Glycomics helps investigate the role glycosylation plays in complex diseases. Liquid chromatography (LC) coupled with mass spectrometry (MS) is routinely used to profile the glycans released from proteins in a biological sample. This enables us to compare observed glycans and their abundances among different biological samples to discover candidate biomarkers. One of the challenges in label-free LC/MS-based glycan profiling is the presence of various charge states and derived adduct ions. We propose a novel Glycan Profile Annotation (GPA) algorithm to automatically cluster and annotate these ions using a graphical model. Specifically, GPA aims to generate a list of unique neutral masses representing putative glycan composition derived from various charge states and multiple adducts. We demonstrate the performance of GPA in recognizing ions derived from the same glycan through analysis of LC/MS data from a serum biomarker discovery study. In addition, a simulation study is carried out to evaluate GPA's performance against existing tools in handling ambiguous cases.

MOTIVATION: Liquid chromatography-mass spectrometry (LC-MS) has been widely used for profiling expression levels of biomolecules in various '-omic' studies including proteomics, metabolomics and glycomics. Appropriate LC-MS data preprocessing steps are needed to detect true differences between biological groups. Retention time (RT) alignment, which is required to ensure that ion intensity measurements among multiple LC-MS runs are comparable, is one of the most important yet challenging preprocessing steps. Current alignment approaches estimate RT variability using either single chromatograms or detected peaks, but do not simultaneously take into account the complementary information embedded in the entire LC-MS data. RESULTS: We propose a Bayesian alignment model for LC-MS data analysis. The alignment model provides estimates of the RT variability along with uncertainty measures. The model enables integration of multiple sources of information including internal standards and clustered chromatograms in a mathematically rigorous framework. We apply the model to LC-MS metabolomic, proteomic and glycomic data. The performance of the model is evaluated based on ground-truth data, by measuring correlation of variation, RT difference across runs and peak-matching performance. We demonstrate that Bayesian alignment model improves significantly the RT alignment performance through appropriate integration of relevant information. AVAILABILITY AND IMPLEMENTATION: MATLAB code, raw and preprocessed LC-MS data are available at http://omics.georgetown.edu/alignLCMS.html. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: As a common post-translational modification, protein glycosylation plays an important role in many biological processes, and it is known to be associated with human diseases. Mass spectrometry (MS)-based glycomic profiling techniques have been developed to measure the abundances of glycans in complex biological samples and applied to the discovery of putative glycan biomarkers. To automate the annotation of glycomic profiles in the liquid chromatography-MS (LC-MS) data, we present here a user-friendly software tool, MultiGlycan, implemented in C# on Windows systems. We tested MultiGlycan by using several glycomic profiling datasets acquired using LC-MS under different preparations and show that MultiGlycan executes fast and generates robust and reliable results. AVAILABILITY: MultiGlycan can be freely downloaded at http://darwin.informatics.indiana.edu/MultiGlycan/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.