We extracted pairwise sets of 14,997; 14,798; 14,667 and 14,065 one-to-one (1:1) orthologous protein-coding genes (Ensembl v42) between human and dog (H-D), chimpanzee-dog (C-D), mouse-dog (M-D) and rat-dog (R-D), respectively. Using those 1:1 orthologs as comparative anchors, we built four fine-scale whole-genome pairwise synteny maps (Additional data file 1) with the program AutoGRAPH, which we recently developed 13. We identified 218, 229, 326 and 325 CSOs, i.e. chromosomal segments for which markers are in the same linear order on the chromosome as noted across species 25, between H-D, C-D, M-D and R-D respectively. The mean distance between two consecutive genes was ~180 kb. In all synteny maps, CSOs cover almost the entire genome while breakpoint regions, areas delimitating CSOs, cover only ~5% of a genome and may contain single-gene segment or very short synteny blocks 33 (Additional data file 2).

In each pairwise synteny map, we localized the missing gene orthologs on the reference sequence (Figure 1). Of the 412 missing genes, the vast majority (mean of 92.3%; range 92 to 94%) mapped within CSOs with only 7.7% mapping within breakpoints. In all reference species the missing genes spanned all chromosomes, although their distribution varied greatly, i.e. one to 44 per human (HSA) chromosome in the case of the human-dog synteny map.

We used multiple pairwise synteny maps described above to identify short, targeted, orthologous genomic intervals. On each reference genome, these intervals are delimited by the closest flanking 1:1 orthologs on either side of each missing gene that in turn define orthologous intervals on the canine genome as shown in Figure 1. The use of multiple pairwise maps enabled us to identify the shortest consensus interval on the canine genome to search for genes, that we refer to as Consensus Ortholog IntervaLs (COILs) (Figure 1). From the 412 missing genes, we delimited 383 COILs (92.9%) having a mean size of 347 kb (Additional data file 3). For a set of 17 COILs (4.1%) localized in common breakpoint regions (i.e. overlapping between at least two species) 2434 and for 12 missing genes, no COIL could be determined because of the absence of a consensus interval.

Within each canine COIL, we used the GeneWise program 6 to splice and align the protein sequence of each reference species in order to most accurately predict the structure of the dog gene. We retained gene predictions produced by at least two reference species protein templates. This produced 231 gene structure predictions with amino acid identity > 40% (Figure 2). Fifty-three genes were predicted using only rodent protein sequence as templates, thus illustrating the complementary contribution of multispecies analysis. We post-processed GeneWise results to detect potential gene features and found the presence of a coding start site for 53.1% of the gene predictions. In addition, amongst the 231 predicted genes, 75% of the predictions with multi-exonic structure exhibit at least a canonical splice site (GT/AG).

To address the question whether COIL delimitation is too restrictive for gene prediction, we aligned the human transcript sequences corresponding to the 383 missing genes for which we defined a COIL, against the assembly of the canine genome sequence (CanFam 2.0) with the Exonerate program 35. We repeated the analysis with chimpanzee, mouse and rat transcript sequences. We considered the best five matching sequences to relax the limitations of conventional best-match methods 29. Then, we defined a concordance between the COIL approach and the whole-genome sequence analysis, when matching sequences from the Exonerate-based analysis for at least two species were totally embedded in COILs. Based on this criterion, concordance was obtained for 342 (89.2%) genes. Of the 41 instances with no agreement between the expected syntenic location and the whole-genome sequence analysis, 36 showed weak match (identity < 20%) within the canine genome assembly suggesting unspecific alignment while five showed a significant match, from at least two species suggesting that these genes may have acquired a new location in the dog. Of the latter five instances, we identified only one gene prediction (PLA2G4C) with conservative criteria indicating a relocated gene in a non-syntenic genomic area.

In this study, we applied Genewise program with a sequence similarity-based method that explicitly models the conservation of gene structure and a high degree of conservation. As such model is known to show a marked decrease in performance for less similar genes 36, we further investigate the undetected subset of genes using a probabilistic pair hidden Markov model (HMM) that show a weaker dependence on percent identity and performs better to pick out distant homologs. The Genewise HMM based analysis allowed to predict 36 additional genes (Figure 2). Both prediction sets were merged into a single set (n = 268) for further analysis.

