Article

The DNA Data Bank of Japan launches a new resource, the DDBJ Omics Archive of functional genomics experiments

Center for Information Biology and DNA Data Bank of Japan, National Institute of Genetics, Research Organization for Information and Systems, Yata, Mishima 411-8510, Japan.
Nucleic Acids Research (Impact Factor: 8.81). 11/2011; 40(Database issue):D38-42. DOI: 10.1093/nar/gkr994
Source: PubMed

ABSTRACT The DNA Data Bank of Japan (DDBJ; http://www.ddbj.nig.ac.jp) maintains and provides archival, retrieval and analytical resources for biological information. The central DDBJ resource consists of public, open-access nucleotide sequence databases including raw sequence reads, assembly information and functional annotation. Database content is exchanged with EBI and NCBI within the framework of the International Nucleotide Sequence Database Collaboration (INSDC). In 2011, DDBJ launched two new resources: the 'DDBJ Omics Archive' (DOR; http://trace.ddbj.nig.ac.jp/dor) and BioProject (http://trace.ddbj.nig.ac.jp/bioproject). DOR is an archival database of functional genomics data generated by microarray and highly parallel new generation sequencers. Data are exchanged between the ArrayExpress at EBI and DOR in the common MAGE-TAB format. BioProject provides an organizational framework to access metadata about research projects and the data from the projects that are deposited into different databases. In this article, we describe major changes and improvements introduced to the DDBJ services, and the launch of two new resources: DOR and BioProject.

0 Followers
 · 
232 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Microbial genome sequence submissions to the International Nucleotide Sequence Database Collaboration (INSDC) have been annotated with organism names that include the strain identifier. Each of these strain-level names has been assigned a unique 'taxid' in the NCBI Taxonomy Database. With the significant growth in genome sequencing, it is not possible to continue with the curation of strain-level taxids. In January 2014, NCBI will cease assigning strain-level taxids. Instead, submitters are encouraged provide strain information and rich metadata with their submission to the sequence database, BioProject and BioSample.
    Standards in Genomic Sciences 06/2014; 9(3):1275-7. DOI:10.4056/sigs.4851102 · 3.17 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Biological databases vary enormously in size and data complexity, from small databases that contain a few million Resource Description Framework (RDF) triples to large databases that contain billions of triples. In this paper, we evaluate whether RDF native stores can be used to meet the needs of a biological database provider. Prior evaluations have used synthetic data with a limited database size. For example, the largest BSBM benchmark uses 1 billion synthetic e-commerce knowledge RDF triples on a single node. However, real world biological data differs from the simple synthetic data much. It is difficult to determine whether the synthetic e-commerce data is efficient enough to represent biological databases. Therefore, for this evaluation, we used five real data sets from biological databases.
    Journal of Biomedical Semantics 07/2014; 5:32. DOI:10.1186/2041-1480-5-32
  • [Show abstract] [Hide abstract]
    ABSTRACT: The life sciences field is entering an era of big data with the breakthroughs of science and technology. More and more big data-related projects and activities are being performed in the world. Life sciences data generated by new technologies are continuing to grow in not only size but also variety and complexity, with great speed. To ensure that big data has a major influence in the life sciences, comprehensive data analysis across multiple data sources and even across disciplines is indispensable. The increasing volume of data and the heterogeneous, complex varieties of data are two principal issues mainly discussed in life science informatics. The ever-evolving next-generation Web, characterized as the Semantic Web, is an extension of the current Web, aiming to provide information for not only humans but also computers to semantically process large-scale data. The paper presents a survey of big data in life sciences, big data related projects and Semantic Web technologies. The paper introduces the main Semantic Web technologies and their current situation, and provides a detailed analysis of how Semantic Web technologies address the heterogeneous variety of life sciences big data. The paper helps to understand the role of Semantic Web technologies in the big data era and how they provide a promising solution for the big data in life sciences.
    Bioscience trends 08/2014; 8(4):192-201. DOI:10.5582/bst.2014.01048 · 1.21 Impact Factor

Full-text (4 Sources)

Download
65 Downloads
Available from
May 23, 2014