Douglas G. Howe

The Zebrafish Information Network: the zebrafish model organism database

The Zebrafish Information Network (ZFIN; ) is a web based community resource that implements the curation of zebrafish genetic, genomic and developmental data. ZFIN provides an integrated representation of mutants, genes, genetic markers, mapping panels, publications and community resources such as meeting announcements and contact information. Recent enhancements to ZFIN include (i) comprehensive curation of gene expression data from the literature and from directly submitted data, (ii) increased support and annotation of the genome sequence, (iii) expanded use of ontologies to support curation and query forms, (iv) curation of morpholino data from the literature, and (v) increased versatility of gene pages, with new data types, links and analysis tools.

ZFIN: enhancements and updates to the zebrafish model organism database

ZFIN, the Zebrafish Model Organism Database, http://zfin.org, serves as the central repository and web-based resource for zebrafish genetic, genomic, phenotypic and developmental data. ZFIN manually curates comprehensive data for zebrafish genes, phenotypes, genotypes, gene expression, antibodies, anatomical structures and publications. A wide-ranging collection of web-based search forms and tools facilitates access to integrated views of these data promoting analysis and scientific discovery. Data represented in ZFIN are derived from three primary sources: curation of zebrafish publications, individual research laboratories and collaborations with bioinformatics organizations. Data formats include text, images and graphical representations. ZFIN is a dynamic resource with data added daily as part of our ongoing curation process. Software updates are frequent. Here, we describe recent additions to ZFIN including (i) enhanced access to images, (ii) genomic features, (iii) genome browser, (iv) transcripts, (v) antibodies and (vi) a community wiki for protocols and antibodies.

ZFIN, the Zebrafish Model Organism Database: increased support for mutants and transgenics

ZFIN, the Zebrafish Model Organism Database (http://zfin.org), is the central resource for zebrafish genetic, genomic, phenotypic and developmental data. ZFIN curators manually curate and integrate comprehensive data involving zebrafish genes, mutants, transgenics, phenotypes, genotypes, gene expressions, morpholinos, antibodies, anatomical structures and publications. Integrated views of these data, as well as data gathered through collaborations and data exchanges, are provided through a wide selection of web-based search forms. Among the vertebrate model organisms, zebrafish are uniquely well suited for rapid and targeted generation of mutant lines. The recent rapid production of mutants and transgenic zebrafish is making management of data associated with these resources particularly important to the research community. Here, we describe recent enhancements to ZFIN aimed at improving our support for mutant and transgenic lines, including (i) enhanced mutant/transgenic search functionality; (ii) more expressive phenotype curation methods; (iii) new downloads files and archival data access; (iv) incorporation of new data loads from laboratories undertaking large-scale generation of mutant or transgenic lines and (v) new GBrowse tracks for transgenic insertions, genes with antibodies and morpholinos.

Towards BioDBcore: a community-defined information specification for biological databases

The present article proposes the adoption of a community-defined, uniform, generic description of the core attributes of biological databases, BioDBCore. The goals of these attributes are to provide a general overview of the database landscape, to encourage consistency and interoperability between resources; and to promote the use of semantic and syntactic standards. BioDBCore will make it easier for users to evaluate the scope and relevance of available resources. This new resource will increase the collective impact of the information present in biological databases.

Molecular and Behavioral Effects of a Null Mutation in All PKA Cβ Isoforms

Abstract The cAMP-dependent protein kinase (PKA) Cβ gene encodes three isoforms, two of which (Cβ2 and Cβ3) are transcribed from neural-specific promoters. Here we report the effects of knocking out all PKA Cβ subunit isoforms in mice. Total PKA activity was unaffected in the hippocampus and amygdala, while basal PKA activity was reduced by 26% in the brains of Cβall −/− mice despite a compensatory increase in Cα protein. Cued fear conditioning was disrupted in Cβall −/− mice when tested on a mixed C57BL/6/129 background but was indistinguishable from wild type mice when bred onto a 98% C57BL/6 background. This suggests an amygdala-specific deficit in the Cβall null mice that is sensitive to strain-specific genetic modifiers. Behavioral testing including locomotor activity, contextual fear conditioning, and conditioned taste aversion was normal in Cβall null mice on the 50% C57BL/6J background. We conclude that Cβ protein is not essential for neuronal development or function but may play a more subtle role in memory that is modulated by strain-specific genetic modifiers.

ZFIN, the Zebrafish Model Organism Database: updates and new directions

The Zebrafish Model Organism Database (ZFIN; http://zfin.org) is the central resource for genetic and genomic data from zebrafish (Danio rerio) research. ZFIN staff curate detailed information about genes, mutants, genotypes, reporter lines, sequences, constructs, antibodies, knockdown reagents, expression patterns, phenotypes, gene product function, and orthology from publications. Researchers can submit mutant, transgenic, expression, and phenotype data directly to ZFIN and use the ZFIN Community Wiki to share antibody and protocol information. Data can be accessed through topic‐specific searches, a new site‐wide search, and the data‐mining resource ZebrafishMine (http://zebrafishmine.org). Data download and web service options are also available. ZFIN collaborates with major bioinformatics organizations to verify and integrate genomic sequence data, provide nomenclature support, establish reciprocal links, and participate in the development of standardized structured vocabularies (ontologies) used for data annotation and searching. ZFIN‐curated gene, function, expression, and phenotype data are available for comparative exploration at several multi‐species resources. The use of zebrafish as a model for human disease is increasing. ZFIN is supporting this growing area with three major projects: adding easy access to computed orthology data from gene pages, curating details of the gene expression pattern changes in mutant fish, and curating zebrafish models of human diseases. genesis 53:498–509, 2015.

