2020
DOI: 10.1101/2020.06.03.131979
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Examining and fine-tuning the selection of glycan compositions with GlyConnect Compozitor

Abstract: A key point in achieving accurate intact glycopeptide identification is the definition of the glycan composition file that is used to match experimental with theoretical masses by a glycoproteomics search engine. At present, these files are mainly built from searching the literature and/or querying data sources focused on posttranslational modifications. Most glycoproteomics search engines include a default composition file that is readily used when processing mass spectrometry data. We introduce here a glycan… Show more

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Cited by 2 publications
(3 citation statements)
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“…First, we performed a simple yet effective visualization of glycan compositions using the GlyConnect Compozitor 32 tool to correct the sparsity of missing compositions and identify potential outliers’ glycan clusters (Figure S3) . This step allowed us to confirm the ‘difficult-to-identify glycopeptide features such as NeuAc, NeuGc, and multi-Fuc that are often mis-annotated 33 and pass the empirical score.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…First, we performed a simple yet effective visualization of glycan compositions using the GlyConnect Compozitor 32 tool to correct the sparsity of missing compositions and identify potential outliers’ glycan clusters (Figure S3) . This step allowed us to confirm the ‘difficult-to-identify glycopeptide features such as NeuAc, NeuGc, and multi-Fuc that are often mis-annotated 33 and pass the empirical score.…”
Section: Resultsmentioning
confidence: 99%
“…In this work, the glycan database used for glycopeptide identification is limited to the default glycan compositions available in Byonic. Nevertheless, visualization of glycan compositions via GlyConnect Compozitor 32 enables the user to modify the glycan compositions and refine the search as required to reflect the context of the investigation. Notwithstanding, the logical next step would be to associate glycan composition-based visualization with every single glycopeptide detected to identify the outliers and increase confidence in glycopeptide identification.…”
Section: Resultsmentioning
confidence: 99%
“…Additionally, the identity of the involved enzymes is still incompletely understood, even in humans 4,7 . Therefore, we developed an empirical algorithm that does not rely on known enzymatic activities and that finds the minimum number of proposed intermediates to connect all observed structures in a biosynthetic network (see STAR Methods), conceptually vaguely related to the Compozitor framework 21 , yet not limited to compositions but also taking glycan structure into account. In this, glycans are conceptualized as nodes, connected by edges representing biosynthetic steps.…”
Section: Constructing Biosynthetic Network Of the Free Milk Glycomementioning
confidence: 99%