Database The Journal of Biological Databases and Curation

Publisher: Oxford Journals (Firm), Oxford University Press (OUP)

Current impact factor: 3.37

Impact Factor Rankings

2016 Impact Factor Available summer 2017
2014 / 2015 Impact Factor 3.372
2013 Impact Factor 4.457
2012 Impact Factor 4.2
2011 Impact Factor 2.071

Impact factor over time

Impact factor

Additional details

5-year impact 4.51
Cited half-life 3.20
Immediacy index 0.61
Eigenfactor 0.01
Article influence 1.68
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 (OUP)

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  • Classification

Publications in this journal

  • Source
    [Show abstract] [Hide abstract] ABSTRACT: PhyloPro is a database and accompanying web-based application for the construction and exploration of phylogenetic profiles across the Eukarya. In this update article, we present six major new developments in PhyloPro: (i) integration of Pfam-A domain predictions for all proteins; (ii) new summary heatmaps and detailed level views of domain conservation; (iii) an interactive, network-based visualization tool for exploration of domain architectures and their conservation; (iv) ability to browse based on protein functional categories (GOSlim); (v) improvements to the web interface to enhance drill down capability from the heatmap view; and (vi) improved coverage including 164 eukaryotes and 12 reference species. In addition, we provide improved support for downloading data and images in a variety of formats. Among the existing tools available for phylogenetic profiles, PhyloPro provides several innovative domain-based features including a novel domain adjacency visualization tool. These are designed to allow the user to identify and compare proteins with similar domain architectures across species and thus develop hypotheses about the evolution of lineage-specific trajectories. Database URL:
    Full-text · Article · Jan 2016 · Database The Journal of Biological Databases and Curation
  • Source
    [Show abstract] [Hide abstract] ABSTRACT: The Rat Genome Database (RGD; provides critical datasets and software tools to a diverse community of rat and non-rat researchers worldwide. To meet the needs of the many users whose research is disease oriented, RGD has created a series of Disease Portals and has prioritized its curation efforts on the datasets important to understanding the mechanisms of various diseases. Gene-disease relationships for three species, rat, human and mouse, are annotated to capture biomarkers, genetic associations, molecular mechanisms and therapeutic targets. To generate gene–disease annotations more effectively and in greater detail, RGD initially adopted the MEDIC disease vocabulary from the Comparative Toxicogenomics Database and adapted it for use by expanding this framework with the addition of over 1000 terms to create the RGD Disease Ontology (RDO). The RDO provides the foundation for, at present, 10 comprehensive disease area-related dataset and analysis platforms at RGD, the Disease Portals. Two major disease areas are the focus of data acquisition and curation efforts each year, leading to the release of the related Disease Portals. Collaborative efforts to realize a more robust disease ontology are underway. Database URL:
    Full-text · Article · Jan 2016 · Database The Journal of Biological Databases and Curation
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    [Show abstract] [Hide abstract] ABSTRACT: Rice [Oryza sativa (L.)] feeds more than half of the world’s population. Rhizoctonia solani is a major fungal pathogen of rice causing extreme crop losses in all rice-growing regions of the world. R. solani AG1 IA is a major cause of sheath blight in rice. In this study, we constructed a comprehensive and user-friendly web-based database, RSIADB, to analyse its draft genome and transcriptome. The database was built using the genome sequence (10 489 genes) and annotation information for R. solani AG1 IA. A total of six RNAseq samples of R. solani AG1 IA were also analysed, corresponding to 10, 18, 24, 32, 48 and 72 h after infection of rice leaves. The RSIADB database enables users to search, browse, and download gene sequences for R. solani AG1 IA, and mine the data using BLAST, Sequence Extractor, Browse and Construction Diagram tools that were integrated into the database. RSIADB is an important genomic resource for scientists working with R. solani AG1 IA and will assist researchers in analysing the annotated genome and transcriptome of this pathogen. This resource will facilitate studies on gene function, pathogenesis factors and secreted proteins, as well as provide an avenue for comparative analyses of genes expressed during different stages of infection. Database URL:
    Full-text · Article · Jan 2016 · Database The Journal of Biological Databases and Curation
  • No preview · Article · Jan 2016 · Database The Journal of Biological Databases and Curation
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    [Show abstract] [Hide abstract] ABSTRACT: Annotation of orthologous and paralogous genes is necessary for many aspects of evolutionary analysis. Methods to infer these homology relationships have traditionally focused on protein-coding genes and evolutionary models used by these methods normally assume the positions in the protein evolve independently. However, as our appreciation for the roles of non-coding RNA genes has increased, consistently annotated sets of orthologous and paralogous ncRNA genes are increasingly needed. At the same time, methods such as PHASE or RAxML have implemented substitution models that consider pairs of sites to enable proper modelling of the loops and other features of RNA secondary structure. Here, we present a comprehensive analysis pipeline for the automatic detection of orthologues and paralogues for ncRNA genes. We focus on gene families represented in Rfam and for which a specific covariance model is provided. For each family ncRNA genes found in all Ensembl species are aligned using Infernal, and several trees are built using different substitution models. In parallel, a genomic alignment that includes the ncRNA genes and their flanking sequence regions is built with PRANK. This alignment is used to create two additional phylogenetic trees using the neighbour-joining (NJ) and maximum-likelihood (ML) methods. The trees arising from both the ncRNA and genomic alignments are merged using TreeBeST, which reconciles them with the species tree in order to identify speciation and duplication events. The final tree is used to infer the orthologues and paralogues following Fitch's definition. We also determine gene gain and loss events for each family using CAFE. All data are accessible through the Ensembl Comparative Genomics (‘Compara’) API, on our FTP site and are fully integrated in the Ensembl genome browser, where they can be accessed in a user-friendly manner. Database URL:
    Full-text · Article · Jan 2016 · Database The Journal of Biological Databases and Curation
  • No preview · Article · Jan 2016 · Database The Journal of Biological Databases and Curation
  • No preview · Article · Jan 2016 · Database The Journal of Biological Databases and Curation
  • Source
    [Show abstract] [Hide abstract] ABSTRACT: HitPredict is a consolidated resource of experimentally identified, physical protein-protein interactions with confidence scores to indicate their reliability. The study of genes and their inter-relationships using methods such as network and pathway analysis requires high quality protein-protein interaction information. Extracting reliable interactions from most of the existing databases is challenging because they either contain only a subset of the available interactions, or a mixture of physical, genetic and predicted interactions. Automated integration of interactions is further complicated by varying levels of accuracy of database content and lack of adherence to standard formats. To address these issues, the latest version of HitPredict provides a manually curated dataset of 398 696 physical associations between 70 808 proteins from 105 species. Manual confirmation was used to resolve all issues encountered during data integration. For improved reliability assessment, this version combines a new score derived from the experimental information of the interactions with the original score based on the features of the interacting proteins. The combined interaction score performs better than either of the individual scores in HitPredict as well as the reliability score of another similar database. HitPredict provides a web interface to search proteins and visualize their interactions, and the data can be downloaded for offline analysis. Data usability has been enhanced by mapping protein identifiers across multiple reference databases. Thus, the latest version of HitPredict provides a significantly larger, more reliable and usable dataset of protein-protein interactions from several species for the study of gene groups.Database URL:
    Full-text · Article · Dec 2015 · Database The Journal of Biological Databases and Curation