Numerous approaches for detecting horizontal gene transfers have been proposed in the last 2 decades. Phylogenetic trees of protein or DNA sequences, unusual distribution of genes, nucleotide composition (including codon biases) are some of the HT features that are considered within the framework of these models (16, 34) , Hidden Markov Models (HMMs) (12, 14, 35) and Factorial Correspondence Analysis (FCA) (37) are some criteria that are currently employed. Each of the resulting models has its own advantages and caveats (28) (29) (30) . As it has been recently pointed out by Ragan (49) and Lawrence and Ochman (50) , each approach deals with a particular subset of HTs, being for example more efficient for detecting recent transfers, or more effective for the detection of ancient HTs. Our approach, which is clearly based on oligonucleotide composition, assumes that different species have different signatures but does not rely on any other assumption. It is not surprising, therefore, that the genomic signature approach provides results (in terms of % of DNA transferred) in reasonable agreement with those proposed by Garcia-Vallve (16) and Nakamura et al. (14) for the 22 species that were analyzed in common. Correlations between percentages of HTs found by these three methods are highly significant Two species are extensively studied for HT content: B.subtilis (five methods including ours) and E.coli (six methods including ours). H.influenzae is also analyzed by Garcia-Vallve (16) and Nakamura (14) . Comparisons of methods are presented in Tables 2-4 and detailed in Supplementary Materials 3, 5 and 7. A voting procedure (majority rule) has been implemented to determine the status of genes with respect to atypicality. For that task, our initial analysis is converted in terms of genes (Supplementary Materials 3, 5 and 7). Degree of agreement between methods is subsequently observed using the statistical Kappa coefficient (51) . Kappa measures the degree of agreement on a scale from minus infinity to 1. A Kappa of one indicates full agreement, a Kappa of zero indicates that there is no more agreement than expected by chance and negative values are observed if agreement is weaker than expected by chance (a very rare situation). (14, 13, 11, 13 and 15%, respectively). The number of detected genes per method is close, ranging from 457 for Nakamura (14) to 599 for this work (median 537). Detailed votes are given in Table 2 . Among the 4100 genes of B.subtilis genome, 1011 genes are detected by at least one method (about 25% of B.subtilis genes). The number of 'single vote' genes ranges from 116 for Garcia-Vallve (16) to 47 for Nicolas (12) . A total of 470 genes make up the majority consensus set and we detected 453 of them, which is the best score of the five methods. The best agreement with the majority consensus (in terms of Kappas) is reached by Nicolas (12), followed by our method and Moszer (36) ( Table 2 ). Our method gets the best agreement with Nicolas (12) and the worst with the other HMM method used by Nakamura (14) (pairwise Kappa comparison, Table 2 and Supplementary Material 3). In fact, Nakamura approach is at variance with every other approach (14) . It gets the lowest Kappa with the Garcia-Vallve (16) Hayes (35) Lawrence (34) Nakamura (14) Medigue (49) This work majority consensus or with whatever other methods. From Table 2 , the probable number of HT genes in B.subtilis would range from 230 to 1011 with a 'reasonable' estimation around 470 corresponding to the majority consensus. It is to be noted that our method is unable to find two genes that are detected by every other methods (Supplementary Material 3) . These genes are 338 and 236 nt long, respectively, as compared with 2500 nt, the median size of atypical regions detected by our method (Table 1) . Clearly, our method is not appropriate for detecting short isolated atypical genes.