Glycowork: A Python package for glycan data science and machine learning.

Python data science glycobioinformatics glycobiologymachine learning

Journal

Glycobiology
ISSN: 1460-2423
Titre abrégé: Glycobiology
Pays: England
ID NLM: 9104124

Informations de publication

Date de publication:
18 11 2021
Historique:
received: 23 04 2021
revised: 02 06 2021
accepted: 25 06 2021
pubmed: 1 7 2021
medline: 22 3 2022
entrez: 30 6 2021
Statut: ppublish

Résumé

While glycans are crucial for biological processes, existing analysis modalities make it difficult for researchers with limited computational background to include these diverse carbohydrates into workflows. Here, we present glycowork, an open-source Python package designed for glycan-related data science and machine learning by end users. Glycowork includes functions to, for instance, automatically annotate glycan motifs and analyze their distributions via heatmaps and statistical enrichment. We also provide visualization methods, routines to interact with stored databases, trained machine learning models and learned glycan representations. We envision that glycowork can extract further insights from glycan datasets and demonstrate this with workflows that analyze glycan motifs in various biological contexts. Glycowork can be freely accessed at https://github.com/BojarLab/glycowork/.

Identifiants

pubmed: 34192308
pii: 6311240
doi: 10.1093/glycob/cwab067
pmc: PMC8600276
doi:

Substances chimiques

Polysaccharides 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1240-1244

Informations de copyright

© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Références

Bioinformatics. 2017 May 1;33(9):1317-1323
pubmed: 28093404
Curr Opin Virol. 2014 Aug;7:79-87
pubmed: 24995558
J Chem Inf Model. 2014 Jun 23;54(6):1558-66
pubmed: 24897372
BMC Bioinformatics. 2020 Feb 4;21(1):42
pubmed: 32019496
Carbohydr Res. 2008 Aug 11;343(12):2162-71
pubmed: 18436199
Dev Cell. 2021 Apr 19;56(8):1195-1209.e7
pubmed: 33730547
Glycobiology. 2020 Jan 28;30(2):70-71
pubmed: 31573039
Cell Host Microbe. 2021 Jan 13;29(1):132-144.e3
pubmed: 33120114
Sci Adv. 2021 Feb 19;7(8):
pubmed: 33608275
Nat Methods. 2015 Feb;12(2):115-21
pubmed: 25633503
Glycobiology. 2017 Jan;27(1):3-49
pubmed: 27558841
Curr Opin Chem Biol. 2013 Oct;17(5):827-31
pubmed: 23856055
Bioinformatics. 2009 Jun 1;25(11):1422-3
pubmed: 19304878
J Proteome Res. 2019 Sep 6;18(9):3532-3537
pubmed: 31310539
Glycoconj J. 2010 Aug;27(6):561-70
pubmed: 20734133
Glycobiology. 2016 Oct;26(10):1027-1028
pubmed: 28120784
PLoS One. 2018 Jul 25;13(7):e0200929
pubmed: 30044828
Mol Cell Proteomics. 2020 Feb;19(2):224-232
pubmed: 31848260
Annu Rev Cell Dev Biol. 2010;26:721-44
pubmed: 20604711
Curr Opin Chem Biol. 2009 Oct;13(4):406-13
pubmed: 19625207
Cell Rep. 2021 Jun 15;35(11):109251
pubmed: 34133929
Chem Biol. 2014 Jan 16;21(1):1-15
pubmed: 24439204

Auteurs

Luc Thomès (L)

Department of Chemistry and Molecular Biology and Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, 41390 Gothenburg, Sweden.

Rebekka Burkholz (R)

Department of Biostatistics, Harvard School of Public Health, Boston, 02115 MA, USA.

Daniel Bojar (D)

Department of Chemistry and Molecular Biology and Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, 41390 Gothenburg, Sweden.

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Classifications MeSH