Bréhélin et al. BMC Genomics 2010, 11:35 http://www.biomedcentral.com/1471-2164/11/35 Page 4 of 14 Figure 1 The probabilistic model. For each species, only three prototypes are represented here, while several dozens are usually used. Prototypes are modeled with multivariate Gaussian models represented here by series of means and standard deviations. P. falciparum prototypes are labeled with their prior probabilities. Prior probabilities of the prototype of the reference species are on the outgoing transitions from the mute state M. Direct transitions corresponding to evolutionary conservation between prototypes are in bold. Assessing functional conservation Once this model is built, it can be used for assessing the functional conservation between two putative homologs. Given the gene expression profiles of the two genes, the procedure involves searching for the prototypes k and k’ that have the highest probability of generating the two profiles. This can be done with the classical Viterbi algo- rithm of the HMMs [27]. If these prototypes share a direct transition, then P(k’ |k) reflects the conservation of coexpression between k and k’. P(k)·P(k’ |k) is the prior probability of these prototypes under the hypoth- esis of dependence of the two profiles. In a similar way, P(k)·P(k’ ) is the prior probability of these prototypes for genes that are conditionally independent. The greater the difference between these probabilities, the more likely the functional conservation between genes. Thus, if the ratio of these probabilities, i.e. P(k’ |k)/P(k’ ) is higher than a given threshold ξ, the annotations of the reference gene are transferred to the query gene. Other- wise, the annotations are not transferred. Estimate of accuracy with the Gene Ontology We first investigated the global accuracy of our approach using genes already annotated in the Gene Ontology (GO, http://www.geneontology.org), which is a systematic and standardized nomenclature to annotate genes in terms of their associated biological processes (BP), cellular components (CC) and molecular functions (MF), in a species-independent manner. The validation procedure is as follows. First, an HMM is learned from a set of gene pairs selected by RBH. Next, given several (putative) homologous genes, the HMM is used to select from among these pairs those authorizing annotation transfers from the reference to the query species. Trans- fers involving gene pairs already annotated in both spe- cies are used to compute an estimate of the accuracy of the method. Namely, the proportion of annotations of the reference gene shared by the query gene is com- puted, and the global accuracy is then estimated by averaging all results (see Methods). Most available P. falciparum annotations have an IEA code only, indicating that they have not been reviewed by a curator. In the BP ontology for example, of the 1799 annotated genes (35%), 1067 (68%) have an IEA code. In these conditions, it is recommended to first check the method on curated annotations of better annotated organisms. To this end, we estimated the method accuracy using D. melanogaster as the query species and S. cerevisiae as the reference species. The procedure was applied to the Pilot (PI) and Spellman (SP) data sets. We first investigated the method accuracy on genes with high sequence similarity, by applying it to the RBH pairs used to learn the HMM.