Finnee 2024 – a Matlab toolbox for untargeted analysis of LC-HRMS datasets with MS1 scans acquired as continuous scans.

Metabolite identification and quantification in biological samples are crucial for understanding biochemical processes, disease mechanisms, and biomarker discovery. While targeted metabolomics focuses on specific compounds, untargeted metabolomics comprehensively assesses the entire metabolome. Liquid chromatography hyphenated with high-resolution mass spectrometry (LC-HRMS) has become a powerful tool for untargeted metabolomics due to its ability to detect various chemicals. LC-HRMS combines liquid chromatography's separation capabilities with mass spectrometry's high-resolution mass analysis. MS1 spectra, acquired in LC-HRMS, provide detailed mass-to-charge ratio information for detected ions, enabling simultaneous profiling of metabolites without prior knowledge of their structures or retention times. Pre-processing steps and feature detection algorithms are applied to raw MS1 spectra, facilitating downstream analysis, including statistical analysis, metabolite annotation, and pathway mapping. The choice between profile and centroid scans for MS1 data acquisition entails trade-offs in resolution, data complexity, and processing requirements. Several software tools have been developed to handle LC-HRMS data, providing functionalities for pre-processing, peak detection, metabolite identification, and statistical analysis. This article presents Finnee2024, a Matlab toolbox for analysing profile scan-based MS1 data. The toolbox allows good coverage, low false positives, and improved visualisation and control of extracted features. It offers enhancements over previous versions, facilitating faster calculations and leveraging the advantages of profile scans. The article highlights the changes in Finnee2024 using datasets from a comparative study of software packages, demonstrating its performance and usability.