An Ultra-Fast Metabolite Prediction Algorithm

Small molecules are central to all biological processes and metabolomics becoming an increasingly important discovery tool. Robust, accurate and efficient experimental approaches are critical to supporting and validating predictions from post-genomic studies. To accurately predict metabolic changes and dynamics, experimental design requires multiple biological replicates and usually multiple treatments. Mass spectra from each run are processed and metabolite features are extracted. Because of machine resolution and variation in replicates, one metabolite may have different implementations (values) of retention time and mass in different spectra. A major impediment to effectively utilizing untargeted metabolomics data is ensuring accurate spectral alignment, enabling precise recognition of features (metabolites) across spectra. Existing alignment algorithms use either a global merge strategy or a local merge strategy. The former delivers an accurate alignment, but lacks efficiency. The latter is fast, but often inaccurate. Here we document a new algorithm employing a technique known as quicksort. The results on both simulated data and real data show that this algorithm provides a dramatic increase in alignment speed and also improves alignment accuracy.