple samples to enable quantitative comparison of each metabolite between samples. The analysis incorporates quantification of multiple peaks/ions that have different chromatographic retention times but are detected within a single SRM transition.
Allows alignment of homogeneous data. mSPA is especially designed for two-dimensional gas chromatography mass spectrometry (GC × GC–MS). It employs the peak distance and the spectra similarity sequentially in parallel to proceed. This tool was tested on two sets of GC × GC–MS data. The results show that the Canberra distance is able to make good performances and the proposed mixture similarity peak alignment algorithm prevailed against all literature reported methods.
Aims to formalize the analysis of gas chromatography-mass spectrometry (GC/MS) data. PYQUAN uses the AMDIS data to align the individual samples against the personal retention time (RT) library. It offers the possibility to backfill expected peaks in samples where AMDIS did not recognize those peaks. Moreover, this program utilizes AMDIS for producing for each peak a list of all possible identifications, based on a personally managed mass spectra library.
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