ReviewMass spectrometry reviews
Software Design and Analytical Challenges for Confident Glycopeptide Identification With Data-Independent Acquisition.
Review in Mass spectrometry reviews. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- GlycoDiveR: A Modular R Framework to Analyze and Visualize Highly Dimensional Glycoproteomics Data.ACS measurement science au · 2026Article
- GlycoDiveR: a modular R framework to analyze and visualize highly dimensional glycoproteomics data.bioRxiv : the preprint server for biology · 2026Article
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 an abundant post-translational modification that impacts a wide variety of functions, including protein regulation, cell adhesion, and structural integrity. The application of proteomics methods to glycopeptide assignment faces unique challenges due to high heterogeneity, which results in complex populations with low overall abundance per glycopeptide. In addition, glycans dissociate at a lower collision energy compared to their attached peptide component. The resulting mass spectral data require specialized assignment software, which has caused glycoproteomics to lag traditional proteomics. Existing software primarily focuses on data-dependent acquisition (DDA), but manual validation is frequently required, and experiments are necessarily limited by the stochastic nature of DDA ion-selection. Data-independent acquisition (DIA) allows for a more complete and robust analysis of glycopeptide samples, but analysis software is still sparse. In this review, we discuss the current state of DDA analysis software, the limitations, and how it can inform our forays into DIA glycoproteomics.
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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.