RESOURCE

Development and Evaluation of an Automated Annotation Pipeline and cDNA Annotation System

    • 1Laboratory for Genome Exploration Research Group, RIKEN Genomic Sciences Center (GSC), RIKEN Yokohama Institute, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
    • 2Multimedia Development Center, Advanced Technology Development Department, NTT Software Corporation, Yokohama, Kanagawa 231-8554, Japan
    • 3Institute for Molecular Bioscience and ARC Special Research Centre for Functional and Applied Genomics, Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland 4072, Australia
    • 4Mouse Genome Informatics Group, The Jackson Laboratory, Bar Harbor, Maine 04609, USA
    • 5Structural Studies, MRC Laboratory of Molecular Biology, Hills Road, Cambridge, CB2 2QH, UK
    • 6The European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
    • 7Graduate School of Information Science and Technology, Osaka University, Toyonaka, Osaka 560-8531, Japan
    • 8The National Center for Biotechnology Information, National Institutes of Health, Bethesda, Maryland 20894, USA
    • 9Genome Science Laboratory, RIKEN, Hirosawa, Wako, Saitama 351-0198, Japan
    • 10The Institute for Genomic Research, Rockville, Maryland 20850, USA
Published June 2, 2003. Vol 13 Issue 6b, pp. 1542-1551. https://doi.org/10.1101/gr.992803
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Abstract

Manual curation has long been held to be the “gold standard” for functional annotation of DNA sequence. Our experience with the annotation of more than 20,000 full-length cDNA sequences revealed problems with this approach, including inaccurate and inconsistent assignment of gene names, as well as many good assignments that were difficult to reproduce using only computational methods. For the FANTOM2 annotation of more than 60,000 cDNA clones, we developed a number of methods and tools to circumvent some of these problems, including an automated annotation pipeline that provides high-quality preliminary annotation for each sequence by introducing an “uninformative filter” that eliminates uninformative annotations, controlled vocabularies to accurately reflect both the functional assignments and the evidence supporting them, and a highly refined, Web-based manual annotation tool that allows users to view a wide array of sequence analyses and to assign gene names and putative functions using a consistent nomenclature. The ultimate utility of our approach is reflected in the low rate of reassignment of automated assignments by manual curation. Based on these results, we propose a new standard for large-scale annotation, in which the initial automated annotations are manually investigated and then computational methods are iteratively modified and improved based on the results of manual curation.

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