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

Abstract

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 algorithm's 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 procedure's 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.