Sequence alignments were next generated between gene predictions and canine transcript sequences (Unigene april 08 37). We identified significant alignment (sequence similarity > 96% for at least 150 bp) in 53% of cases with an average of 7.5 ESTs/mRNA per gene prediction (range 1–99). Using Interproscan, 38 protein motifs were found from InterPro database for 80.5% of the gene predictions, providing additional evidence for dog gene identification.

As a further validation step, the construction of canine predicted protein three-dimensional models was investigated based on the homologous structure of the human ortholog or paralog (>40% identity), which was used as a template. For the subset of genes for which the 3D structure is solved (n = 21), canine-human comparative modelling was determined using the SWISS-MODEL server 39. In 16 instances of canine-human comparative modelling, the mean identity obtained between sequences was 70%. Homology-based 3D model for each canine prediction was validated using the Verify 3D graphs 40 (data not shown) that distinguish between homology models of higher and lower accuracy.

To test for possible overlap between gene predictions obtained in this study and all canine genes annotated in Ensembl (v42), we performed sequence alignment between these two sets of predictions. A total of 232 (88%) predicted genes did not overlap any Ensembl annotated protein-coding genes. Therefore, these were classified as "definite" gene identifications together with the delineation of new orthologous relationships with the four reference species (Additional data file 4). The remaining 36 gene predictions overlapped an annotated gene (protein identity > 80%) indicating that these gene predictions correspond to sequences already defined as genes, but with undetected or spurious orthologous relationships (Figure 2). 41.

To assess the validity of gene predictions through the strength and direction of selective constraints, we used a functionality test that uses the ratio of replacement to silent nucleotide substitution rates (d_N/d_S). The ratio d_N/d_S, where d_N is the number of non-synonymous nucleotide substitution per non-synonymous site and d_S the number of synonymous nucleotide substitution per synonymous site, is used as a proxy for the evolutionary constraints that occur on nucleotide substitution 42. The calculation of the d_N/d_S ratio requires the comparison to a homologous reference sequence. First, we constructed a benchmark set of true orthologous genes using all 1:1 orthologous genes between human and dog (n = 14,994) to obtain a representative d_N/d_S value. From this benchmark set, we calculated the median d_N/d_S ratio of 0.15 using all d_N/d_S values extracted from the pairwise alignments of transcripts (Figure 3). To assess the 232 gene predictions identified in this study with the functionality test, we determined d_N/d_S ratio for each of the gene predictions in comparison to their human functional orthologous gene from pairwise transcripts alignments. We calculated a median d_N/d_S of 0.19, a value highly similar to the benchmark set (0.15). To further assess the d_N/d_Scomparison, d_N/d_S values were analyzed through their distributions (as log d_N/d_S) between benchmark and predicted genes sets (Figure 4) and we did not detect statistically significant differences (Mann-Whitney test; P = 0.16). Therefore d_N/d_S similar distributions are indicative of similar high selective constraints and little or no positive selection on both benchmark and predicted genes sets, suggesting the functional properties of the canine gene predictions products involved are conserved.

To analyze the evolutionary rate of the new canine predicted gene sequences in a phylogenetic context we used the 232 mouse genes in addition to human genes and dog predicted genes to assess the levels of selective constraint of each lineage in comparison to the rest of the tree. In this way, differences or similarity in selective constraints can be predicted on all lineages within the phylogeny. For each of the 232 genes, we inferred the d_N and d_S values and calculated the d_N/d_S ratio. The median d_N/d_S for the dog lineage was found between human and mouse (Table 1), a result in agreement to these determined for 13,816 human, mouse and dog genes with 1:1:1 orthologs 2 with similar differences found across the three lineages.

Off the 412 missing genes, a subset of 55 predictions containing ORF-disrupting mutations lead to pseudogene identification. Among pseudogenes, we determined if protein sequences have different numbers of in-frame stop codons and/or frameshift disruptions. Using such quantitative measures, two mutation levels were apparent. A set of inactivated genes (n = 21) was predicted with accumulated mutations (mean = 4.2; range 2–11) and a second set (n = 34) was predicted with one mutation (Figure 3). To normalize the mutation rate by taking into account the coding sequence length, we expect proteins of similar lengths to now have similar numbers of stop-codons or a frameshift. We therefore examined the ratio of accumulation of ORF-disrupting mutations per 100 AA in both groups of pseudogenes. A mutation rate of 0.28 was determined for the group of pseudogenes with one mutation and a significant higher rate of 1.21 (Mann-Whitney test; P = 8.052e - 7) was found for the set of pseudogenes with accumulated mutations.

