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. 2020 Jun 1;36(12):3941-3943.
doi: 10.1093/bioinformatics/btaa238.

GlyGen data model and processing workflow

Robel Kahsay  1 , Jeet Vora  1 , Rahi Navelkar  1 , Reza Mousavi  1 , Brian C Fochtman  1 , Xavier Holmes  1 , Nagarajan Pattabiraman  1 , Rene Ranzinger  2 , Rupali Mahadik  2 , Tatiana Williamson  2 , Sujeet Kulkarni  2 , Gaurav Agarwal  2 , Maria Martin  3 , Preethi Vasudev  3 , Leyla Garcia  4 , Nathan Edwards  5 , Wenjin Zhang  5 , Darren A Natale  5 , Karen Ross  5 , Kiyoko F Aoki-Kinoshita  6 , Matthew P Campbell  7 , William S York  2 , Raja Mazumder  1
Affiliations

GlyGen data model and processing workflow

Robel Kahsay et al. Bioinformatics. .

Abstract

Summary: Glycoinformatics plays a major role in glycobiology research, and the development of a comprehensive glycoinformatics knowledgebase is critical. This application note describes the GlyGen data model, processing workflow and the data access interfaces featuring programmatic use case example queries based on specific biological questions. The GlyGen project is a data integration, harmonization and dissemination project for carbohydrate and glycoconjugate-related data retrieved from multiple international data sources including UniProtKB, GlyTouCan, UniCarbKB and other key resources.

Availability and implementation: GlyGen web portal is freely available to access at https://glygen.org. The data portal, web services, SPARQL endpoint and GitHub repository are also freely available at https://data.glygen.org, https://api.glygen.org, https://sparql.glygen.org and https://github.com/glygener, respectively. All code is released under license GNU General Public License version 3 (GNU GPLv3) and is available on GitHub https://github.com/glygener. The datasets are made available under Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Supplementary information: Supplementary data are available at Bioinformatics online.

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Figures

Fig. 1.
Fig. 1.
GlyGen data processing workflow showing various steps. Data are retrieved from various resources including UniProtKB, GlyTouCan, UniCarbKB, RefSeq and other key resources, followed by extraction and filtering based on relevance to glycobiology. Extracted data are integrated after harmonization that is based on various standard ontologies. The resulting datasets are then ingested into a MongoDB docstore and Virtuoso triplestore using the GlyGen data model
See this image and copyright information in PMC

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