The global landscape of sequence diversity
© Peregrín-Álvarez and Parkinson; licensee BioMed Central Ltd. 2007
Received: 25 May 2007
Accepted: 8 November 2007
Published: 08 November 2007
Systematic comparisons between genomic sequence datasets have revealed a wide spectrum of sequence specificity from sequences that are highly conserved to those that are specific to individual species. Due to the limited number of fully sequenced eukaryotic genomes, analyses of this spectrum have largely focused on prokaryotes. Combining existing genomic datasets with the partial genomes of 193 eukaryotes derived from collections of expressed sequence tags, we performed a quantitative analysis of the sequence specificity spectrum to provide a global view of the origins and extent of sequence diversity across the three domains of life.
Comparisons with prokaryotic datasets reveal a greater genetic diversity within eukaryotes that may be related to differences in modes of genetic inheritance. Mapping this diversity within a phylogenetic framework revealed that the majority of sequences are either highly conserved or specific to the species or taxon from which they derive. Between these two extremes, several evolutionary landmarks consisting of large numbers of sequences conserved within specific taxonomic groups were identified. For example, 8% of sequences derived from metazoan species are specific and conserved within the metazoan lineage. Many of these sequences likely mediate metazoan specific functions, such as cell-cell communication and differentiation.
Through the use of partial genome datasets, this study provides a unique perspective of sequence conservation across the three domains of life. The provision of taxon restricted sequences should prove valuable for future computational and biochemical analyses aimed at understanding evolutionary and functional relationships.
Sequence space - the sum of all distinct protein and DNA sequences - is vast. A single copy of every possible 300 residue protein, for example, would fill several universes . In consequence, the evolution of genes, which mainly occurs through duplication, divergence and recombination , has led to only a small sampling of the available space. Systematic comparisons of proteins and coding sequences from existing genome scale datasets from a wide variety of organisms  are beginning to yield insights into the generation and extent of sequence diversity across life [4–9]. In addition to the continued discovery of apparently novel genes and gene families with each new sampled organism, these studies are beginning to reveal a wide spectrum of sequence specificity. At one extreme, sequences may be highly conserved across many different species from several evolutionarily distant lineages. The identification of these conserved sequences, perhaps constrained through extensive interactions with several different protein partners (for example, histones ), can provide clues about the genome content of the last universal common ancestor . At the other end of the spectrum of sequence specificity, sequences may be unique to a single species [12–14]. These so-called ORFan sequences are thought to represent sequences that are either remote homologs of known gene families, difficult to detect through current tools, or sequences that may have arisen de novo from non-coding sequences. However, it should be noted that many ORFans may simply arise as a consequence of incomplete sampling of sequence space. Further exploration of this space through additional sequencing is, therefore, expected to reduce their incidence .
While the exploration of this spectrum of sequence specificity is being usefully exploited to derive novel evolutionary and functional relationships, much of the focus has centered on sequences of prokaryotic origin. This is primarily due to the greater number of bacterial genomes that have been sequenced to date. However, the high incidence of lateral gene transfer (LGT) events in prokaryotes has resulted in the lack of a robustly defined phylogeny and, hence, studies of sequence diversity have largely focused on the identification and characterization of sequences at the two extremes of the spectrum [14–18]. On the other hand, while the taxonomic relationships in eukaryotes are more clearly defined, detailed systematic analyses of diversity within eukaryotes on the basis of fully sequenced genomes are precluded by the limited number and phylogenetic range of organisms that have been sequenced .
