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Danio rerio is a model organism used to investigate vertebrate development. Manipulation of the zebrafish genome and resultant gene products by mutation or targeted knockdown has made the zebrafish a good system for investigating gene function, providing a resource to investigate genetic contributors to phenotype and human disease. Phenotypic outcomes can be the result of gene mutation, targeted knockdown of gene products, manipulation of experimental conditions, or any combination thereof. Zebrafish have been used in various genetic and chemical screens to identify genetic and environmental contributors to phenotype and disease outcomes. The Zebrafish Information Network (ZFIN, zfin.org) is the central repository for genetic, genomic, and phenotypic data that result from research using D. rerio. Here we describe how ZFIN annotates phenotype, expression, and disease model data across various experimental designs, how we computationally determine wild-type gene expression, the phenotypic gene, and how these results allow us to propagate gene expression, phenotype, and disease model data to the correct gene, or gene related entity.

The Zebrafish Information Network (zfin.org) is the central repository for Danio rerio genetic and genomic data. The Zebrafish Information Network has served the zebrafish research community since 1994, expertly curating, integrating, and displaying zebrafish data. Key data types available at the Zebrafish Information Network include, but are not limited to, genes, alleles, human disease models, gene expression, phenotype, and gene function. The Zebrafish Information Network makes zebrafish research data Findable, Accessible, Interoperable, and Reusable through nomenclature, curatorial and annotation activities, web interfaces, and data downloads. Recently, the Zebrafish Information Network and 6 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 the Zebrafish Information Network including recent updates to the gene page to provide access to single-cell RNA sequencing 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.

The Alliance of Genome Resources (Alliance) is a consortium of the major model organism databases and the Gene Ontology that is guided by the vision of facilitating exploration of related genes in human and well-studied model organisms by providing a highly integrated and comprehensive platform that enables researchers to leverage the extensive body of genetic and genomic studies in these organisms. Initiated in 2016, the Alliance is building a central portal (www.alliancegenome.org) for access to data for the primary model organisms along with gene ontology data and human data. All data types represented in the Alliance portal (e.g. genomic data and phenotype descriptions) have common data models and workflows for curation. All data are open and freely available via a variety of mechanisms. Long-term plans for the Alliance project include a focus on coverage of additional model organisms including those without dedicated curation communities, and the inclusion of new data types with a particular focus on providing data and tools for the non-model-organism researcher that support enhanced discovery about human health and disease. Here we review current progress and present immediate plans for this new bioinformatics resource.

The Zebrafish Information Network (ZFIN) (https://zfin.org/) is the database for the model organism, zebrafish (Danio rerio). ZFIN expertly curates, organizes and provides a wide array of zebrafish genetic and genomic data, including genes, alleles, transgenic lines, gene expression, gene function, mutant phenotypes, orthology, human disease models, nomenclature and reagents. New features at ZFIN include increased support for genomic regions and for non-coding genes, and support for more expressive Gene Ontology annotations. ZFIN has recently taken over maintenance of the zebrafish reference genome sequence as part of the Genome Reference Consortium. ZFIN is also a founding member of the Alliance of Genome Resources, a collaboration of six model organism databases (MODs) and the Gene Ontology Consortium (GO). The recently launched Alliance portal (https://alliancegenome.org) provides a unified, comparative view of MOD, GO, and human data, and facilitates foundational and translational biomedical research.

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.

We propose a visual representation of bibliographic data based on shared references. Our method employs a distance metric that is derived from bibliographic coupling and then subjected to fast approximate multidimensional scaling. Its utility is demonstrated by an explorative analysis of social network publications that, most notably, depicts the genesis of an area now commonly referred to as network science. However, the example also illustrates some common pitfalls in bibliometric analysis.

The Zebrafish Model Organism Database (ZFIN; zfin.org) serves as the central repository for genetic and genomic data produced using zebrafish (Danio rerio). Data in ZFIN are either manually curated from peer-reviewed publications or submitted directly to ZFIN from various data repositories. Data types currently supported include mutants, transgenic lines, DNA constructs, gene expression, phenotypes, antibodies, morpholinos, TALENs, CRISPRs, disease models, movies, and images. The rapidly changing methods of genomic science have increased the production of data that cannot readily be represented in standard journal publications. These large data sets require web-based presentation. As the central repository for zebrafish research data, it has become increasingly important for ZFIN to provide the zebrafish research community with support for their data sets and guidance on what is required to submit these data to ZFIN. Regardless of their volume, all data that are submitted for inclusion in ZFIN must include a minimum set of information that describes the data. The aim of this chapter is to identify data types that fit into the current ZFIN database and explain how to provide those data in the optimal format for integration. We identify the required and optional data elements, define jargon, and present tools and templates that can help with the acquisition and organization of data as they are being prepared for submission to ZFIN. This information will also appear in the ZFIN wiki, where it will be updated as our services evolve over time.

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.

