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
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
1240-1244Informations de copyright
© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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