Aside from fully sequenced genomes, a large amount of sequence data has been, and continues to be, generated within the context of survey sequencing projects. Metagenomics projects, such as those exploring sequence diversity in the human gut or niches within the ocean, are continuing to expand the known repertoire of protein families [4, 9, 20]. However, due to the methods employed, these projects tend to focus on prokaryotes. Furthermore, the use of shotgun sequencing applied to heterogeneous samples leads to difficulties in assessing the taxonomic relationships within these datasets. More pertinently, over the past decade a plethora of sequencing projects has been initiated with the express aim of generating sequence data in the form of expressed sequence tags (ESTs) from eukaryotic taxa that have previously been neglected by genome sequencing initiatives (for example, [21–24]). As we have previously demonstrated, it is possible to use these datasets to identify non-redundant sets of genes associated with each species [25, 26]. Due to the incomplete nature of these collections of genes, we term such collections 'partial genomes'. These datasets provide a tremendous source of eukaryotic sequence information from a diverse range of species with well defined taxonomic relationships and have recently been exploited to explore genetic diversity within, for example, Nematoda  and the Coleoptera . In a previous study we collated and processed 1.2 million ESTs from 193 species of eukaryotes to create 546,451 putative gene sequences . Here we use these data to supplement 741,098 protein sequences from 198 fully sequenced genome datasets to perform a systematic analysis of sequence diversity across the three domains of life. Uniquely, we place our findings in the context of previously defined taxonomic relationships to identify and characterize landmarks of sequence evolution within the tree of life. These evolutionary datasets are provided through a publicly accessible online resource .
Sampling sequence space within the three domains of life
Taxonomic distribution of genomic datasets used in this study
No. of species
No. of sequences
Fully sequenced genomes
Sequence and gene family discovery rates for various complete and partial genome datasets
Sequence rate (%)†
Family rate (%)†
No. of complete/partial genomes
11.8 (± 1.5)
15.4 (± 1.8)
CG Bacteria strains filtered
15.9 (± 1.5)
20.6 (± 1.7)
13.4 (± 1.7)
17.0 (± 2.0)
CG Bacteria species filtered
20.9 (± 1.6)
26.1 (± 1.6)
16.3 (± 1.8)
19.9 (± 2.1)
40.3 (± 2.9)
42.8 (± 2.8)
CG Bacteria sequences > 100 residues
8.6 (± 1.4)
PG Sequences > 300 bp
35.6 (± 2.8)
Since the rate of sequence discovery decreases as a function of accumulated genomes, we were interested in determining the 'current sequence discovery rate' (CSDR), here defined as the percentage of distinct sequences associated with the last genome added to the existing dataset. From Figure 1d we obtain CSDR values of 11.8% for the 161 bacterial genomes (consistent with previous estimates ) and 40.3% for the 193 eukaryotic partial genomes (Table 2). Together with the large difference in OSDR, these values suggest that the eukaryotic partial genome datasets are more genetically diverse than the bacterial datasets. Previously, it has been suggested that many apparently novel sequences may rather represent artifacts of short, potentially mis-annotated sequences. Therefore, while subsequent studies have shown that many short sequences do indeed encode functional proteins [14, 17], it is possible that short sequences may be responsible for the observed increase in diversity associated with the partial genome datasets. We therefore repeated these analyses using only sequences greater than 100 residues in the bacterial datasets and 300 bp in the partial genome datasets (Figure 1a, d). Although we noted decreases in the rate of sequence discovery, excluding the shorter sequences resulted in similar trends to those observed in the full sequence datasets (CSDR = 8.6% for bacterial genomes and 35.6% for partial genomes; Table 2).
Impact of sampling bias and genome duplication on genetic diversity
Rather than being randomly sampled, selection of organisms for genome sequencing projects have primarily been motivated by medical or economic concerns. This bias has resulted in the generation of sequences from many closely related strains of bacteria (for example, five strains of Staphylococcus aureus are represented in our dataset) that could affect sequence discovery rates (Additional data file 5). Recalculations of sequence discovery rates using only a single representative (largest) for each bacterial species (127 genomes total) or only a single representative (largest) for each bacterial genus (86 genomes total) increased CSDR by 2.5% and 4.6%, respectively (Figure 1e and Table 2). However, despite these increases, rates of sequence discovery are still considerably lower than those obtained for the partial genome datasets in which no genomes were removed.
