BACKGROUND: Plasmodium falciparum is the main causative agent of malaria. Of the 
5 484 predicted genes of P. falciparum, about 57% do not have sufficient 
sequence similarity to characterized genes in other species to warrant 
functional assignments. Non-homology methods are thus needed to obtain 
functional clues for these uncharacterized genes. Gene expression data have been 
widely used in the recent years to help functional annotation in an 
intra-species way via the so-called Guilt By Association (GBA) principle.
RESULTS: We propose a new method that uses gene expression data to assess 
inter-species annotation transfers. Our approach starts from a set of likely 
orthologs between a reference species (here S. cerevisiae and D. melanogaster) 
and a query species (P. falciparum). It aims at identifying clusters of 
coexpressed genes in the query species whose coexpression has been conserved in 
the reference species. These conserved clusters of coexpressed genes are then 
used to assess annotation transfers between genes with low sequence similarity, 
enabling reliable transfers of annotations from the reference to the query 
species. The approach was used with transcriptomic data sets of P. falciparum, 
S. cerevisiae and D. melanogaster, and enabled us to propose with high 
confidence new/refined annotations for several dozens hypothetical/putative P. 
falciparum genes. Notably, we revised the annotation of genes involved in 
ribosomal proteins and ribosome biogenesis and assembly, thus highlighting 
several potential drug targets.
CONCLUSIONS: Our approach uses both sequence similarity and gene expression data 
to help inter-species gene annotation transfers. Experiments show that this 
strategy improves the accuracy achieved when using solely sequence similarity 
and outperforms the accuracy of the GBA approach. In addition, our experiments 
with P. falciparum show that it can infer a function for numerous hypothetical 
genes.