Predicts collision cross-section (CCS) values of metabolites in a quickly way. MetCCS Predictor can be used for small chemical compounds like drugs, pesticides, nature products and so on, which may have a broad impact in other fields. It provides search and match function, which supports users to readily search the CCS values of metabolites in the MetCCS database or match experimentally measured m/z and CCS values with the database for metabolite identification.
A powerful platform to discriminate biological features from the various noise sources prevalent in untargeted metabolomic data. The process is experimentally straightforward and can be easily implemented in any metabolomic laboratory. Feature credentialing reliably removes artifactual features such as those arising from chemical and informatic noise, thereby resulting in a valuable list of features of biological origin. These credentialed features address many of the drawbacks associated with feature counting in comparing method performance on the basis of metabolome coverage. As such, counting credentialed features can be used in the development and optimization of untargeted metabolomic approaches as demonstrated by the reoptimization of XCMS parameters. Credentialing features is also an effective data reduction strategy for untargeted metabolomic results such that a smaller number of peaks can be targeted for MS/MS analysis.
Serves for merging untargeted metabolomic data from high-resolution mass spectrometry in the positive and negative ionization modes. MSCombine is able to find common entities detected in both positive and negative ionization mode and to delete this entity in the less sensible mode and combine both matrices. For instance, this tool can be applied to data sets obtained from the analysis of human serum and urine.
Provides a strategy for feature selection to establish a blood-based diagnostic test for thrombotic myocardial infarction. WoAC is an R package that gathers methods based on intermediate genetic algorithm solutions. It also uses a classifier for the discrimination of acute myocardial infarction from the abundance of circulating metabolites in plasma. The method was tested comparatively to Lasso and Random Forest approaches.
Proceeds to automatic batch-conversion of capillary electrophoresis coupled to mass spectrometry (CE-MS) files into an effective electrophoretic mobility scale. ROMANCE permits direct comparison of the measured electrophoretic mobility with values obtained in either the same or different laboratories from reference standards. It can be used for two kinds of background electrolyte (BGE) markers.
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