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      The New Bioinformatics: Integrating Ecological Data from the Gene to the Biosphere

      1 , 1 , 1 , 2
      Annual Review of Ecology, Evolution, and Systematics
      Annual Reviews

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          Abstract

          Bioinformatics, the application of computational tools to the management and analysis of biological data, has stimulated rapid research advances in genomics through the development of data archives such as GenBank, and similar progress is just beginning within ecology. One reason for the belated adoption of informatics approaches in ecology is the breadth of ecologically pertinent data (from genes to the biosphere) and its highly heterogeneous nature. The variety of formats, logical structures, and sampling methods in ecology create significant challenges. Cultural barriers further impede progress, especially for the creation and adoption of data standards. Here we describe informatics frameworks for ecology, from subject-specific data warehouses, to generic data collections that use detailed metadata descriptions and formal ontologies to catalog and cross-reference information. Combining these approaches with automated data integration techniques and scientific workflow systems will maximize the value of data and open new frontiers for research in ecology.

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          Most cited references47

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            GenBank

            GenBank® is a comprehensive database that contains publicly available DNA sequences for more than 165 000 named organisms, obtained primarily through submissions from individual laboratories and batch submissions from large-scale sequencing projects. Most submissions are made using the web-based BankIt or standalone Sequin programs and accession numbers are assigned by GenBank staff upon receipt. Daily data exchange with the EMBL Data Library in the UK and the DNA Data Bank of Japan helps to ensure worldwide coverage. GenBank is accessible through NCBI's retrieval system, Entrez, which integrates data from the major DNA and protein sequence databases along with taxonomy, genome, mapping, protein structure and domain information, and the biomedical journal literature via PubMed. BLAST provides sequence similarity searches of GenBank and other sequence databases. Complete bimonthly releases and daily updates of the GenBank database are available by FTP. To access GenBank and its related retrieval and analysis services, go to the NCBI Homepage at http://www.ncbi.nlm.nih.gov.
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              A survey of approaches to automatic schema matching

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                Author and article information

                Journal
                Annual Review of Ecology, Evolution, and Systematics
                Annu. Rev. Ecol. Evol. Syst.
                Annual Reviews
                1543-592X
                1545-2069
                December 2006
                December 2006
                : 37
                : 1
                : 519-544
                Affiliations
                [1 ]National Center for Ecological Analysis and Synthesis, University of California, Santa Barbara, California 93101; email: , ,
                [2 ]Genome Center, University of California, Davis, California 95616; email:
                Article
                10.1146/annurev.ecolsys.37.091305.110031
                8cdbaa40-94c3-412d-8427-5a453bc50d1d
                © 2006
                History

                Medicine,Statistics,Cancer biology,Bioinformatics & Computational biology,Genetics,Life sciences

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