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. 2020 Apr 23;36(12):3941–3943. doi: 10.1093/bioinformatics/btaa238

GlyGen data model and processing workflow

Robel Kahsay b1,✉, Jeet Vora b1, Rahi Navelkar b1, Reza Mousavi b1, Brian C Fochtman b1, Xavier Holmes b1, Nagarajan Pattabiraman b1, Rene Ranzinger b2, Rupali Mahadik b2, Tatiana Williamson b2, Sujeet Kulkarni b2, Gaurav Agarwal b2, Maria Martin b3, Preethi Vasudev b3, Leyla Garcia b4, Nathan Edwards b5, Wenjin Zhang b5, Darren A Natale b5, Karen Ross b5, Kiyoko F Aoki-Kinoshita b6, Matthew P Campbell b7, William S York b2, Raja Mazumder b1
Editor: Peter Robinson
PMCID: PMC7320628  PMID: 32324859

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.

1 Introduction

This application note introduces the GlyGen data-processing workflow used to build the backend for the GlyGen (York et al., 2019) knowledgebase. This includes detailed information on the molecular, biophysical and functional properties of glycans, genes and proteins organized in pathways and ontologies as well as a rapidly growing body of biological big data related to mutation and expression. All data integrated in the GlyGen project are publicly available in standard formats supported by NCBI (Sayers et al., 2019) and EMBL-EBI (Cook et al., 2018) to promote standardization and sharing of data within the broader glycomics community. GlyGen is a five-star linked open data compliant knowledgebase and a registered member of FAIRsharing.org fulfilling BioDBcore requirements (https://fairsharing.org/biodbcore-001375/).

2 Data integration workflow

The framework used to integrate GlyGen data starts by collecting glycan, protein and glycoprotein datasets from major data resources and data generators. The collected heterogeneous datasets are processed following the workflow shown in Figure 1.

Fig. 1.

Fig. 1.

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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

2.1 Data sources

In GlyGen, GlyTouCan (Tiemeyer et al., 2017) and PubChem (Kim et al., 2016) provide glycan-related data, whereas protein-related data are collected from resources, such as UniProtKB (UniProt Consortium, 2019), NCBI Reference Sequence (RefSeq) (O’Leary et al., 2016), BioMuta (Dingerdissen et al., 2018), BioXpress (Dingerdissen et al., 2018), Mouse Genome Institute (Bult et al., 2019), Orthologous Matrix (Altenhoff et al., 2018), Disease Ontology (Kibbe et al., 2015), Genomics England PanelApp (Martin et al., 2019) and the Monarch Initiative (Mungall et al., 2017). Finally, glycoprotein-related data are integrated from UniCarbKB (Campbell et al., 2014), PDB (Berman et al., 2000) and UniProtKB. In addition to these major resources mentioned, other relevant datasets are collected from various research laboratories.

2.2 Dataset standardization, integration and quality control

Data downloaded from various resources are versioned and stored in the GlyGen backend server. The GlyGen knowledgebase maintains strict protein (UniProtKB canonical accessions) and glycan (GlyTouCan accessions) lists, which are used as the protein and glycan primary keys, respectively. Each dataset is mapped to one of these primary keys, and any non-standard identifiers are mapped to their equivalent standard identifiers. Datasets pass through quality control and filtering steps, such as file format sanity, primary accession checks, residue or amino acid position accuracy and various other filtering steps outlined by subject matter experts. The processed dataset is then assigned a GlyGen dataset identifier, and a dataset BioCompute Object (BCO) (Alterovitz et al., 2018) is created to provide detailed documentation of the data-processing workflow. These datasets can be searched, browsed and downloaded from the GlyGen data page (https://data.glygen.org). A detailed description of data preprocessing and normalization for glycan, protein and glycoprotein datasets is given in the Supplementary Texts S1–S3, the GlyGen data page and dataset sample view page are shown in the Supplementary Figure S1a and b.

