Analysis of disease-associated objects at the Rat Genome Database

Ontological Analysis of Coronavirus Associated Human Genes at the COVID-19 Disease Portal

The COVID-19 pandemic stemmed a parallel upsurge in the scientific literature about SARS-CoV-2 infection and its health burden. The Rat Genome Database (RGD) created a COVID-19 Disease Portal to leverage information from the scientific literature. In the COVID-19 Portal, gene-disease associations are established by manual curation of PubMed literature. The portal contains data for nine ontologies related to COVID-19, an embedded enrichment analysis tool, as well as links to a toolkit. Using these information and tools, we performed analyses on the curated COVID-19 disease genes. As expected, Disease Ontology enrichment analysis showed that the COVID-19 gene set is highly enriched with coronavirus infectious disease and related diseases. However, other less related diseases were also highly enriched, such as liver and rheumatic diseases. Using the comparison heatmap tool, we found nearly 60 percent of the COVID-19 genes were associated with nervous system disease and 40 percent were associated with gastrointestinal disease. Our analysis confirms the role of the immune system in COVID-19 pathogenesis as shown by substantial enrichment of immune system related Gene Ontology terms. The information in RGD's COVID-19 disease portal can generate new hypotheses to potentiate novel therapies and prevention of acute and long-term complications of COVID-19.

HumanMine: advanced data searching, analysis and cross-species comparison

HumanMine (www.humanmine.org) is an integrated database of human genomics and proteomics data that provides a powerful interface to support sophisticated exploration and analysis of data compiled from experimental, computational and curated data sources. Built using the InterMine data integration platform, HumanMine includes genes, proteins, pathways, expression levels, Single nucleotide polymorphism (SNP), diseases and more, integrated into a single searchable database. HumanMine promotes integrative analysis, a powerful approach in modern biology that allows many sources of evidence to be analysed together. The data can be accessed through a user-friendly web interface as well as a powerful, scriptable web service Application programming interface (API) to allow programmatic access to data. The web interface includes a useful identifier resolution system, sophisticated query options and interactive results tables that enable powerful exploration of data, including data summaries, filtering, browsing and export. A set of graphical analysis tools provide a rich environment for data exploration including statistical enrichment of sets of genes or other biological entities. HumanMine can be used for integrative multistaged analysis that can lead to new insights and uncover previously unknown relationships.

Database URL: https://www.humanmine.org

Improving Interpretation of Cardiac Phenotypes and Enhancing Discovery With Expanded Knowledge in the Gene Ontology

Supplemental Digital Content is available in the text.

Background:

A systems biology approach to cardiac physiology requires a comprehensive representation of how coordinated processes operate in the heart, as well as the ability to interpret relevant transcriptomic and proteomic experiments. The Gene Ontology (GO) Consortium provides structured, controlled vocabularies of biological terms that can be used to summarize and analyze functional knowledge for gene products.

Methods and Results:

In this study, we created a computational resource to facilitate genetic studies of cardiac physiology by integrating literature curation with attention to an improved and expanded ontological representation of heart processes in the Gene Ontology. As a result, the Gene Ontology now contains terms that comprehensively describe the roles of proteins in cardiac muscle cell action potential, electrical coupling, and the transmission of the electrical impulse from the sinoatrial node to the ventricles. Evaluating the effectiveness of this approach to inform data analysis demonstrated that Gene Ontology annotations, analyzed within an expanded ontological context of heart processes, can help to identify candidate genes associated with arrhythmic disease risk loci.

Conclusions:

We determined that a combination of curation and ontology development for heart-specific genes and processes supports the identification and downstream analysis of genes responsible for the spread of the cardiac action potential through the heart. Annotating these genes and processes in a structured format facilitates data analysis and supports effective retrieval of gene-centric information about cardiac defects.

LasDB: A collective database for laboratory animal strain resources

BACKGROUND

With the aim of establishing the most comprehensive database of laboratory animal strains, the "laboratory animal strain resources database" (LasDB) was constructed as a searchable online database of all laboratory animal strains, stocks and mutant embryonic stem-cell lines available worldwide, including inbred, outbred, mutant and genetically engineered strains.

METHODS

MySQL database software was used to construct the LasDB, offering an easy-to-use interface.

RESULTS

To date, LasDB has a collection covering data for 21 596 mouse strains, 2062 rat strains, 13 monkey strains, 2 hamster strains, 5 dog strains, 5 rabbit strains and more than 50 other laboratory animal strains. LasDB will be continually improved with regular updates of new laboratory animal strains from all over the world.

CONCLUSION

To the best of our knowledge, this is the first database that attempts to systematically integrate all available laboratory animal strain data with the aim of supporting open usage and full resource sharing.

