https://www.selleckchem.com/products/clozapine-n-oxide.html Protein glycosylation is a complex post-translational modification with crucial cellular functions in all domains of life. Currently, large-scale glycoproteomics approaches rely on glycan database dependent algorithms and are thus unsuitable for discovery-driven analyses of glycoproteomes. Therefore, we devised SugarPy, a glycan database independent Python module, and validated it on the glycoproteome of human breast milk. We further demonstrated its applicability by analyzing glycoproteomes with uncommon glycans stemming from the green alga Chlamydomonas reinhardtii and the archaeon Haloferax volcanii. SugarPy also facilitated the novel characterization of glycoproteins from the red alga Cyanidioschyzon merolae. The source code is freely available on GitHub (https//github.com/SugarPy/SugarPy), and its implementation in Python ensures support for all operating systems. Supplementary data are available at Bioinformatics online. Supplementary data are available at Bioinformatics online. The importance of a data management strategy is increasingly necessary for demonstrating value and driving performance within pharmacy departments. Data analytics capabilities often do not match the pace of data accumulation. At our organization, the establishment of an embedded pharmacy analytics and outcomes (PAO) team has been instrumental to pharmacy services in generating and demonstrating value and proactively supporting a business intelligence strategy grounded in a data-driven culture. The PAO team was established to support the operational and strategic needs of clinical, financial, and operational pharmacy services. The team is charged with implementing the vision of extending medication-use influence and data insight to drive value-based patient care outcomes while decreasing waste, optimizing therapeutic decisions, and achieving medication management standardization across the continuum of healthcare. The PAO team