Bréhélin et al. BMC Genomics 2010, 11:35 http://www.biomedcentral.com/1471-2164/11/35 24 and 30 have expression measurements in the Boz- dech data, respectively, while 9 and 4 genes are actually retrieved by co-coexpression analysis. These genes code for three putative DNA replication licensing factors (PF07_0023, PF13_0291 and PFL0580w), two putative minichromosome maintenance proteins (PFE1345c and PFL0560c), the proliferating cell nuclear antigen (PF13_0328), the putative karyopherin alpha (PF08_0087) and beta (PFE1195w) homologs, the Myb2 protein (PF10_0327), the NIMA-related protein kinase (PFL1370w), the ROI kinase like protein (PFD0975w), casein kinase I (PF11_0377) and casein kinase II regula- tory subunit (PF13_0232). Interestingly, most of these genes were found in the Bozdech versus Spellman com- parison (see cluster pairs #24, #27 and #28 in Additional File 4). A few other P. falciparum genes are paired with S. cerevisiae genes with annotations related to cell cycle control (see Add. File 9). For example, analysis of cluster pair #24 of the BO-SP output allowed us to refine the annotations of a ROI kinase like protein (PFD0975w); analysis of cluster pair #28 allowed refinement of the annotation of a putative serine/threonine kinase similar to RAD53 (PF11_0488); in cluster pair #20, several hypothetical P. falciparum genes could be pinpointed as putatively involved in a cell cycle related process. In cluster pair #11 of the LR-GA output, which contains the casein kinase I gene (PF11_0377) [43], a functional annotation of a chromosome condensation protein involved in sister chromatid exchange is proposed for the hypothetical gene MAL13P1.21, paired with a signifi- cant e-value = 4e-07 to YBL097W. By contrast with the genes involved in ribosomal bio- genesis and assembly, very few P. falciparum genes involved in cell cycle regulation can be eventually identi- fied. This is possibly due to the lack of some of the mar- ker genes in the data sets compared, to the highly atypical way in which the parasite divides, i.e. schizog- ony [44], and to the evolutionary distance between P. falciparum and the model organisms in which the cell cycle has been studied in depth, i.e. animals and fungi [45]. High and low representation of certain functions in co- coexpression analysis The high representation of biological functions such as ribosome biogenesis and assembly, as well as the appar- ent lack of other biological functions such as cell cycle regulation might be a bias of the present method for dif- ferent biological reasons. First, we expected to find the conservation of housekeeping genes throughout eukar- yote biodiversity, including genes involved in protein biosynthetic processes. Secondly, the biological specifici- ties of the published studies we selected as data sets for our analyses might also explain this fact. Intuitively, out- puts of co-coexpression analysis and therefore Page 9 of 14 usefulness in terms of gain to transfer annotations are likely highly dependent on the type of transcriptomic data sets compared. In particular, co-coexpression based annotations are expected to depend both on (1) the organisms that are compared (i.e. their phylogenetic dis- tance), and (2) on the biological conditions of the assays used to produce the transcriptomic profiles. Concerning the choice of organisms, we obtained bet- ter results in terms of the capacity to transfer annota- tions by comparing P. falciparum to S. cerevisiae than by comparing P. falciparum to D. melanogaster. The reasons for the better performance of the P. falciparum vs. S. cerevisiae comparisons may be due to the better annotation status of the S. cerevisiae genome compared to that of D. melanogaster and/or the relative genetic distance between the species we compared. Undoubt- edly, improvements in D. melanogaster genome annota- tions will improve our ability to transfer annotations from the fly to P. falciparum, which were sometimes limiting in the present study. Concerning the biological conditions, the transcrip- tomic data we selected in published studies (for review, [46]) for our analyses focused on proliferating and dif- ferentiating stages of Plasmodium, mobilizing the tran- scriptional/translational machinery and possibly adding weight to this particular cellular process in the final out- put. This is further supported by the presence in the results of several clusters involved in subcellular target- ing of proteins soon after their biosynthesis. Moreover, querying P. falciparum genes involved in cell cycle regu- lation using the Spellman data set [22] that directly deals with this question, gave more results than using the Gasch data set [21], a compendium of 176 stress conditions, which gave no results at all when compared to the Bozdech data set, and very few when compared to the Le Roch data set. Thus, while it is possible that the potential of the co-coexpression analysis as a strat- egy to assist in the functional inference of hypothetical genes may be exhausted after covering a limited number of conserved eukaryotic housekeeping processes, it may also benefit from two improvements: 1) using species that are phylogenetically less distant, ideally, other Api- complexans, and 2) comparing profiling studies of biolo- gical situations specifically chosen to answer specific questions. For example, comparing Arabidopsis thaliana to P. falciparum transcriptomic data could help uncover genes inherited from the ancestral algae [9]. Comparison with the GBA approach During the writing of this article, Zhou et al. (2008) [15] published a database of GO functional predictions for several P. falciparum genes based on a Guilt By Associa- tion method named OPI. Their predictions are based on a transcriptomic data set covering all life cycle stages of the parasite and combining gene expression