The InterMine Android app: Cross-organism genomic data in your pocket

InterMine is a data integration and analysis software system that has been used to create both inter-connected and stand-alone biological databases for the analysis of large and complex biological data sets. Together, the InterMine databases provide access to extensive data across multiple organisms. To provide more convenient access to these data from Android mobile devices, we have developed the InterMine app, an application that can be run on any Android mobile phone or tablet. The InterMine app provides a single interface for data access, search and exploration of the InterMine databases. It can be used to retrieve information on genes and gene lists, and their relatives across species. Simple searches can be used to access a range of data about a specific gene, while links to the InterMine databases provide access to more detailed report pages and gene list analysis tools. The InterMine app thus facilitates rapid exploration of genes across multiple organisms and kinds of data.

The InterMine Android app: Cross-organism genomic data in your pocket

InterMine is a data integration and analysis software system that has been used to create both inter-connected and stand-alone biological databases for the analysis of large and complex biological data sets. Together, the InterMine databases provide access to extensive data across multiple organisms. To provide more convenient access to these data from Android mobile devices, we have developed the InterMine app, an application that can be run on any Android mobile phone or tablet. The InterMine app provides a single interface for data access, search and exploration of the InterMine databases. It can be used to retrieve information on genes and gene lists, and their relatives across species. Simple searches can be used to access a range of data about a specific gene, while links to the InterMine databases provide access to more detailed report pages and gene list analysis tools. The InterMine app thus facilitates rapid exploration of genes across multiple organisms and kinds of data.

AgBioData consortium recommendations for sustainable genomics and genetics databases for agriculture

The future of agricultural research depends on data. The sheer volume of agricultural biological data being produced today makes excellent data management essential. Governmental agencies, publishers and science funders require data management plans for publicly funded research. Furthermore, the value of data increases exponentially when they are properly stored, described, integrated and shared, so that they can be easily utilized in future analyses. AgBioData (https://www.agbiodata.org) is a consortium of people working at agricultural biological databases, data archives and knowledgbases who strive to identify common issues in database development, curation and management, with the goal of creating database products that are more Findable, Accessible, Interoperable and Reusable. We strive to promote authentic, detailed, accurate and explicit communication between all parties involved in scientific data. As a step toward this goal, we present the current state of biocuration, ontologies, metadata and persistence, database platforms, programmatic (machine) access to data, communication and sustainability with regard to data curation. Each section describes challenges and opportunities for these topics, along with recommendations and best practices.

The Rat Genome Database 2015: genomic, phenotypic and environmental variations and disease

The Rat Genome Database (RGD, http://rgd.mcw.edu) provides the most comprehensive data repository and informatics platform related to the laboratory rat, one of the most important model organisms for disease studies. RGD maintains and updates datasets for genomic elements such as genes, transcripts and increasingly in recent years, sequence variations, as well as map positions for multiple assemblies and sequence information. Functional annotations for genomic elements are curated from published literature, submitted by researchers and integrated from other public resources. Complementing the genomic data catalogs are those associated with phenotypes and disease, including strains, QTL and experimental phenotype measurements across hundreds of strains. Data are submitted by researchers, acquired through bulk data pipelines or curated from published literature. Innovative software tools provide users with an integrated platform to query, mine, display and analyze valuable genomic and phenomic datasets for discovery and enhancement of their own research. This update highlights recent developments that reflect an increasing focus on: (i) genomic variation, (ii) phenotypes and diseases, (iii) data related to the environment and experimental conditions and (iv) datasets and software tools that allow the user to explore and analyze the interactions among these and their impact on disease.

Transcriptional profiling in rat hair follicles following simulated Blast insult: a new diagnostic tool for traumatic brain injury

With wide adoption of explosive-dependent weaponry during military activities, Blast-induced neurotrauma (BINT)-induced traumatic brain injury (TBI) has become a significant medical issue. Therefore, a robust and accessible biomarker system is in demand for effective and efficient TBI diagnosis. Such systems will also be beneficial to studies of TBI pathology. Here we propose the mammalian hair follicles as a potential candidate. An Advanced Blast Simulator (ABS) was developed to generate shock waves simulating traumatic conditions on brains of rat model. Microarray analysis was performed in hair follicles to identify the gene expression profiles that are associated with shock waves. Gene set enrichment analysis (GSEA) and sub-network enrichment analysis (SNEA) were used to identify cell processes and molecular signaling cascades affected by simulated bomb blasts. Enrichment analyses indicated that genes with altered expression levels were involved in central nervous system (CNS)/peripheral nervous system (PNS) responses as well as signal transduction including Ca2+, K+-transportation-dependent signaling, Toll-Like Receptor (TLR) signaling and Mitogen Activated Protein Kinase (MAPK) signaling cascades. Many of the pathways identified as affected by shock waves in the hair follicles have been previously reported to be TBI responsive in other organs such as brain and blood. The results suggest that the hair follicle has some common TBI responsive molecular signatures to other tissues. Moreover, various TBI-associated diseases were identified as preferentially affected using a gene network approach, indicating that the hair follicle may be capable of reflecting comprehensive responses to TBI conditions. Accordingly, the present study demonstrates that the hair follicle is a potentially viable system for rapid and non-invasive TBI diagnosis.