In addition to sampling biases in bacteria, whole genome duplication events observed for many eukaryotic lineages could result in the retention of many replicates of similar genes and, thus, contribute to the higher sequence discovery rates observed in eukaryotes. We therefore repeated our analyses using gene families (Table 2 and Additional data files 2 and 3). For both bacterial and partial genome datasets, the 'current gene family discovery rate' (CGDR - similar to CSDR but applied to gene families) was slightly higher (15.4% and 42.8%, respectively) than the respective CSDRs (Figure 1e and Table 2). However, the large difference observed between the two datasets indicates that genome-specific duplication events do not have a major influence on sequence discovery rates. Furthermore, analyses of gene family discovery rates within different eukaryotic taxa revealed similar trends to those observed for sequence discovery rates (Additional data file 5).
Together these results suggest that the observed differences in sequence discovery rates between the various taxa are not simply due to sequencing biases or lineage specific duplications, but rather reflect genuine differences in sequence diversity.
Sequence comparisons between the three domains of life
Within the set of sequences common to all three domains, we may expect to find a core set of 'promiscuous' sequences common to all 198 complete genomes. Previous estimates suggest that there may be as few as 34-80 such genes per genome [15, 16, 30]. Our analyses identified 13,055 sequences (representing 2% of all sequences from the complete genomes) possessing significant sequence similarity to a sequence from each of the 198 complete genomes. Compared with other less well conserved sequences and consistent with previous findings, these promiscuous sequences are associated with a limited number of basic biological processes, including transcription, translation and metabolism (Figure 2b).
Although we might expect to find similar numbers of promiscuous sequences in each genome, there was considerable variation: from 15 in the nanoarchaeotan Nanoarchaeum equitans to 208 in the alphaproteobacterium Sinorhizobium meliloti (mean = 64, standard deviation = 33.7). This variation could indicate species-specific expansions associated with one or more of these core genes. Using the COGENT database , the 13,055 sequences could be classified into 74 distinct gene families (Additional data file 2). The numbers of gene families per genome (mean = 19.5, standard deviation = 2.6) varied from 13 for Cryptosporidium parvum (derived from 16 sequences) to 28 for Saccharomyces cerevisiae and Homo sapiens (59 and 150 sequences, respectively).
The families that contain promiscuous sequences from a majority of genomes from each of the three domains of life include tRNA synthetases (TR-000178, TR000339, TR-000213 and TR-00352), ABC transporters (TR-00006, and TR-000000), elongation factors (TR-000038), translation initiation factors (TR-000155) and GTP binding proteins (TR-000443). These groups may be indicative of a high level of sequence integrity associated with coupling nucleotide binding activity required for their respective functionalities.
Of the families containing promiscuous sequences restricted to one or two domains, 17 are common to at least 50% of the eukaryotic species, 11 are common to at least 50% of Archaeal species, and 9 are common to at least 50% of the bacterial species. These families represent taxa specific subgroups. For example, there are two distinct families of aspartyl, glutaminyl and leucyl synthetases. One set (TR-000216, TR-000742 and TR-002174) is represented in Archaea and Eukarya, while the other (TR-000296, TR-000139 and TR-000266) is represented in Bacteria and Eukarya.
The families containing promiscuous sequences from a limited number of genomes but many non-promiscuous sequences from many other sequences (for example, TR-000223 and TR-000013) may indicate potential gene fusion events or incorrect gene models in which the promiscuous sequences are associated with additional sequence not found in the other members of the family.
Most of the families containing examples of promiscuous (and non-promiscuous) sequences from only a limited number of genomes are representative of sequences that are related to others in the promiscuous sequence dataset (note, for example, the many instances of families of ABC transporters) but which the MCL algorithm has presumably assigned to different families on the basis of distinctive sequence features. Alternatively, promiscuous sequences in these families may possess sequence similarity to sequences outside the set of 13,055 'core' sequences. For example, BLAST analyses of promiscuous sequences derived from Escherichia coli reveal that the genes RBG2, RFC2, RIX7 and RFC3 do not have significant sequence similarity to any of the 59 promiscuous sequences identified in S. cerevisiae (data not shown).
These analyses confirm that COGENT has grouped a number of promiscuous sequences into families on the basis of either domain or species-specific adaptations (groups 2 and 4). Interestingly, there are few examples of families containing promiscuous sequences that are representative of adaptations associated with intermediate taxonomic groups of bacteria (for example, the proteobacteria or spirochaetes). However, further investigations are required to determine if this is biologically meaningful or simply an artifact associated with the sequence clustering algorithm.
