Introduction: Data processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow.
Objectives: Merge in the same platform the steps required for metabolomics data processing.
Methods: KniMet is a workflow for the processing of mass spectrometry-metabolomics data based on the KNIME Analytics platform.
Results: The approach includes key steps to follow in metabolomics data processing: feature filtering, missing value imputation, normalization, batch correction and annotation.
Conclusion: KniMet provides the user with a local, modular and customizable workflow for the processing of both GC-MS and LC-MS open profiling data.
Keywords: Data processing; GC–MS; LC–MS; Metabolomics.