Although transcribed pseudogenes have been experimentally identified 43, a significant part of pseudogenes are thought to be transcriptionally silent in comparison to protein-coding genes. We thus searched for sequence alignment with canine transcript sequences (Unigene april 08 37) to assess the transcription activity of the pseudogene predictions with two and more mutations. We obtained alignment for 14%, a result in agreement with a recent report 44 showing that 19% of pseudogenes are the sources of novel RNA transcripts. These data indicate that the predicted pseudogenes are mostly undetected as expressed sequences in comparison to gene predictions with intact ORF (53%) and therefore significantly correspond to untranscribed pseudogenes 44.

To assess independently of the presence of stop codons or frame-shifts, the validity of pseudogene predictions, we used the functionality test that uses the d_N/d_S ratio. Assuming a constant mutation rate, the d_N/d_S ratio between dog pseudogenes, for which a loss of function occurred, and their human functional orthologs should theoretically relax towards 0.57 (as the average of 1.0 in the absence of selection and 0.15 for negative selection as we calculated from the benchmark set) 10. Thus, we calculated d_N/d_S ratio for each of the candidate pseudogene predictions in comparison to their human functional orthologous gene from pairwise transcripts pair alignments. For the pseudogene set with accumulated mutations, we calculated a median d_N/d_S of 0.50 indicating a considerable relaxation of selective constraints of the canine pseudogenes in comparison to their human functional orthologous (Figure 3 and Table 1). Furthermore, the d_N/d_S distributions obtained were shifted upwards in comparison to the benchmark set (Figure 4), which is significant to a Mann-Whitney test (P = 5.17e - 6), indicating relaxation of evolutionary constraints on the predicted pseudogenes. For the pseudogene set with one mutation, the median d_N/d_S of 0.18 was observed, suggesting no detectable differences in selective constraints between predicted pseudogenes from the canine sequence and their human functional counterparts. In addition, we analyzed whether the d_N/d_S ratio has an independent value before and after the stop codon among the predicted pseudogenes. In 26/28 instances, no significant differences were detected when comparing d_N/d_S ratio for the two parts of each gene. In two cases, the d_N/d_S value before the stop was indicative of strong selective constraints (<0.1), in comparison to the value detected after the stop (>0.9), which suggest that the biological function may have been preserved.

We next searched to determine if the canine predicted pseudogenes showed any deviations from the expected rate of evolution using a phylogenetic context that includes human and mouse gene sequences. Such variation in rate may reflect relaxation of constraints in the dog lineage. The deviation between dog predicted pseudogenes with multiple mutations and the human and mouse lineages differs clearly (d_N/d_S = 0.41 for dog, 0.19 for mouse and 0.26 for human; Kruskal-Wallis test: P = 1.04e - 2) while no significant deviation (P = 0.36) was observed for the set of pseudogenes with one mutation (Table 2). We therefore retained the 21 pseudogene predictions with both the higher d_N/d_S value as characterized by pairwise and phylogenetic approaches and high mutation rate as gene loss candidates.

In addition to pseudogene identification, 11 gene predictions could not be detected with sufficient protein identity (average = 21.7%), both in the targeted genomic region (COIL) and in the whole canine sequence. For these predictions with no readily identifiable counterparts in dog, we searched for sequence alignment with canine expressed sequences (Unigene april 08) to address the underlying assumption that genes are not transcribed when placed in the context of highly degraded sequence. We identified sequence alignment in only three cases. These results showed that the gene predictions with poor sequence similarity were largely undetected as expressed sequences in comparison to gene predictions with intact ORF.

For the last subset of 49 canine genes that remained undetected in this study, we address the possibility that gene predictions could have been prevented because of a gap in the canine sequence assembly. We searched for gap content in the COILs that lack canine orthologous genes. For 12 COILs, the gap content was found to account for >10% of the total size of the COIL, seven-fold more than a random expectation set (n = 1000, gap = 1.32%) and manual inspection of sequence content resulted in identifying multiple sequence gaps. The 12 missing genes in those short targeted regions were therefore not retained in further analysis. Based on these results, a total of 37 undetected genes was considered and merged with the 11 gene predictions that could not be detected with sufficient protein identity and the 21 pseudogenes into a single set (n = 69) of gene loss candidates for further analyses (Figure 2 and Additional data file 5).

