GenomicKB: a knowledge graph for the human genome

Abstract

Genomic Knowledgebase (GenomicKB) is a graph database for researchers to explore and investigate human genome, epigenome, transcriptome, and 4D nucleome with simple and efficient queries. The database uses a knowledge graph to consolidate genomic datasets and annotations from over 30 consortia and portals, including 347 million genomic entities, 1.36 billion relations, and 3.9 billion entity and relation properties. GenomicKB is equipped with a web-based query system (https://gkb.dcmb.med.umich.edu/) which allows users to query the knowledge graph with customized graph patterns and specific constraints on entities and relations. Compared with traditional tabular-structured data stored in separate data portals, GenomicKB emphasizes the relations among genomic entities, intuitively connects isolated data matrices, and supports efficient queries for scientific discoveries. GenomicKB transforms complicated analysis among multiple genomic entities and relations into coding-free queries, and facilitates data-driven genomic discoveries in the future.

Phenome-wide identification of therapeutic genetic targets, leveraging knowledge graphs, graph neural networks, and UK Biobank data

“…Second, it may be possible to additionally exploit NLP techniques to expand the input gene lists used by Mantis-ML, thereby enhancing score robustness, with gene lists from semantically similar diseases aggregated (taking, e.g., the union) to ensure a larger number of positive examples in training. Third, it could be instructive to include additional datasets, leveraging results from any number of preexisting knowledge graph databases to further enhance predictions ( 33 , 34 ). Last, it may also be possible to integrate PheWAS results directly in training, taking their association indicator as another gene feature and predicting scores across the exome using a form of cross-validation, ensuring that final scores are “out of fold.” This is left as further research for the future versions of Mantis-ML.…”

FAIR Data and Software: Improving Efficiency and Quality of Biocatalytic Science

“…An ontology is a framework or schema that can be instantiated by data, such as genes and their function annotation as defined by the Gene Ontology. Integrating different databases or data models from different domains with the help of an ontology enables querying and reasoning using a knowledge graph system …”

“…Integrating different databases or data models from different domains with the help of an ontology enables querying and reasoning using a knowledge graph system. 55 However, standards and ontologies are yet not implemented within the tools that are applied in the daily laboratory routines by experimentalists for the analysis of data, the modeling of processes, and the design of experiments. There is still a considerable gap between the teams that develop and discuss domain ontologies and the bench scientist who would apply them in their daily life.…”

Positioning Genomic Features in Biomedical Knowledge Graphs using the Homo sapiens Chromosomal Location Ontology for GRCh38 (HSCLO38)

“…HSCLO38’s purpose is to simplify the integration of genomic experimental data at different resolution scales within any biomedical knowledge graph. Other knowledge graphs have been designed for identifying, mapping, or analyzing genomic features (Feng et al 2022), however, HSCLO38 is a system designed to be utilized by any knowledge graph interested in utilizing a method for rapid integration of genomic features by GRCh38.…”