The Zebrafish Information Network (ZFIN, http://zfin.org), the model organism database for zebrafish, provides the central location for curated zebrafish genetic, genomic and developmental data. Extensive data integration of mutant phenotypes, genes, expression patterns, sequences, genetic markers, morpholinos, map positions, publications and community resources facilitates the use of the zebrafish as a model for studying gene function, development, behavior and disease. Access to ZFIN data is provided via web-based query forms and through bulk data files. ZFIN is the definitive source for zebrafish gene and allele nomenclature, the zebrafish anatomical ontology (AO) and for zebrafish gene ontology (GO) annotations. ZFIN plays an active role in the development of cross-species ontologies such as the phenotypic quality ontology (PATO) and the gene ontology (GO). Recent enhancements to ZFIN include (i) a new home page and navigation bar, (ii) expanded support for genotypes and phenotypes, (iii) comprehensive phenotype annotations based on anatomical, phenotypic quality and gene ontologies, (iv) a BLAST server tightly integrated with the ZFIN database via ZFIN-specific datasets, (v) a global site search and (vi) help with hands-on resources.

CORRIGENDUM: InterMOD: integrated data and tools for the unification of model organism research

Model organisms are widely used for understanding basic biology and have significantly contributed to the study of human disease. In recent years, genomic analysis has provided extensive evidence of widespread conservation of gene sequence and function amongst eukaryotes, allowing insights from model organisms to help decipher gene function in a wider range of species. The InterMOD consortium is developing an infrastructure based around the InterMine data warehouse system to integrate genomic and functional data from a number of key model organisms, leading the way to improved cross-species research. So far including budding yeast, nematode worm, fruit fly, zebrafish, rat and mouse, the project has set up data warehouses, synchronized data models and created analysis tools and links between data from different species. The project unites a number of major model organism databases, improving both the consistency and accessibility of comparative research, to the benefit of the wider scientific community.

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.

The zebrafish model organism database (ZFIN) provides a Web resource of zebrafish genomic, genetic, developmental, and phenotypic data. ZFIN curates and integrates data from current literature and from direct data submissions from laboratories. In addition, ZFIN collaborates with other bioinformatics organizations to provide links to other relevant data. These data can be accessed through a variety of Web-based search and display tools. This unit focuses on some of the basic methods to search, visualize, and analyze ZFIN data, including genes, gene expression, mutants, morphants, transgenics, anatomical structures, and antibodies. ZFIN's GBrowse genome viewer, BLAST, and protocol and antibody wikis are also discussed.

We describe an algorithm for radial layout of undirected graphs, in which nodes are constrained to concentric circles centered at the origin. Such constraints are typical, e.g., in the layout of social networks, when structural centrality is mapped to geometric centrality or when the pri- mary intention of the layout is the display of the vicinity of a distinguished node. Our approach is based on an extension of stress minimization with a weighting scheme that gradually imposes radial constraints on the inter-mediate layout during the majorization process, and thus is an attempt to preserve as much information about the graph structure as possible.

We present a method for clockwise drawings of directed cyclic graphs. It is based on the eigenvalue decomposition of a skew-symmetric matrix associated with the graph and draws edges clockwise around the center instead of downwards, as in the traditional hierarchical drawing style. The method does not require preprocessing for cycle removal or layering, which often involves computationally hard problems. We describe an efficient algorithm which produces optimal solutions, and we present some application examples.

In numerous application areas, general undirected graphs need to be drawn, and force-directed layout appears to be the most frequent choice. We present an extensive experimental study showing that, if the goal is to represent the distances in a graph well, a combination of two simple algorithms based on variants of multidimensional scaling is to be preferred because of their efficiency, reliability, and even simplicity. We also hope that details in the design of our study help advance experimental methodology in algorithm engineering and graph drawing, independent of the case at hand.

We propose a visualization approach for large dynamic graph structures with high degree variation and low diameter. In particular, we reduce visual complexity by multiple modes of representation in a single-level visualization rather than abstractions of lower levels of detail. This is useful for non-interactive display and eases dynamic layout, which we address in the online scenario. Our approach is illustrated on a family of large networks featuring all of the above structural characteristics, the physical Internet on the autonomous systems level over time.

We present a novel sampling-based approximation technique for classical multidimensional scaling that yields an extremely fast layout algorithm suitable even for very large graphs. It produces layouts that compare favorably with other methods for drawing large graphs, and it is among the fastest methods available. In addition, our approach allows for progressive computation, i.e. a rough approximation of the layout can be produced even faster, and then be refined until satisfaction.

Centrality indices are an essential concept in network analysis. For those based on shortest-path distances the computation is at least quadratic in the number of nodes, since it usually involves solving the single-source shortest-paths (SSSP) problem from every node. Therefore, exact computation is infeasible for many large networks of interest today. Centrality scores can be estimated, however, from a limited number of SSSP computations. We present results from an experimental study of the quality of such estimates under various selection strategies for the source vertices.

The efforts put into XML-related technologies have exciting consequences for XML-based graph data formats such as GraphML. We here give a systematic overview of the possibilities offered by XSLT style sheets for processing graph data, and illustrate that many basic tasks required for tools used in graph drawing can be implemented by means of style sheets, which are convenient to use, portable, and easy to customize.

We determine the zero-temperature phase diagram of the hard-core Bose-Hubbard model on a square lattice by mean-field theory supplemented by a linear spin-wave analysis. Due to the interplay between nearest and next-nearest neighbor interaction and cubic anisotropy several supersolid phases with checkerboard, stripe domain or intermediate symmetry are stabilized. The phase diagrams show three different topologies depending on the relative strength of nearest and next-nearest neighbor interaction. We also find a rich variety of new quantum critical behavior and multicritical points and discuss the corresponding effective actions and universality classes., Comment: 19 pages, ReVTeX, 18 figures included, submitted to PRB