Do we detect losses of genes that occur specifically in the dog or do such losses occur in other mammalian lineages as well? If so, do such losses correspond to the time the dog branch diverged from the Euarchontoglires (rodent/primate) lineage? One way to analyze these possibilities is to determine their phyletic pattern using ten species from chicken to human and to define the amount of time between gene origin and present. The timing of genes origin was defined by searching for 1:1 orthologs between human and nine species. In addition to human, chimp, mouse and rat genome sequence assemblies, we used scaffold assemblies of elephant, tenrec and armadillo from the Afrotheria and Xenarthra superorder and two non-placental genome assemblies of opossum and platypus. We also included the chicken sequence to infer gene origins that occurred as long as 310 million years ago (MYA) (Figure 5).

Orthologous genes were detected between human and all species (except dog) for 11 genes. Therefore, they have an origin that occurred before the separation of the mammals and birds lineages and have been functional for 310 million years (My). In addition, 17 genes were identified in all species of the opossum/platypus, elephant-tenrec-armadillo and Euarchontoglires branches, a period of 170 My, 17 in all species of the elephant-tenrec-armadillo and Euarchontoglires branches (100 My), and 10 in Euarchontoglires only (87 My) (Figure 5) 45.

Overall, 28 canine gene losses could be characterized as being functional in other species for more than 170 My and 10 genes were not detected before 87 My and therefore specifically arose in rodent and primate lineages. For these genes, postulating that they arose through duplication events of a parental gene, we searched for paralogs among all human genes. For seven genes (ZNF426, WFDC12, ZIK1, HLA-SX-alpha, PNMA5, PNMA3, ZNF251) we identified at least one paralog (sequence identity >30%) in the close vicinity of the parental gene (mean of 71 kb; range: 16–128 kb).

We further used the Ensembl reconciliation tree method 46 for checking possible duplication events specific of the primates and rodents lineages. Indeed, assuming that all homologous genes are known, the reconciliation of the gene tree with the species tree allows to distinguish duplication from speciation events and therefore orthologous from paralogous genes. Five genes (ZNF426, ZIK1, HLA-SX-alpha, PNMA5, PNMA3) have in-paralogs in the reference species suggesting a pattern of duplication event (Additional data file 6).

These results suggest that tandem duplication events have occurred and lead to specific in-paralogs in the branch leading to human species. Another contribution of this analysis is that it permits identification of 10 losses that occur in several lineages indicating multiple and independent gene loss events 47.

For the 28 canine-specific gene losses that have been functional for more than 170 My, we determined the functional annotation of the human genes using WebGestalt, a Web-based gene set analysis toolkit 48. The classification using the GOTree sub-module includes seven genes that belong to the biological process of response to stimulus with PROZ, a vitamin K-dependent protein Z precursor involved in blood coagulation pathway and SERPINA10 a protein Z-dependant protease inhibitor that regulates factor Xa involved in blood coagulation. Moreover, it includes five genes involved in response to stimulus pathways that play a role in sensory function such as UGT2A which encodes an enzyme with transferase activity that may catalyze inactivation and facilitate elimination of odorants, OR1Q1, OR1B1, ORN1 which arethree olfactory receptors, and Noggin, a secreted polypeptide encoded by the NOG gene that appears to have pleiotropic effect, both early in development as well as in sensory perception of sound. Other genes of interest belong to families with at least six members such as TBX22 a transcription factor involved in the regulation of various aspects of embryonic development, in particular cell type specification and regulation of morphogenetic movements 49, and MS4A3 which is a subset of the superfamily of tetraspan transmembrane protein encoding genes. Several genes were classified with function involved in DNA repair, apoptosis and tumor formation such as BOK which encodes a Bcl-2 related protein and PDE1B which may play a role in apoptosis. To address the question of which tissue might be significantly affected by gene loss, we determined a gene-expression profile characterization per tissue based on the occurrence frequency of the ESTs profiles of human genes corresponding to the gene lost set using the tissue expression profile sub-module of WebGestalt. Testis-expressed gene expression profiles showed a significant over or under representation and, to a lesser extent, expression profiles related to placenta and kidney tissues did as well (Additional data file 7).