Quantifying sequence diversity within a phylogenetic framework
Within the three main proteobacterial divisions (Alphaproteobacteria, Betaproteobacteria and Gamma/Delta/Epsilonproteobacteria) 2-3% of their sequences were common (found in at least one species from each of the three main divisions) and specific to proteobacteria (likely representing core proteobacterial genes). Furthermore, a greater fraction of Betaproteobacterial (6.8%) and Gamma/Delta/Epsilonproteobacterial (4.1%) sequences shared significant similarity with sequences from the other group, compared with the Alphaproteobacteria. Even considering the different sizes of the datasets, these results suggest a closer evolutionary relationship between these first two groups consistent with previous findings .
Within Archaea, a large fraction of sequences was found to be common and specific to the various archaeal groups. For example, 8.6% of sequences associated with Crenarchaeota are specific and common across the Euryacheaota/Crenarchaeota lineage, while 24.3% of Nanoarchaeota genes share sequence similarity only with other Archaea. This suggests a common core of archaeal specific sequences and demonstrates the divergence between archaea and bacteria.
Due to the lack of a robustly defined bacterial phylogeny, rather than attempt to map the remaining sequences common across deeper taxonomic groups, we analyzed the occurrence of sequences with similarity to sequences from one or more additional taxa (Figure 4b). The largest group of sequences (145,647; 31% of the prokaryotic sequences analyzed in this study) was found to be common across all six prokaryotic groups, representing either a core set of housekeeping genes that have arisen through a common ancestor or sequences that may be highly prone to LGT [18, 33–35]. The next largest category (37,700; 8%) consist of sequences that are common to all five categories of bacteria but absent in Archaea. These might represent common ancestral bacterial sequences that have been lost within Archaea and are not readily acquired by these organisms through LGT.
Consequently, the cladograms presented in Figures 4a and 5 do not provide explicit information on relative times of divergence and caution should, therefore, be exercised in interpreting comparisons between the various branches.
Consistent with findings from the fully sequenced eukaryotes, the partial genome datasets contain a larger proportion (approximately 40-70%) of taxon- and species-specific sequences than the prokaryotic datasets. As might be expected, taxa with larger numbers of species contained the highest proportion of conserved taxon-specific sequences (that is, sequences specific to a taxon but common to > 1 species): plants (37.4%, 76 species); ascomycetes (17.9%, 17 species) and tylenchids (17.5%, 15 species). Comparisons between taxa revealed several branches that contain a relatively large proportion of common sequences. Within the nematodes, for example, 10.6% of the 29,666 spirurid sequences have similarity only to rhabditid and/or tylenchid sequences, while reciprocally, 7.4% of the 60,366 rhabditid and tylenchid sequences are also common only to spirurids (the difference in percentages is likely due to the different sizes of the respective datasets). Similarly, 6% of the 14,785 basidiomycete sequences and 3.6% of the 44,358 ascomycete sequences are specific to basidiomycetes and ascomycetes, while 5.4% of 46,420 tetrapod sequences and 8.2% of 28,971 teleost sequences are common and specific to tetrapods and teleosts. These sequences potentially represent core sets of essential nematode, fungi and vertebrate specific genes that arose relatively early in their respective lineages.
Interestingly, despite comparable sequence and gene family discovery rates (Table 2), the arthropod taxa displayed much lower proportions of panarthropod core sequences than the nematode, fungal and deuterostome lineages. For example, only 0.5% of 11,600 insect sequences and 1.4% of 4,041 crustacean sequences are specific to pancrustacea. Although this might indicate a relatively rapid divergence of the three lineages after their ancestral split from the nematodes, these findings might also be readily explained by the lower number of available sequences associated with these taxa. Additional sequencing within the arthropods may, therefore, increase the number of such common sequences with a corresponding decrease in the proportion of species and taxon-specific sequences associated with these groups.
