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


BACKGROUND
Predictions produced are available at http://www.bioinformatics.leeds.ac.uk/∼bio5pmrt/PAGODA.
Tedder
Philip M R
PM
Institute of Molecular and Cellular Biology, University of Leeds, Leeds, UK.
Bradford
James R
JR
Needham
Chris J
CJ
McConkey
Glenn A
GA
Bulpitt
Andrew J
AJ
Westhead
David R
DR
D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D018503:Genome, Protozoan; D023281:Genomics / Q000379:methods*; D010963:Plasmodium falciparum / Q000235:genetics*; D015800:Protozoan Proteins / Q000235:genetics
D015800:Protozoan Proteins