Research progress in the construction of database and analysis system in the field of microorganisms

The Big Data Technology and Application Development Department of the Computer Network Information Center of the Chinese Academy of Sciences and the Institute of Microbiology of the Chinese Academy of Sciences have made new progress in the construction of the database and analysis system in the field of microorganisms, and proposed a method to build a knowledge map using semantic web technology.
Multi-source heterogeneous data such as virus-related strains, genomes, protein sequences, protein structures, antibodies, literature and patents are mapped to the Resource Description Framework (RDF), and a coronavirus knowledge graph database gcCov based on the semantic web framework is constructed
.

gcCov contains more than 60 million semantic triples.
Through the semantic integration of multi-source heterogeneous data, it supports large-scale data-driven knowledge discovery, and has the ability to perform correlation analysis on gene, structure, antibody and other data, which is helpful for To advance future research into fundamental viral mechanisms and drug and vaccine design
.

The research results have been published in mLife
.

In recent decades, the coronavirus has continued to threaten global public health security
.

Research on the novel coronavirus is extensive, and the number of related publications is growing rapidly
.

The sheer volume of scientific research data makes it challenging to integrate different types of research into a searchable, semantically connected dataset
.

Currently, available coronavirus databases are mainly concentrated in the field of genomic analysis (such as CovDB1 and ViPR2) or the field of publications (such as LitCovid3)
.

And these databases do not establish correlations between genomic data and other types of information such as papers, patents and antibodies, hindering further knowledge discovery
.

　　The Semantic Web can integrate distributed network resources into the knowledge base of shared ontology, study the potential relationship between objects, and is an effective solution for biomedical data integration
.

To analyze the interrelationships among the massive data, this study devised a pipeline method to integrate data from different sources into the Semantic Web framework
.

Based on this approach, the study constructed the gcCov database, which uses Link Open Data to provide extensive information and associations about the coronavirus
.

gcCov is the first and only coronavirus database published using Linked Open Data and based on the Semantic Web framework
.

It helps scientists detect connections between linked data, thereby uncovering new knowledge hidden in massive amounts of data
.

Providing clues to current prevention and treatment strategies, gcCov is an important tool to meet the growing need for information in coronavirus research
.

Schematic diagram of the data processing pipeline Source: Computer Network Information Center, Chinese Academy of Sciences