MassUntangler: A novel alignment tool for label-free liquid chromatography–mass spectrometry proteomic data

Liquid chromatography–mass spectrometry (LC–MS) has become an important analytical tool for quantitative proteomics and biomarker discovery. In the label-free differential LC–MS approach computational methods are required for an accurate alignment of peaks extrapolated from the experimental raw data accounting for retention time and m/z signals intensity, which are strongly affected by sample matrix and instrumental performance. A novel procedure “MassUntangler” for pairwise alignment has been developed, relying on a pattern-based matching algorithm integrated with filtering algorithms in a multi-step approach. The procedure has been optimized employing a two-step approach. Firstly, low-complexity LC–MS data derived from the enzymatic digestion of two standard proteins have been analyzed. Then, the algorithmʹs performance has been evaluated by comparing the results with other achieved using state-of-the-art alignment tools. In the second step, our algorithm has been used for the alignment of high-complexity LC–MS data consisting of peptides obtained by an Escherichia coli lysate available from a public repository previously used for the comparison of other alignment tools. MassUntangler gave excellent results in terms of precision scores (from 80% to 93%) and recall scores (from 68% to 89%), showing performances similar and even better than the previous developed tools. Considering the mass spectrometry sensitivity and accuracy, this approach allows the identification and quantification of peptides present in a biological sample at femtomole level with high confidence. The procedureʹs capability of aligning LC–MS data previously corrected for distortion in retention time has been studied through a hybrid approach, in which MassUntangler was interfaced with the OpenMS TOPP tool MapAligner. The hybrid aligner yielded better results, showing that an integration of different bioinformatic approaches for accurate label-free LC–MS data alignment should be used.