Alignment of capillary electrophoresis-mass spectrometry datasets using accurate mass information

Capillary electrophoresis-mass spectrometry (CE-MS) is a powerful technique for the analysis of small soluble compounds in biological fluids. A major drawback of CE is the poor migration time reproducibility, which makes it difficult to combine data from different experiments and correctly assign compounds. A number of alignment algorithms have been developed but not all of them can cope with large and irregular time shifts between CE-MS runs. Here we present a genetic algorithm designed for alignment of CE-MS data using accurate mass information. The utility of the algorithm was demonstrated on real data, and the results were compared with one of the existing packages. The new algorithm showed a significant reduction of elution time variation in the aligned datasets. The importance of mass accuracy for the performance of the algorithm was also demonstrated by comparing alignments of datasets from a standard time-of-flight (TOF) instrument with those from the new ultrahigh resolution TOF maXis (Bruker Daltonics).