Glycans introduce complexity to the proteins to which they are attached. These modifications vary during the progression of many diseases; thus, they serve as potential biomarkers for disease diagnosis and prognosis. The immense structural diversity of glycans makes glycosylation analysis and quantitation difficult. Fortunately, recent advances in analytical techniques provide the opportunity to quantify even lowabundant glycopeptides and glycans derived from complex biological mixtures, allowing for the identification of glycosylation differences between healthy samples and those derived from disease states. Understanding the strengths and weaknesses of different quantitative glycomics analysis methods is important for selecting the best strategy to analyze glycosylation changes in any given set of clinical samples. To provide guidance towards selecting the proper approach, we discuss four widely used quantitative glycomics analysis platforms, including fluorescence-based analysis of released N-linked glycans and three different varieties of MS-based analysis: liquid chromatography (LC)-mass spectrometry (MS) analysis of glycopeptides, matrixassisted laser desorption ionization-time of flight MS, and LC-ESI-MS analysis of released N-linked glycans. These methods' strengths and weaknesses are compared, particularly associated with the figures of merit that are important for clinical biomarker studies, including: the initial sample requirements, the methods' throughput, sample preparation time, the number of species identified, the methods' utility for isomer separation and structural characterization, method-related challenges associated with quantitation, repeatability, the expertise required, and the cost for each analysis. This review, therefore, provides unique guidance to researchers who endeavor to undertake a clinical glycomics analysis by offering insights on the available analysis technologies.

Applying machine learning strategies to interpret mass spectrometry data has the potential to revolutionize the way in which disease is diagnosed, prognosed, and treated. A persistent and tedious obstacle, however, is relaying mass spectrometry data to the machine learning algorithm. Given the native format and large size of mass spectrometry data files, preprocessing is a critical step. To ameliorate this challenge, we sought to create an easy-to-use, continuous pipeline that runs from data acquisition to the machine learning algorithm. Here, we present a start-to-finish pipeline designed to facilitate supervised and unsupervised classification of mass spectrometry data. The input can be any ESI data set collected by LC-MS or flow injection, and the output is a machine learning ready matrix, in which each row is a feature (an abundance of a particular m/z), and each column is a sample. This workflow provides automated handling of large mass spectrometry data sets for researchers seeking to implement machine learning strategies but who lack expertise in programming/coding to rapidly format the data. We demonstrate how the pipeline can be used on two different mass spectrometry data sets: 1) ESI-MS of fingerprint lipid compositions acquired by direct infusion and, 2) LC-MS of IgG glycopeptides. This workflow is uncomplicated and provides value via its simplicity and effectiveness.