Assessing functional annotation transfers with inter-species conserved coexpression: application to Plasmodium falciparum.
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.
Bréhélin
Laurent
L
Méthodes et algorithmes pour la Bioinformatique, LIRMM, Univ, Montpellier 2, CNRS; 161 rue Ada, 34392 MONTPELLIER, France. brehelin@lirmm.fr
Florent
Isabelle
I
Gascuel
Olivier
O
Maréchal
Eric
E
D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D017124:Conserved Sequence; D015870:Gene Expression; D020869:Gene Expression Profiling; D018503:Genome, Protozoan; D008957:Models, Genetic; D010963:Plasmodium falciparum / Q000235:genetics*; D015800:Protozoan Proteins / Q000235:genetics; D012441:Saccharomyces cerevisiae / Q000235:genetics; D017422:Sequence Analysis, DNA / Q000379:methods
D015800:Protozoan Proteins