2.3 Biocompute Objects for GlyGen datasets

The dataset BCOs are created in conformance to the current BCO specifications (1.3.0) (https://github.com/biocompute-objects/BCO_Specification/tree/master/schemas). A dataset BCO is created with the data integration process perspective to enable capturing all the metadata related to the processing steps performed in the workflow. The dataset BCOs constructed this way can be used as a ‘readme’ for the dataset that provides precise details on how the dataset is integrated. The use of the BCO standard facilitates granular tracking of metadata especially the provenance that helps in providing appropriate attribution and license information that dictates the usage of the dataset, workflow exchange between the researchers and the reproducibility of the dataset. These dataset BCOs are recorded and represented in machine-readable JavaScript Object Notation (JSON) format and can be viewed and downloaded from the GlyGen data page (https://data.glygen.org).

2.4 GlyGen docstore and web services

As mentioned earlier, the GlyGen data integration workflow creates glycan, protein and glycoprotein centric datasets. These datasets are used to generate glycan, protein and glycoprotein centric JSON objects, which are stored in a MongoDB docstore. The GlyGen docstore is used as a backend for various GlyGen web services that are used by the GlyGen frontend as well as other external applications. The GlyGen web services (https://api.glygen.org), which have been documented using the Swagger framework (https://swagger.io/) allow programmatic access of GlyGen data objects for glycans, proteins and glycoproteins. Some of these web services are generic and provide searching, listing and detailed record access functionalities for GlyGen data objects, while others are custom designed to respond to specific biological questions or use cases collected from the user community. The GlyGen API’s webpage is shown in the Supplementary Figure S2.

3 GlyGen data model, triplestore and SPARQL endpoint

All data in the GlyGen project are also available in the Resource Description Framework (RDF) format using namespace from various existing ontologies. The UniProt Core Ontology (Redaschi and UniProt Consortium, 2009) and GlyGen Ontology are used to describe protein-centric data whereas glycan-centric data are described using the GlycoRDF Ontology (Ranzinger et al., 2015). The GlyGen Ontology along with the Glycoconjugate Ontology (https://github.com/glycoinfo/GlycoCoO) provides the necessary namespace to represent glycoprotein data.

A partial view of the GlyGen data model is given in the Supplementary Figure S4, showing a glycoprotein entry linked to a protein sequence and one or many glycosylation sites. A glycosylation site consists of an exact or fuzzy position on a protein sequence that is known to have been glycosylated by a glycan or glycan set. An exact glycosylation site position is linked to the amino acid type that occupies it. The GlyGen knowledgebase uses a Virtuoso triplestore to store GlyGen triple data, and a SPARQL Protocol and RDF Query Language (SPARQL) endpoint (https://sparql.glygen.org) is built to provide programmatic access to the triplestore. The webpage for the GlyGen SPARQL interface and triplestore content statistics for release-1.5 are shown in the Supplementary Figure S3 and Table S1, respectively.

4 Conclusion

This application note has introduced the data model and processing workflow used for building the backend for the GlyGen knowledgebase. In this processing and integration workflow, data are retrieved and extracted from a number of resources and then standardized and harmonized to create clean high-quality datasets. The dataset creation process is fully documented in a form of metadata by creating BCOs. These datasets are further processed to create JSON objects and RDF triples that populate MongoDB docstore and Virtuoso triplestore backend databases, respectively. The docstore is used by various GlyGen web services while the triplestore is accessed through the GlyGen SPARQL endpoint.

Supplementary Material

btaa238_Supplementary_Data
Click here for additional data file. (2.6MB, zip)

Acknowledgements

We would like to acknowledge the following people for their contributions: Jodie L. Abrahams, Yukie Akune, Sena Arpinar, Sanath Bhat, Judith A. Blake, Richard D. Cummings, Hayley Dingerdissen, Ten Feizi, Jefferey Gildersleeve, Radoslav Goldman, Vinamra Jain, Johnathon G. Keeney, Charles H. King, Akul Mehta, Sandeep Nakarakommula, Gareth Owen, Michael J. Pierce, Dacian Reece-Stremtan, Vijay Shankar, Tyler J. Stewart, Peng Su, Michael Tiemeyer, Robert J. Woods and Issaku Yamada.

Funding

This work was supported by the National Institutes of Health [1U01GM125267 - 01]; partly by the Australian Research Data Commons (ARDC); and Institute for Glycomics.

Conflict of Interest: none declared.

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Associated Data

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Supplementary Materials

btaa238_Supplementary_Data
Click here for additional data file. (2.6MB, zip)

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