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    Gene function prediction using semantic similarity clustering and enrichment analysis in the malaria parasite Plasmodium falciparum.

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    Authors
    Tedder, P M R
    Bradford, James R
    Needham, C J
    McConkey, G A
    Bulpitt, A J
    Westhead, D R
    Affiliation
    Institute of Molecular and Cellular Biology, University of Leeds, Leeds, UK.
    Issue Date
    2010-10-01
    
    Metadata
    Show full item record
    Abstract
    MOTIVATION: Functional genomics data provides a rich source of information that can be used in the annotation of the thousands of genes of unknown function found in most sequenced genomes. However, previous gene function prediction programs are mostly produced for relatively well-annotated organisms that often have a large amount of functional genomics data. Here, we present a novel method for predicting gene function that uses clustering of genes by semantic similarity, a naïve Bayes classifier and 'enrichment analysis' to predict gene function for a genome that is less well annotated but does has a severe effect on human health, that of the malaria parasite Plasmodium falciparum. RESULTS: Predictions for the molecular function, biological process and cellular component of P.falciparum genes were created from eight different datasets with a combined prediction also being produced. The high-confidence predictions produced by the combined prediction were compared to those produced by a simple K-nearest neighbour classifier approach and were shown to improve accuracy and coverage. Finally, two case studies are described, which investigate two biological processes in more detail, that of translation initiation and invasion of the host cell. AVAILABILITY: Predictions produced are available at http://www.bioinformatics.leeds.ac.uk/∼bio5pmrt/PAGODA.
    Citation
    Gene function prediction using semantic similarity clustering and enrichment analysis in the malaria parasite Plasmodium falciparum. 2010, 26 (19):2431-7 Bioinformatics
    Journal
    Bioinformatics
    URI
    http://hdl.handle.net/10541/115750
    DOI
    10.1093/bioinformatics/btq450
    PubMed ID
    20693320
    Type
    Article
    Language
    en
    ISSN
    1367-4811
    ae974a485f413a2113503eed53cd6c53
    10.1093/bioinformatics/btq450
    Scopus Count
    Collections
    All Paterson Institute for Cancer Research

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