Database The Journal of Biological Databases and Curation

Publisher: Oxford Journals (Firm), Oxford University Press

Description

  • Impact factor
    4.20
  • 5-year impact
    4.19
  • Cited half-life
    2.20
  • Immediacy index
    0.73
  • Eigenfactor
    0.00
  • Article influence
    1.80
  • Other titles
    Journal of biological databases and curation
  • ISSN
    1758-0463
  • OCLC
    319891682
  • Material type
    Document, Periodical, Internet resource
  • Document type
    Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details

Oxford University Press

  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author cannot archive a post-print version
  • Restrictions
    • 12 month embargo on science, technology, medicine articles
    • 24 month embargo on arts and humanities articles
    • Some titles may have different embargoes
  • Conditions
    • Pre-print can only be posted prior to acceptance
    • Pre-print must be accompanied by set statement (see link)
    • Pre-print must not be replaced with post-print, instead a link to published version with amended set statement should be made
    • Pre-print on personal website, employer website, free public server or pre-prints in subject area
    • Post-print on Institutional or Central repositories
    • Publisher version cannot be used except for Nucleic Acids Research articles
    • Published source must be acknowledged
    • Must link to publisher version
    • Set phrase to accompany archived copy (see policy)
    • Articles in some journals can be made Open Access on payment of additional charge
    • Eligible UK authors may deposit in OpenDepot
    • Publisher will deposit on behalf of NIH funded authors to PubMed Central, Nucleic Acids Research authors must pay their fee first
    • Some titles may use different policies
  • Classification
    ‚Äč yellow

Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: Gene Ontology (GO) annotation is a common task among model organism databases (MODs) for capturing gene function data from journal articles. It is a time-consuming and labor-intensive task, and is thus often considered as one of the bottlenecks in literature curation. There is a growing need for semiautomated or fully automated GO curation techniques that will help database curators to rapidly and accurately identify gene function information in full-length articles. Despite multiple attempts in the past, few studies have proven to be useful with regard to assisting real-world GO curation. The shortage of sentence-level training data and opportunities for interaction between text-mining developers and GO curators has limited the advances in algorithm development and corresponding use in practical circumstances. To this end, we organized a text-mining challenge task for literature-based GO annotation in BioCreative IV. More specifically, we developed two subtasks: (i) to automatically locate text passages that contain GO-relevant information (a text retrieval task) and (ii) to automatically identify relevant GO terms for the genes in a given article (a concept-recognition task). With the support from five MODs, we provided teams with >4000 unique text passages that served as the basis for each GO annotation in our task data. Such evidence text information has long been recognized as critical for text-mining algorithm development but was never made available because of the high cost of curation. In total, seven teams participated in the challenge task. From the team results, we conclude that the state of the art in automatically mining GO terms from literature has improved over the past decade while much progress is still needed for computer-assisted GO curation. Future work should focus on addressing remaining technical challenges for improved performance of automatic GO concept recognition and incorporating practical benefits of text-mining tools into real-world GO annotation. Database URL: http://www.biocreative.org/tasks/biocreative-iv/track-4-GO/.
    Database The Journal of Biological Databases and Curation 01/2014; 2014.
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    ABSTRACT: CanvasDB is an infrastructure for management and analysis of genetic variants from massively parallel sequencing (MPS) projects. The system stores SNP and indel calls in a local database, designed to handle very large datasets, to allow for rapid analysis using simple commands in R. Functional annotations are included in the system, making it suitable for direct identification of disease-causing mutations in human exome- (WES) or whole-genome sequencing (WGS) projects. The system has a built-in filtering function implemented to simultaneously take into account variant calls from all individual samples. This enables advanced comparative analysis of variant distribution between groups of samples, including detection of candidate causative mutations within family structures and genome-wide association by sequencing. In most cases, these analyses are executed within just a matter of seconds, even when there are several hundreds of samples and millions of variants in the database. We demonstrate the scalability of canvasDB by importing the individual variant calls from all 1092 individuals present in the 1000 Genomes Project into the system, over 4.4 billion SNPs and indels in total. Our results show that canvasDB makes it possible to perform advanced analyses of large-scale WGS projects on a local server. Database URL: https://github.com/UppsalaGenomeCenter/CanvasDB.
    Database The Journal of Biological Databases and Curation 01/2014; 2014.
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    ABSTRACT: The Critical Assessment of Information Extraction systems in Biology (BioCreAtIvE) challenge evaluation tasks collectively represent a community-wide effort to evaluate a variety of text-mining and information extraction systems applied to the biological domain. The BioCreative IV Workshop included five independent subject areas, including Track 3, which focused on named-entity recognition (NER) for the Comparative Toxicogenomics Database (CTD; http://ctdbase.org). Previously, CTD had organized document ranking and NER-related tasks for the BioCreative Workshop 2012; a key finding of that effort was that interoperability and integration complexity were major impediments to the direct application of the systems to CTD's text-mining pipeline. This underscored a prevailing problem with software integration efforts. Major interoperability-related issues included lack of process modularity, operating system incompatibility, tool configuration complexity and lack of standardization of high-level inter-process communications. One approach to potentially mitigate interoperability and general integration issues is the use of Web services to abstract implementation details; rather than integrating NER tools directly, HTTP-based calls from CTD's asynchronous, batch-oriented text-mining pipeline could be made to remote NER Web services for recognition of specific biological terms using BioC (an emerging family of XML formats) for inter-process communications. To test this concept, participating groups developed Representational State Transfer /BioC-compliant Web services tailored to CTD's NER requirements. Participants were provided with a comprehensive set of training materials. CTD evaluated results obtained from the remote Web service-based URLs against a test data set of 510 manually curated scientific articles. Twelve groups participated in the challenge. Recall, precision, balanced F-scores and response times were calculated. Top balanced F-scores for gene, chemical and disease NER were 61, 74 and 51%, respectively. Response times ranged from fractions-of-a-second to over a minute per article. We present a description of the challenge and summary of results, demonstrating how curation groups can effectively use interoperable NER technologies to simplify text-mining pipeline implementation. Database URL: http://ctdbase.org/
    Database The Journal of Biological Databases and Curation 01/2014; 2014.
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    ABSTRACT: Emerging infectious diseases remain a significant threat to public health. Most emerging infectious disease agents in humans are of zoonotic origin. Bats are important reservoir hosts of many highly lethal zoonotic viruses and have been implicated in numerous emerging infectious disease events in recent years. It is essential to enhance our knowledge and understanding of the genetic diversity of the bat-associated viruses to prevent future outbreaks. To facilitate further research, we constructed the database of bat-associated viruses (DBatVir). Known viral sequences detected in bat samples were manually collected and curated, along with the related metadata, such as the sampling time, location, bat species and specimen type. Additional information concerning the bats, including common names, diet type, geographic distribution and phylogeny were integrated into the database to bridge the gap between virologists and zoologists. The database currently covers >4100 bat-associated animal viruses of 23 viral families detected from 196 bat species in 69 countries worldwide. It provides an overview and snapshot of the current research regarding bat-associated viruses, which is essential now that the field is rapidly expanding. With a user-friendly interface and integrated online bioinformatics tools, DBatVir provides a convenient and powerful platform for virologists and zoologists to analyze the virome diversity of bats, as well as for epidemiologists and public health researchers to monitor and track current and future bat-related infectious diseases. Database URL: http://www.mgc.ac.cn/DBatVir/
    Database The Journal of Biological Databases and Curation 01/2014; 2014:bau021.
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    ABSTRACT: Despite great biological and computational efforts to determine the genetic causes underlying human heritable diseases, approximately half (3500) of these diseases are still without an identified genetic cause. Model organism studies allow the targeted modification of the genome and can help with the identification of genetic causes for human diseases. Targeted modifications have led to a vast amount of model organism data. However, these data are scattered across different databases, preventing an integrated view and missing out on contextual information. Once we are able to combine all the existing resources, will we be able to fully understand the causes underlying a disease and how species differ. Here, we present an integrated data resource combining tissue expression with phenotypes in mouse lines and bringing us one step closer to consequence chains from a molecular level to a resulting phenotype. Mutations in genes often manifest in phenotypes in the same tissue that the gene is expressed in. However, in other cases, a systems level approach is required to understand how perturbations to gene-networks connecting multiple tissues lead to a phenotype. Automated evaluation of the predicted tissue-phenotype associations reveals that 72-76% of the phenotypes are associated with disruption of genes expressed in the affected tissue. However, 55-64% of the individual phenotype-tissue associations show spatially separated gene expression and phenotype manifestation. For example, we see a correlation between 'total body fat' abnormalities and genes expressed in the 'brain', which fits recent discoveries linking genes expressed in the hypothalamus to obesity. Finally, we demonstrate that the use of our predicted tissue-phenotype associations can improve the detection of a known disease-gene association when combined with a disease gene candidate prediction tool. For example, JAK2, the known gene associated with Familial Erythrocytosis 1, rises from the seventh best candidate to the top hit when the associated tissues are taken into consideration. Database URL: http://www.sanger.ac.uk/resources/databases/phenodigm/phenotype/list.
    Database The Journal of Biological Databases and Curation 01/2014; 2014:bau017.
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    ABSTRACT: We present a bioinformatics database named Renal Gene Expression Database (RGED), which contains comprehensive gene expression data sets from renal disease research. The web-based interface of RGED allows users to query the gene expression profiles in various kidney-related samples, including renal cell lines, human kidney tissues and murine model kidneys. Researchers can explore certain gene profiles, the relationships between genes of interests and identify biomarkers or even drug targets in kidney diseases. The aim of this work is to provide a user-friendly utility for the renal disease research community to query expression profiles of genes of their own interest without the requirement of advanced computational skills. Availability and implementation: Website is implemented in PHP, R, MySQL and Nginx and freely available from http://rged.wall-eva.net. Database URL: http://rged.wall-eva.net.
    Database The Journal of Biological Databases and Curation 01/2014; 2014.
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    ABSTRACT: The Cancer Genomics Hub (CGHub) is the online repository of the sequencing programs of the National Cancer Institute (NCI), including The Cancer Genomics Atlas (TCGA), the Cancer Cell Line Encyclopedia (CCLE) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) projects, with data from 25 different types of cancer. The CGHub currently contains >1.4 PB of data, has grown at an average rate of 50 TB a month and serves >100 TB per week. The architecture of CGHub is designed to support bulk searching and downloading through a Web-accessible application programming interface, enforce patient genome confidentiality in data storage and transmission and optimize for efficiency in access and transfer. In this article, we describe the design of these three components, present performance results for our transfer protocol, GeneTorrent, and finally report on the growth of the system in terms of data stored and transferred, including estimated limits on the current architecture. Our experienced-based estimates suggest that centralizing storage and computational resources is more efficient than wide distribution across many satellite labs. Database URL: https://cghub.ucsc.edu.
    Database The Journal of Biological Databases and Curation 01/2014; 2014.