The Zebrafish Model Organism Database: new support for human disease models, mutation details, gene expression phenotypes and searching

The Zebrafish Model Organism Database (ZFIN; http://zfin.org) is the central resource for zebrafish (Danio rerio) genetic, genomic, phenotypic and developmental data. ZFIN curators provide expert manual curation and integration of comprehensive data involving zebrafish genes, mutants, transgenic constructs and lines, phenotypes, genotypes, gene expressions, morpholinos, TALENs, CRISPRs, antibodies, anatomical structures, models of human disease and publications. We integrate curated, directly submitted, and collaboratively generated data, making these available to zebrafish research community. Among the vertebrate model organisms, zebrafish are superbly suited for rapid generation of sequence-targeted mutant lines, characterization of phenotypes including gene expression patterns, and generation of human disease models. The recent rapid adoption of zebrafish as human disease models is making management of these data particularly important to both the research and clinical communities. Here, we describe recent enhancements to ZFIN including use of the zebrafish experimental conditions ontology, ‘Fish’ records in the ZFIN database, support for gene expression phenotypes, models of human disease, mutation details at the DNA, RNA and protein levels, and updates to the ZFIN single box search.

Representing kidney development using the gene ontology.

Gene Ontology (GO) provides dynamic controlled vocabularies to aid in the description of the functional biological attributes and subcellular locations of gene products from all taxonomic groups (www.geneontology.org). Here we describe collaboration between the renal biomedical research community and the GO Consortium to improve the quality and quantity of GO terms describing renal development. In the associated annotation activity, the new and revised terms were associated with gene products involved in renal development and function. This project resulted in a total of 522 GO terms being added to the ontology and the creation of approximately 9,600 kidney-related GO term associations to 940 UniProt Knowledgebase (UniProtKB) entries, covering 66 taxonomic groups. We demonstrate the impact of these improvements on the interpretation of GO term analyses performed on genes differentially expressed in kidney glomeruli affected by diabetic nephropathy. In summary, we have produced a resource that can be utilized in the interpretation of data from small- and large-scale experiments investigating molecular mechanisms of kidney function and development and thereby help towards alleviating renal disease.

Representing Ontogeny Through Ontology: A Developmental Biologist’s Guide to The Gene Ontology

Developmental biology, like many other areas of biology, has undergone a dramatic shift in the perspective from which developmental processes are viewed. Instead of focusing on the actions of a handful of genes or functional RNAs, we now consider the interactions of large functional gene networks and study how these complex systems orchestrate the unfolding of an organism, from gametes to adult. Developmental biologists are beginning to realize that understanding ontogeny on this scale requires the utilization of computational methods to capture, store and represent the knowledge we have about the underlying processes. Here we review the use of the Gene Ontology (GO) to study developmental biology. We describe the organization and structure of the GO and illustrate some of the ways we use it to capture the current understanding of many common developmental processes. We also discuss ways in which gene product annotations using the GO have been used to ask and answer developmental questions in a variety of model developmental systems. We provide suggestions as to how the GO might be used in more powerful ways to address questions about development. Our goal is to provide developmental biologists with enough background about the GO that they can begin to think about how they might use the ontology efficiently and in the most powerful ways possible. Mol. Reprod. Dev. 77: 314–329, 2010.

Mining the Gene Wiki for functional genomic knowledge

BackgroundOntology-based gene annotations are important tools for organizing and analyzing genome-scale biological data. Collecting these annotations is a valuable but costly endeavor. The Gene Wiki makes use of Wikipedia as a low-cost, mass-collaborative platform for assembling text-based gene annotations. The Gene Wiki is comprised of more than 10,000 review articles, each describing one human gene. The goal of this study is to define and assess a computational strategy for translating the text of Gene Wiki articles into ontology-based gene annotations. We specifically explore the generation of structured annotations using the Gene Ontology and the Human Disease Ontology.ResultsOur system produced 2,983 candidate gene annotations using the Disease Ontology and 11,022 candidate annotations using the Gene Ontology from the text of the Gene Wiki. Based on manual evaluations and comparisons to reference annotation sets, we estimate a precision of 90-93% for the Disease Ontology annotations and 48-64% for the Gene Ontology annotations. We further demonstrate that this data set can systematically improve the results from gene set enrichment analyses.ConclusionsThe Gene Wiki is a rapidly growing corpus of text focused on human gene function. Here, we demonstrate that the Gene Wiki can be a powerful resource for generating ontology-based gene annotations. These annotations can be used immediately to improve workflows for building curated gene annotation databases and knowledge-based statistical analyses.