Data-processing and feature extraction of profiling LC-MS assays is usually performed using untargeted peak detection workflows that aim to comprehensively detect all the LC-MS peaks in a sample set. These workflows generate large data matrices comprised of thousands of features requiring a posteriori metabolite annotation. This is commonly achieved using a combination of computational methods to "group" features into "pseudo-spectra", followed by spectral matching to databases or other experimental data sources. There are already many commercial and open-source software available for untargeted peak detection and postprocessing. Investment in assay-specific chromatographic and spectral library building unlocks the possibility of deploying a more targeted feature extraction workflow. In this context, feature extraction is performed solely with the objective of detecting and integrating a set of well characterized and previously annotated signals. This approach provides users with a consistent output that includes a fixed set of variables and conclusive assessment of detection or non-detection of those metabolites in individual samples. We have developed an opensource R package, peakPantheR , which leverages the knowledge gained from assay characterisation and enables targeted detection, peak fitting and integration of pre-annotated LC-MS features. The software also enables convenient visual review of individual peak performance across a study (Figure ). The approach produces high-quality annotated datasets that are accessible to a broader range of biomedical researchers and more immediately interpretable than untargeted feature tables. Further details on PeakPantheR installation and usage can be found at . Regardless of the feature extraction method used, quality control and feature filtering procedures should be applied to assess and ensure the quality of the dataset and individual LC-MS features. For this purpose, we have implemented routines for metabolomic data preprocessing and quality control in an open source Python software, the nPYc-Toolbox. The nPYc-Toolbox provides functionality for batch and run order correction, feature filtering procedures based on repeated injections and dilution series of pooled QC samples, exploratory multivariate quality control, and summary reporting (Figure ).