Bréhélin et al. BMC Genomics 2010, 11:35 http://www.biomedcentral.com/1471-2164/11/35 genus or even the Apicomplexan phylum to which these organisms belong [5], it is certainly further exacerbated by the high evolutionary distance between P. falciparum and other sequenced organisms [6], which makes homology detection particularly difficult. The extreme AT bias (above 80%), the high amino-acid bias (six amino acids account for more than 50% of the protein composition), and the presence of a large number of low-complexity regions that are believed to form unstructured segments [7], can impede the efficacy of standard methods of sequence comparison based on BLAST [8,9] or HMMER [10]. Thus, P. falciparum is a typical organism for which new approaches are needed to help detection of distant homologs. Usually, a low (stringent) e-value threshold is used to determine whether a measured BLAST sequence simi- larity allows functional annotation transfer. However, even if they have high sequence similarity, homologous proteins are not necessarily functionally equivalent [11]. Moreover, in the case of highly divergent proteins like those of P. falciparum, this strategy can fail to annotate many proteins. In this latter case, increasing the e-value threshold might enable annotation of a larger propor- tion of proteins, but with the risk of more spurious annotations. Here, we propose to use the conservation of coexpression between species to decide whether to transfer, or not, the functional annotations from homo- logs with potentially weak sequence similarity. Gene expression data have been widely used in recent years to help gene annotation via the so-called Guilt by Association (GBA) principle. Contrary to sequence homology which involves inter-species annotation trans- fers—i.e. genes characterized in other species are used to annotate genes of the newly sequenced genome— GBA approaches involve intra-species annotation trans- fers: annotations of an already characterized gene in the organism are transferred to an uncharacterized gene if the two genes share similar transcriptomic profiles [12,13]. This approach has been successfully applied to P. falciparum [14-16] and provides functional clues for many uncharacterized genes. However, depending on the data sets and on the type of function, the accuracy of the GBA predictions can be low [16]. Several studies report the conservation of coexpression between species [17-19]. In particular, these analyses show that if two genes are coexpressed in one organism, then their orthologs also tend to be coexpressed in other organisms, i.e. coexpression is sometimes con- served. In this paper, we propose a method that uses this property to help for annotation transfer in an inter- species way. The principle is as follows: we have (1) a query species (P. falciparum) and a reference species (for example S. cerevisiae), (2) two microarray data sets that monitor the level of expression of the genes of the Page 2 of 14 two species, and (3) a set of likely orthologs between the two species. The coexpression context of a gene in one of the species is defined as the set of genes that appear to be coexpressed with this gene in the microarray data of the species. Then, if two homologs with potentially weak sequence similarity have similar coexpression con- text—i.e., if their coexpression contexts share a suffi- ciently high number of orthologs—, they likely have the same function. We show that considering conserved coexpression (or co-coexpression for short) between species improves the accuracy of functional annotation transfers by sequence similarity alone, and outperforms the accuracy of the classical GBA approaches. Moreover, it also allows reli- able annotation transfers when the homology is doubt- ful, a useful property for the highly divergent proteins of P. falciparum. The method was applied on two different reference species (S. cerevisiae and D. melanogaster), and with different gene expression data screening differ- ent biological conditions for each species. These studies enabled us to provide a function for 74 previously hypothetical genes and to confidently propose refined annotations of previously incomplete or wrong original functions for 58 additional genes. Notably, we revised the annotation of genes involved in ribosomal proteins and ribosome biogenesis and assembly, thus highlighting several potential drug targets. Results Gene expression data For the P. falciparum query species, we used two series of transcriptomic profiles produced during the parasite life cycle. The first series, referred to as the Bozdech data set (BO), measures the expression level of P. falci- parum genes once an hour during one complete 48- hours erythrocytic cycle [20]. The second data set, referred to as the Le Roch data set (LR), measures the expression level of P. falciparum genes in 9 different developmental stages during the entire life cycle both in its human host and in the insect vector, in two experi- mental conditions (a total of 16 expression measure- ments) [14]. For the reference species, we selected two S. cerevisiae and one D. melanogaster transcriptomic studies. The first S. cerevisiae data set, referred to as the Gasch data set (GA), is a compendium of 176 transcrip- tomic measurements in a variety of stress conditions [21] and the second one, the Spellman data set (SP) measures gene expression levels during a little more than two yeast cell cycles (77 expression measurements) [22]. Finally, the D. melanogaster data set (PI) corre- sponds to a transcriptomic study of 15 time points in the early development of the fly [23]. One advantage of our approach is that it can deal with transcriptomic data monitoring different conditions in the two organisms.