Many sequences are also associated with deeper taxonomic splits within Eukarya. Of particular note, approximately 8% of metazoan sequences are common and specific to the protostomes and deuterostomes. These likely represent sequences involved in providing basic multicellular functionality (for example, cell-cell communication and differentiation). Additionally, 1.8% of metazoan sequences and 4.8% of fungal sequences are common to the fungi/metazoan divide. Again, these sequences may represent specific adaptations adopted by the early ancestors of the opistokonts. Since the root of the tree connecting protists, plants and fungi/metazoa has not been well defined, we analyzed each combination separately. Approximately 18-21% of eukaryotic sequences are common to each of the three major eukaryotic taxonomic groups. These sequences are expected to be involved in basic housekeeping functions, such as DNA processing and cellular metabolism.
Comparisons with the prokaryotic datasets revealed that sequences furthest from the root of eukaryotes were less likely to share similarity with a prokaryotic sequence. For example, 58.8% (60,485) of the 102,868 core eukaryotic sequences, 29.4% (1,637) of the 5,573 Fungi-Metazoa specific sequences, and only 14.2% (2,196) of the 15,486 Metazoa specific sequences shared similarity with a prokaryotic sequence. Furthermore, for the majority (135 of 193) of the species-specific datasets, less than 2% of their sequences had significant matches to a prokaryotic sequence. The incidence of a small fraction of these sequences sharing similarity with prokaryotic sequences may reflect a low incidence sequence acquisition through LGT .
While the use of partial genomes offers a breadth and depth of sequence sampling unrivalled by full genomes, potential drawbacks of these datasets have been documented [14, 17, 41]. Indeed, we note that in comparing sequence length with conservation, longer sequences tend to be more highly conserved (Additional data file 7). However, previous reports supporting the legitimacy of short sequences [14, 41], together with the consistency in the proportion of species-specific sequences with the fully sequenced eukaryotic datasets, highlights the usefulness of sequences derived from partial genomes as surrogates for those derived from complete genomes.
Functional analysis of highly conserved eukaryotic sequences
Recently there has been considerable interest in exploring the generation and extent of genetic diversity [4, 5, 20]. Due to the lack of fully sequenced genomes available for eukaryotes, such studies have tended to focus on prokaryotes. Here we have supplemented 198 fully sequenced genomes (19 from eukaryotes) with 193 EST derived datasets (partial genomes) to undertake a comprehensive global analysis of sequence diversity. Our results indicate that eukaryotic (partial genome) datasets tend to possess a higher proportion of taxon- and species-specific sequences in addition to demonstrating a higher rate of novel sequence discovery than prokaryotes. While this reduction in diversity may be associated with biases in the choice of bacterial genomes for sequencing (for example, due to medical and/or economical interests), such biases could only partially account for the observed differences. It is also interesting to note that a recent metagenomics project, focusing on the collation of global ocean samples and hence not subject to the same sampling biases, reported that 14% of sequence reads obtained from a single site were unique to that site [9, 42]. Despite methodological differences, the consistency in these rates provides an additional level of support that biases in genome sampling have not adversely influenced our results.
A major driving force of sequence diversity is thought to involve the duplication of genes followed by their subsequent divergence [2, 12]. The observed differences between the bacterial and partial genome datasets may, therefore, be driven by factors such as generation times and metabolic rates . However, another potentially important factor is the role played by LGT and its ability to transfer new genes (for example, created through duplication and divergence) across species [18, 33–35, 44]. Recent interest in LGT events has led to the concept of a microbial pan-genome consisting of a global pool of genes that are continuously being exchanged within and between prokaryotic species [45, 46]. This constant exchange would obviously decrease the number of species-specific genes relative to those species that mainly rely on vertical transmission of genetic information (that is, eukaryotes). Intriguingly, if LGT is a major source of gene acquisition, the identification of 'core' sequences limited to specific prokaryotic taxa (for example, Proteobacteria) may indicate sets of sequences not readily transferred through LGT. The provision of these sequence datasets will, therefore, facilitate future studies exploring the relationship between their sequence and/or functional properties and their restriction to specific lineages.
