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Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

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A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

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GlyGen data model and processing workflow.

Robel Kahsay¹, Jeet Vora¹, Rahi Navelkar¹

¹Department of Biochemistry & Molecular Medicine, The George Washington School of Medicine and Health Sciences, Washington, DC 20052, USA.

Bioinformatics (Oxford, England)

|April 24, 2020

Summary

This summary is machine-generated.

GlyGen provides a unified glycoinformatics knowledgebase by integrating diverse carbohydrate and glycoconjugate data. This resource enhances glycobiology research through accessible data models and interfaces.

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Area of Science:

Glycobiology
Bioinformatics
Computational Biology

Background:

Glycoinformatics is crucial for advancing glycobiology research.
A comprehensive, integrated knowledgebase for carbohydrate and glycoconjugate data is needed.
Existing data sources are fragmented across multiple international repositories.

Purpose of the Study:

To describe the GlyGen data model, processing workflow, and access interfaces.
To facilitate programmatic data retrieval for answering specific biological questions.
To create a harmonized and accessible resource for the glycobiology community.

Main Methods:

Data integration from multiple international sources (UniProtKB, GlyTouCan, UniCarbKB).
Development of a standardized GlyGen data model.
Implementation of data access interfaces including web services and a SPARQL endpoint.

Main Results:

The GlyGen project successfully integrated and harmonized data from diverse glycoinformatics resources.
A functional data model and processing workflow were established.
Programmatic access interfaces were developed, enabling complex biological queries.

Conclusions:

GlyGen offers a critical, centralized resource for glycobiology research.
The GlyGen data model and access methods support efficient data exploration and analysis.
The project promotes data dissemination and reuse within the scientific community.