ArticleMethods in molecular biology (Clifton, N.J.)2024
Integration of Web-Based Tools to Visualize, Integrate, and Interpret Glycogene Expression and Glycomics Data.
Article in Methods in molecular biology (Clifton, N.J.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Integrated Multiomics Reveals Alterations in Paucimannose and Complex Type N-Glycans in Cardiac Tissue of Patients with COVID-19.Molecular & cellular proteomics : MCP · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Glycosylation is the most abundant and diverse post-translational modification occurring on proteins. Glycans play important roles in modulating cell adhesion, growth, development, and differentiation. Changes in glycosylation affect protein structure and function and contribute to disease processes. Therefore, understanding glycosylation patterns is key for the identification of targets for the diagnosis of diseases, cellular states, and therapy. Glycosylation is a non template-driven process governed by the action of numerous enzymes and substrate availability that varies among cell types and species. Therefore, qualitative and quantitative assessment of global glycosylation and individual glycans remains challenging because it requires integration of multiple complex data types. Glycan structure and quantity data are often integrated with assessments of gene expression to aid contextualization of observed glycosylation changes within biological processes. However, correlating glycogene expression to the glycan structure is challenging because transcriptional changes may not always concur with the final gene product; there is often a lack of information on nucleotide sugar pools, and the final glycan structure is the result of many different glycogenes acting in concert. To overcome these challenges, interactive online tools are emerging as key resources for facilitating the analysis and integration of glycomics and glycogene expression data. Importantly, these tools work in concurrence with glycan biosynthetic schemes and therefore provide a clear indication of the molecular pathways where the glycan and glycogene are involved. In this chapter, we describe the applications of four freely available online tools that can be used for integrated visualization, interpretation, and presentation of RNAseq and glycomics results.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.