Focusing on eukaryotes, by mapping their sequences onto a phylogenetic framework, we identified a widely populated spectrum of sequence specificity. At one extreme approximately 20% of eukaryotic sequences are highly conserved and may represent ancestral eukaryotic genes under significant selective constraints. At the other extreme, from 40-60% of sequences are specific to individual or closely related species. Such sequences represent genes that are either under reduced selective constraints, provide newly acquired functionality (that is, neo- or sub-functionalization ) or are simply redundant and in the process of being lost. Between these two extremes several regions ('landmarks') within the phylogenetic landscape were identified as demonstrating high levels of sequence conservation (for example, the 8% of metazoan sequences that are specific and conserved across protostomes and deuterostomes). In addition to ancestral genes that may have been lost in other lineages, these sequences represent newly evolved genes subject, as they arise, to selective constraints restricting further diversification. An intriguing question concerns whether these landmarks represent 'bursts' of concerted gene innovations. For example, sequences mediating cell adhesion or cell-cell communication would promote a multi-cellular lifestyle that may have resulted in the rapid generation of metazoan specific sequences) . As for the highly conserved sequences, subsequent diversification of these sequences may be limited by these altered constraints. Alternatively, depending upon the relative times of divergence, these landmarks may simply reflect extended periods of evolution allowing the continued accumulation of sequences prior to a divergence event. For example, the relatively high proportion of metazoan specific sequences (8%) may simply reflect an extended period, from the divergence of fungi from metazoan to the divergence of protostomes and deuterostomes (which one estimate puts at 500 Mya ). Relative divergence times might also account for the relatively low numbers of sequences shared across the three major groups of arthropods. Molecular clocks estimate the time of divergence of insects and crustacea to be approximately 650 Mya and that of panarthropods from chelicerates as approximately 720 Mya . Hence, it may not be surprising that during the 70 Mya between these two splits, only 0.5% of insect sequences and 1.4% of crustacean sequences were generated and remain common and specific to these two taxa (compare with the 300 Mya between the time of divergence of the tetrapods and teleosts and their earlier divergence from the cephalochordates ). On the other hand, as noted earlier, factors such as generation time and metabolic rates in addition to significant changes in population size could also play a role in the observed increases in rates of sequence innovation [43, 50, 51].
With current ambiguities in the timing of divergence events , interpretation of these data would greatly benefit from the availability of a fully resolved and robustly timed phylogeny. Conversely, these data may be usefully combined with additional experimental and theoretical studies to unravel the relative influence of these various factors on the generation of sequence diversity within the Eukarya [53, 54].
The collation and comparison of EST based datasets from 193 species provides the first detailed analysis of sequence conservation across Eukarya and highlights significant differences from prokaryotes. In particular, we find that eukaryotes have a much higher incidence of novel sequences than prokaryotes, which may be related to the lower incidence of LGT events. Placing these sequences within a phylogenetic framework provides a detailed map of the origins and extent of sequence diversity. It further allows the identification of sequences specific and conserved to distinct taxonomic groups that are likely to be associated with novel taxon specific innovations. The provision of taxon specific sequences should thus prove valuable for additional computational and biochemical analyses aimed at understanding evolutionary and functional relationships.