A basic application of gene order-based approaches is the capacity to detect short conserved genomic context based on robust orthologous gene pair annotation. Therefore, results are limited by the source of gene annotation. In this study, we used the Ensembl annotation because of its good gene prediction coverage of the four species used as reference genomes. Since annotation of mammalian genome is a continuous process, our gene order-based approach may be improved over the course of time.

The use of short orthologous genomic intervals filtering has been well documented 28. First, it reduces the cost of detecting false-positives as it filters out paralogs, with the exception of those caused by tandem gene duplication, and alignments to processed pseudogenes. Second, it allows a balance between sequence alignment sensitivity versus accuracy 50. Alternatively, for more divergent sequences, alignment criteria may be relaxed in short pre-defined space where the background noise is significantly reduced compared to a genome scale search.

In our analysis, predictions may not provide an exhaustive list of gene predictions as inaccuracies may be generated by sequence artifacts that typically exist in draft sequence assemblies. Another issue related to prediction accuracy is the unexpected and unknown level of highly divergence at the nucleotide level. While scenarios of functional sequences with different evolutionary rate in different species exist 51, we postulated that using protein coding genes with a comparable evolutionary rate amongst four reference species reduces the possibility that a gene evolves independently in the dog species.

A corollary to targeted gene prediction is that the absence of prediction strongly predicts gene relocation to a different region or chromosome or a gene loss event. Gene losses arise through retrotransposition or segmental or tandem duplications events followed by inactivation of one copy, or by degenerative mutations. We used a computational analysis to identify genes lost as pseudogenes based on various detrimental sequence mutations such as in-frame stop codons and frameshifts causing or resulting from loss of function. In this study, pseudogenes were separated in two groups, with the group of pseudogenes with one mutation (showing a low mutation rate) and the second group with an elevated mutation rate (>4 mutations, on average). Pseudogene predictions with one mutation could be overstated due to sequence artifacts that exist in the assembly. Indeed, stop codons and frameshifts are accommodated by algorithm like GeneWise. Other programs specifically designed for aligning pseudogenes such as GeneMapper 52 may be useful for addressing this problem. Another hypothesis is that pseudogene predictions have existed as pseudogenes (i.e. inactivated) for different amounts of time in the carnivore lineage. The formation of pseudogenes present in the canine genome could have been initiated by different or multiple events rather than have resulted from a continuous process over the course of time. Pseudogene characterization through the ratio of silent to replacement nucleotide substitution rates (dN/dS) may be a good indicator of changes in selective constraint that tend to be recent 53. It is clear from our analysis that the dN/dS approach is useful to assess the evolutionary constraints that occur on nucleotide substitution. However, inferences of selection need to be treated with extreme caution.

We identified 28 gene losses that have been functional for more than 170 million years, a time period that extends from platypus to human (Figure 5). Losses of gene in a given species can be considered an adaptive event that may confer selective advantages to an organism 54. Similarly to neutral losses, adaptive losses occurring ~95 MYA (for lineage leading to canid) are expected to leave genomic signatures with ORF-disrupting sequence mutations accumulation due to sequence degeneration. Here, the losses identified are based on ORF-disrupting sequence mutations, absence of EST validation and absence of significant similarity at the protein level. Although highly speculative, one hypothesis is that species-specific gene loss may confer a selective advantage in dog. Among the gene losses we identified were PROZ, a vitamin K-dependent protein Z precursor gene involved in response to stimulus that plays a role in blood coagulation. Mammalian blood coagulation is initiated and regulated by a complex network of interactions involved in normal hemostasis. Interestingly, Lindberg et al. describes a decrease of the expression of heme and globin related genes that correlate with the tameness trait in silver foxes suggesting that differences in behavior have a genetic basis 55. A second hypothesis, is that gene loss may be a direct reflection of the loss of redundancy, where functionally overlapping genes cover for the loss of function as for genes involved in sensory functions 5657.

Biomart 58 version 0.5 (Ensembl v.42) was used to collect orthologous protein-coding genes from the five genomes of interest: human (NCBI 36), chimp (Chimp 2.1), mouse (NCBI m36), rat (RGSC 3.4) and dog (CanFam 2.0). Ensembl Gene Id, orthologous relationships, locations in base pair for each species were downloaded and deposited into a MySQL database (v.4.1.12). The set of 412 protein-coding genes not annotated on the dog genome assembly with a 1:1:1:1:0 Human:Chimp:Mouse:Rat:Dog match was then extracted from the MySQL database.

