Evidence map›Paper›PMID 38951670›Full record

ArticleNature methods2024

Predicting glycan structure from tandem mass spectrometry via deep learning.

James Urban, Chunsheng Jin, Kristina A Thomsson, Niclas G Karlsson, Callum M Ives, Elisa Fadda, Daniel Bojar

Abstract read
In one paragraph

Article in Nature methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

36 citing papers in PubMed.

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  12. Glycan Sequencing, A Brief Primer.Glycoscience & therapy · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

James UrbanDepartment of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden.
Chunsheng JinProteomics Core Facility at Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Kristina A ThomssonProteomics Core Facility at Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Niclas G KarlssonSection of Pharmacy, Department of Life Sciences and Health, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway.ORCID http://orcid.org/0000-0002-3045-2628
Callum M IvesDepartment of Chemistry and Hamilton Institute, Maynooth University, Maynooth, Ireland.ORCID http://orcid.org/0000-0003-0511-1220
Elisa FaddaSchool of Biological Sciences, University of Southampton, Southampton, UK.
Daniel BojarDepartment of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden. daniel.bojar@gu.se.ORCID http://orcid.org/0000-0002-3008-7851

Funding

Science Foundation Ireland (SFI) 20/FFP-P/8809Vetenskapsrådet (Swedish Research Council) BioMS
6 · The paper itself

Abstract

Glycans constitute the most complicated post-translational modification, modulating protein activity in health and disease. However, structural annotation from tandem mass spectrometry (MS/MS) data is a bottleneck in glycomics, preventing high-throughput endeavors and relegating glycomics to a few experts. Trained on a newly curated set of 500,000 annotated MS/MS spectra, here we present CandyCrunch, a dilated residual neural network predicting glycan structure from raw liquid chromatography-MS/MS data in seconds (top-1 accuracy: 90.3%). We developed an open-access Python-based workflow of raw data conversion and prediction, followed by automated curation and fragment annotation, with predictions recapitulating and extending expert annotation. We demonstrate that this can be used for de novo annotation, diagnostic fragment identification and high-throughput glycomics. For maximum impact, this entire pipeline is tightly interlaced with our glycowork platform and can be easily tested at https://colab.research.google.com/github/BojarLab/CandyCrunch/blob/main/CandyCrunch.ipynb . We envision CandyCrunch to democratize structural glycomics and the elucidation of biological roles of glycans.

Indexed as

Deep LearningPolysaccharidesTandem Mass SpectrometryChromatography, LiquidGlycomicsHumansNeural Networks, ComputerSoftwareWorkflowPolysaccharides

Identifiers

PMID38951670
PMCPMC11239490

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.