Materials and methods
The predicted protein sequences of 198 complete genomes used in this study were obtained from the COGENT database . Sequences associated with 193 partial genomes were obtained from the PartigeneDB database . A list of all species datasets is given in Additional data files 2 and 3. A summary of the taxonomic groups represented in this study is shown in Table 1. Detailed taxonomic relationships of the organisms are presented in Additional data file 1. Species were assigned to each group on the basis of taxonomic information derived from the NCBI TaxBrowser resource . Due to ambiguity concerning the phylogenetic relationships of bacteria , no attempt is made to provide an evolutionary perspective beyond the major bacterial groups (proteobacteria, actinobacteria/firmicutes, cyanobacteria, spirochaetes and 'other bacteria' - encompassing species that do not fall in one of the aforementioned groups). Archaeal relationships were inferred with reference to . Eukaryotic relationships were inferred from a number of previously published studies as follows: while the relationships between Fungi, Metazoa and Viridiplantae have been well established, protests are thought to derive from several paraphyletic groups arising at (or close to) the root of the three other major eukaryotic groups . Due to the limited number of partial genomes from this group and to simplify analyses, the 17 protist partial genomes were collated into a single group. For fungi (four taxonomic groups - Ascomycota, Basidiomycota, Glomeromycota and Zygomycota), nematodes (four taxa - Rhabditina, Tylenchina, Spirurina and Dorylaimia) and arthropods/tardigrades (four taxa - Hexapoda, Crustacea, Chelicerata and Tardigrada) the taxonomic divisions and phylogenetic relationships were defined according to previous, established studies [58–60]. Deuterostomes may be readily categorised into five well-defined taxa - Tetrapoda, Teleosta, Cephalochordata, Urochordata and Echinodermata. However, the phylogenetic relationships of the cephalochordates and urochordates with respect to the other three groups remain unclear. For the purposes of our analyses, we assumed relationships based on previously published studies of ribosomal RNA analyses [61, 62]. Finally, there has been much debate concerning the grouping of the major metazoan clades [63–65]. We have chosen to follow the scheme proposed by a recent study performed by Philippe and co-workers . In their analysis of a large dataset of 146 genes derived from a diverse set of 35 animals, they provide strong support for arthropods and nematodes belonging to the protostome group Ecdysozoa. The protostomes (which also include Lophotrochozoans such as mollusks and platyhelminths) then form a group distinct from the deuterostomes, which include the chordates and hemichordates (the so-called Lophotrochozoa-Ecdysozoa-Deuterostomia (L-E-D) hypothesis ).
Given the scale of sequence similarity searches, BLAST  provides the most practical tool for performing these types of analyses. Unlike more sensitive algorithms such as PSI-BLAST, it is both fast and easily automated, allowing the comparison of the hundreds of datasets considered here. For each sequence from each complete and partial genome, a series of BLASTs were performed. The predicted proteomes from each complete genome were compared to each other using BLASTP, while sequences from partial genomes were compared using TBLASTX. Raw bit scores were extracted and stored within an in-house PostgreSQL based database . This results in the creation of a phylogenetic profile  for each sequence from which its relationship to various taxonomic groups can be derived. Significant matches were defined as those having a raw BLAST score of greater or equal to 50 (equivalent to a typical E-score of about 10-5). Previous studies have concluded that the choice of threshold does not significantly affect qualitative findings in these types of analyses [15, 17]. We therefore chose an intermediate threshold that minimizes erroneous matches while maximizing the possibility of identifying related sequences. A full list of species, their taxonomic group, numbers of sequences, fraction of species-specific sequences and fraction of sequences specific to a limited number of taxonomic groups are provided in Additional data files 2 and 3. For the analyses of sequence discovery rates, as genomes are included in a cumulative sequence ensemble (that is, sampled), we identify 'distinct' sequences as those that do not possess significant sequence similarity to sequences from genomes that have already been sampled. A sliding window of 30 genomes was then used to obtain the gradient of the number of distinct sequences compared against the number of sequences that have been sampled. Due to the use of this sliding window, for each dataset, values were not obtained for the first 15 or last 15 genome additions. Four hundred random orderings of genome additions were generated to derive the average and standard deviation of sequence discovery rates for each dataset.
Gene family analyses
Gene family predictions for the 198 complete genome datasets obtained from COGENT have previously been calculated through the use of the TribeMCL algorithm . In order to obtain the predictions for the partial genome datasets, which consist of DNA as opposed to protein sequences, it was first necessary to calibrate the parameters (inflation values and E-value cutoffs) used by the TribeMCL algorithm to maintain consistency with the COGENT analyses. We therefore obtained the cDNA and associated peptide sequences for C. elegans from Wormbase  (Wormpep version 160). After clustering these peptide sequences by TribeMCL using similar parameters used to construct the COGENT gene family predictions (E-value cutoff = 0.001; inflation value = 1.86), a comprehensive scan of parameters was performed for clustering the cDNA sequences. An E-value cutoff of 0.001 and an inflation value of 1.635 were found to provide the most similar number of gene families in the cDNA based families compared with the peptide based families (11,020 versus 11,018, respectively). These parameters were used to cluster the 546,451 partial genome sequences and derive a single set of gene family predictions that encompass all of the partial genomes. Gene family discovery rates were obtained as for the sequence discovery rates - as genomes are sampled, we identify new gene families (including singletons) as those that do not include sequence members from genomes that have already been sampled. Again, we used a sliding window of 30 genomes to obtain the gradient of the number of gene families compared against the total number of sampled sequences. Furthermore, four hundred random orderings of genome additions were generated to derive the average and standard deviation of gene family discovery rates for each dataset.
