Improved metabolite identification with MIDAS and MAGMa through MS/MS spectral dataset-driven parameter optimization

Introduction LC-MS/MS based untargeted metabolomics is evoking high interests in the metabolomics and broader biology community for its potential to uncover the contribution of unanticipated metabolic pathways to phenotypic observations. The major challenge for this methodology is making the computational metabolite identification as reliable as possible in order to reduce subsequent target candidate validation to a minimum. Metabolite library matching techniques based on precise masses and fragment mass patterns have become the de facto method in the field. However, in the literature the original methods are often under-validated, making it complicated to judge their intrinsic value. Objectives We aimed to demonstrate that large MS/MS metabolite spectral libraries can be used not only to validate and compare, but also to improve the methods. Methods Several computational tools for metabolite identification (MAGMa, CFM-ID, MetFrag, MIDAS) were applied on a large MS/MS dataset derived from Metlin. Their performance was first compared and for the two best-performing tools (MAGMa and MIDAS), the performance was then improved by applying a parameter fine-tuning procedure. Results We confirmed MIDAS and MAGMa as the state-of-the-art freely available tools for metabolite identification. Moreover, we were able to identify optimized working parameters, engendering an improvement in their performance. For MAGMa, dynamic, metabolite-dependent optimized parameters were obtained using machine learning techniques. Conclusion We were able to achieve an incremental increase in the identification accuracy of MIDAS and MAGMa. A wrapper script (MAGMa?) capable of calling MAGMa with tailored parameters is made available for download.