A Primer for the Rat Genome Database (RGD)

Laulederkind, Stanley J. F.; Wang, Shur-Jen; Smith, Jennifer; Petri, Victoria; Hoffman, Matthew J.; Pons, Jeff De; Tutaj, Marek; Ghiasvand, Omid; Tutaj, Monika; Thota, Jyothi; Dwinell, Melinda R.; Shimoyama, Mary

doi:10.1007/978-1-4939-7737-6_8

Cited by 12 publications

(8 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 3–5 ZFIN has provided guidance to the zebrafish scientific community on minimum requirements for data submissions to ensure accurate integration with other data as well as encouraging FAIR standards. 6 , 7 In an effort to support FAIR data principles further, ZFIN and the Mouse Genome Database, 8 the Rat Genome Database, 9 WormBase, 10 FlyBase, 11 and the Gene Ontology Consortium 12 have collaborated to create the Alliance of Genome Resources (Alliance). 13 , 14 The Alliance aims to provide a centralized multispecies platform that provides access to integrated, harmonized model organism data that facilitate the use of model organisms to understand the genetic and genomic basis of human biology and disease.…”

Section: Introductionmentioning

confidence: 99%

Zebrafish information network, the knowledgebase for Danio rerio research

et al. 2022

View full text Add to dashboard Cite

The Zebrafish Information Network (ZFIN, zfin.org) is the central repository for Danio rerio genetic and genomic data. ZFIN has served the zebrafish research community since 1994, expertly curating, integrating, and displaying zebrafish data. Key data types available at ZFIN include, but are not limited to, genes, alleles, human disease models, gene expression, phenotype, and gene function. ZFIN makes zebrafish research data Findable, Accessible, Interoperable, and Reusable through nomenclature, curatorial and annotation activities, web interfaces, and data downloads. Recently, ZFIN and six other model organism knowledgebases have collaborated to form the Alliance of Genome Resources, aiming to develop sustainable genome information resources that enable the use of model organisms to understand the genetic and genomic basis of human biology and disease. Here we provide an overview of the data available at ZFIN including recent updates to the gene page to provide access to single-cell RNA sequencing (scRNA-seq) data, links to Alliance web pages, ribbon diagrams to summarize the biological systems and Gene Ontology terms that have annotations, and data integration with the Alliance of Genome Resources.

show abstract

Section: Introductionmentioning

confidence: 99%

Zebrafish information network, the knowledgebase for Danio rerio research

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Protein IDs were mapped to gene IDs by using an alias file in the STRING database. Proteins that were mapped to the same genes were merged into a single node, and the average of their interaction weights were used as the merged interaction weight. Pathway Layer: To create the pathway layer, Rattus norvegicus pathway annotation of genes and the pathway ontology tree were downloaded from the Rat Genome Database (RGD) [19]. Semantic similarity of genes was calculated using the ontologyX R package [11].…”

Section: Methodsmentioning

confidence: 99%

“…Pathway Layer: To create the pathway layer, Rattus norvegicus pathway annotation of genes and the pathway ontology tree were downloaded from the Rat Genome Database (RGD) [19]. Semantic similarity of genes was calculated using the ontologyX R package [11].…”

Section: Methodsmentioning

confidence: 99%

NECo: A node embedding algorithm for multiplex heterogeneous networks

Dursun

Smith

Kwitek

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Diseases such as hypertension, cancer, and diabetes are the causes of nearly 70% of the deaths in the U.S. Such complex diseases involve multiple genes and their interactions with environmental factors. Therefore, identification of genetic factors to understand and decrease the morbidity rates of those complex diseases is an important and challenging task. With the generation of an unprecedented amount of multi-omics datasets, network-based methods have become popular to represent the multilayered complex molecular interactions. Particularly network embeddings, the low-dimensional representations of nodes in a network are utilized for gene function prediction. Most of the network embedding methods, however, could not integrate multiple types of datasets from genes and phenotypes. This is an important limitation as multi-omics data integration alleviates the issues due to missing data and lack of context-specific data. To address this limitation, we developed a network embedding algorithm named GECo that can utilize multilayered heterogeneous networks of genes and phenotypes. We evaluated the performance of GECo using genotypic and phenotypic datasets of the model organism Rattus norvegicus to classify hypertension disease-related genes. Our method significantly outperformed the state-of-the-art network embedding methods by 94.97% AUC in prediction where the second-best performer achieved 85.98% AUC.

show abstract

“…A rdfs:label) for the disease name. Introducing ROBOT templates to the DO production pipeline has enabled the DO team and collaborators (such as Mouse Genome Informatics (MGI) (29,30), Rat Genome Database (RGD) (31), IEDB and Wikidata (32) to coordinate data updates and additions through shared Google spreadsheets. Following thorough quality control and validation, the new axioms are programmatically added to DO, thus greatly reducing the time for data integration and improving overall data quality.…”

Section: Do’s Content Workflow and Disease Classification Expansionmentioning

confidence: 99%

Human Disease Ontology 2018 update: classification, content and workflow expansion

Schriml

Mitraka

Munro

et al. 2018

Nucleic Acids Research

410

330

View full text Add to dashboard Cite

The Human Disease Ontology (DO) (http://www.disease-ontology.org), database has undergone significant expansion in the past three years. The DO disease classification includes specific formal semantic rules to express meaningful disease models and has expanded from a single asserted classification to include multiple-inferred mechanistic disease classifications, thus providing novel perspectives on related diseases. Expansion of disease terms, alternative anatomy, cell type and genetic disease classifications and workflow automation highlight the updates for the DO since 2015. The enhanced breadth and depth of the DO’s knowledgebase has expanded the DO’s utility for exploring the multi-etiology of human disease, thus improving the capture and communication of health-related data across biomedical databases, bioinformatics tools, genomic and cancer resources and demonstrated by a 6.6× growth in DO’s user community since 2015. The DO’s continual integration of human disease knowledge, evidenced by the more than 200 SVN/GitHub releases/revisions, since previously reported in our DO 2015 NAR paper, includes the addition of 2650 new disease terms, a 30% increase of textual definitions, and an expanding suite of disease classification hierarchies constructed through defined logical axioms.

show abstract

A Primer for the Rat Genome Database (RGD)

Cited by 12 publications

References 15 publications

Zebrafish information network, the knowledgebase for Danio rerio research

Zebrafish information network, the knowledgebase for Danio rerio research

NECo: A node embedding algorithm for multiplex heterogeneous networks

Human Disease Ontology 2018 update: classification, content and workflow expansion

Contact Info

Product

Resources

About