We used the program AutoGRAPH 13 to construct pairwise synteny maps between reference genomes and tested genome. AutoGRAPH has been designed to construct synteny maps using genomic coordinates of ortholog pairs. The program transposes genomic coordinates into sequence of ordinal numbers and positions genes on an ordinal scale in relation to others on their respective chromosomes. Conserved segments ordered (CSO) can then be identified with respect to the ranking order. We only considered CSO containing a minimum of three genes. AutoGRAPH inferred the collinearity rate within CSO corresponding to the longest increasing gene order sequence between the two species divided by the total number of orthologs. We discarded CSO that had a collinearity rate less than 0.5. All synteny maps (n = 88) built in this work are presented in Additional data file 1 and can be downloaded.

The GeneWise program 6 (wise2-2-0) was used with default parameters to align each reference protein on the dog COIL forward and reverse strands (option -both) sequence. Predictions were post processed to pick up the highest genewise prediction, to compute sequence identity/similarity against reference proteins and to analyze splice sites conservation. Only predictions exhibiting at least 40% identity with reference proteins were retained. GeneWise was also used with the Hidden Markov model that uses HMM profiles generated with the HMMER package 59. HMM-based prediction considers exons, introns and UTR regions as different states of gene structure that occupy subsequences of a sequence. A gene structure can be considered as an ordered set of state/sub-sequence pairs. A HMM-based prediction is considered as a predicted gene structure if probability of generating a gene structure is maximal over all possible states. Dynamic programming method for finding an optimal parse, or the best sequence of states has 10 been computed with the HMMER package.

Reference transcript sequences were collated from Ensembl (v.42) and aligned against the canine sequence assembly (CanFam2) with the program Exonerate v1.2 35. Exonerate includes various models for aligning splice sites, combining speed and accuracy. We used the est2genome model, with a minimum perfect match of 18 bases to trigger alignments (dnawordlen 18). For each reference transcript, we retained the best five matching sequences.

Canine proteins inferred from the gene predictions were aligned against all canine transcripts with Exonerate using the coding2coding model. Canine predicted proteins were aligned on canine dbEST (est.fa 05/19/07 from UCSC) and UNIGENE (April 2008) using Exonerate with the protein2genome model.

The protein three-dimensional structure was available for 21 human genes. The sequences were retrieved via the Protein Data Bank. The amino sequences for the corresponding canine predictions were obtained from the genewise program prediction. Canine-human comparative modelling was determined using the SWISS-MODEL server 39. Amino acid sequences are aligned between the primary structure of the human and the canine sequence. The three-dimensional model is constructed through the process implemented in the SWISS-MODEL server.

DN/dS analyses were conducted using the maximum-likelihood-based CODEML program (model = 0; PAML package) 60. Sequence alignments of the whole coding region of the human orthologous sequence with canine prediction were realized with clustalW program. Ds values were calculated from pairwise alignments using all transcripts. To filter for possible inconsistencies among orthologous trancripts, we selected the transcript with the smallest phylogenetic distance using the smallest dS. For each dataset, we calculated a threshold on dS which two fold the median dS; all dS larger than this threshold were not used for the dN/dS calculation. DN/dS values of the benchmark set were extracted from Ensembl. DN/dS ratio in the phylogenetics context were calculated using CODEML program using the branch model set as model = 1 and run mode = 0. Sequence alignments of the whole coding region of the human, mouse and canine prediction orthologous sequence were realized with clustalW program

The Gene Ontology Tree Machine (GOTM) and WebGestalt programs 3148 were used to retrieve GO term associated with ensembl gene ID. A hypergeometric test computes the statistical significance of overrepresentations of GO term compared to a reference complete list of genes. Only GO terms that were significantly over-represented (P < 1.0e - 3) were considered.

For each of the 69 candidate gene losses, one-to-one orthologous gene was searched between human and nine species using the complete collection of orthologous protein-coding genes (Ensembl). Genome sequence assemblies were used for human, chimp, mouse, rat, monodelphis, platypus and chicken and scaffold assemblies for elephant, tenrec and armadillo. Timing of gene origin was inferred by determining the longest serie of one-to-one orthologs between the human and each of the nine species.

We used the R package (R Development Core Team 2006. R: A language and environment for statistical computing. http://www.R-project.org) to test the statistical significance in comparing distinct distributions at each step of the method (Mann-Whitney, Kruskal-Wallis and Student's test).