Two sets of functional annotations were used in this study. In the first set, broad functional characterizations were obtained for sequences in both the complete and partial genome datasets with reference to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (release 32.0; 748,177 protein sequences from 206 genomes) . All sequences were assigned to a functional category according to the sequence in KEGG with the highest sequence similarity (BLAST raw score cut-off of ≥ 50). Sequences that did not have significant sequence similarity to a protein in KEGG with a previously assigned functional category were characterized as 'Unknown'. The second set of annotations used in this study, providing more detail on specific gene functions, was applied only to sequences from the partial genome datasets. In brief, each sequence was subjected to a BLAST search against the protein non-redundant database (2.5 million sequences). Up to five matches with E-values < 10-5 were extracted. Meaningful annotations were then extracted from the descriptions of the first 'hit' that did not contain 'hypothetical', 'unknown' or 'unnamed'.
Additional data files
The following additional data are available with the online version of this paper. Additional data file 1 is a figure showing the phylogenetic relationships of the taxonomic groups discussed in the main text. Additional data files 2 and 3 are tables listing the species used in the study together with a detailed breakdown of the taxonomic relationships of their sequences. Additional data file 4 is a figure comparing sequence similarity relationships between members of 16 highly conserved gene families for eukaryotes and bacteria. Additional data file 5 is a figure showing rates of sequence and gene family discovery in different sets of complete and partial genomes. Additional data file 6 is a figure showing the relationship between genome size and number of species-specific sequences. Additional data file 7 is a figure showing the relationship between the sequence conservation and length.
current gene family discovery rate
current sequence discovery rate
expressed sequence tag
Kyoto Encyclopedia of Genes and Genomes
lateral gene transfer
millions of years
National Center for Biotechnology Information
overall gene family discovery rate
overall sequence discovery rate.
Computational analyses were performed using facilities at the Center for Computational Biology, Hospital for Sick Children, Toronto. This work was funded by a grant from the Canadian Institute for Health Research (CIHR). JMP-A is supported by the Hospital for Sick Children (Toronto, Ontario, Canada) Research Training Centre. JP is supported by a CIHR New Investigators award. We would like to thank our colleagues, especially Mark Blaxter, Alan Davidson, Gabriel Moreno-Hagelsieb and James Wasmuth in addition to the two anonymous reviewers for providing many insightful comments during the drafting of the manuscript.
- Arnold FH: Fancy footwork in the sequence space shuffle. Nat Biotechnol. 2006, 24: 328-330. 10.1038/nbt0306-328.PubMedView ArticleGoogle Scholar
- Chothia C, Gough J, Vogel C, Teichmann SA: Evolution of the protein repertoire. Science. 2003, 300: 1701-1703. 10.1126/science.1085371.PubMedView ArticleGoogle Scholar
- Bernal A, Ear U, Kyrpides N: Genomes OnLine Database (GOLD): a monitor of genome projects world-wide. Nucleic Acids Res. 2001, 29: 126-127. 10.1093/nar/29.1.126.PubMedPubMed CentralView ArticleGoogle Scholar
- Gill SR, Pop M, Deboy RT, Eckburg PB, Turnbaugh PJ, Samuel BS, Gordon JI, Relman DA, Fraser-Liggett CM, Nelson KE: Metagenomic analysis of the human distal gut microbiome. Science. 2006, 312: 1355-1359. 10.1126/science.1124234.PubMedPubMed CentralView ArticleGoogle Scholar
- Venter JC, Remington K, Heidelberg JF, Halpern AL, Rusch D, Eisen JA, Wu D, Paulsen I, Nelson KE, Nelson W, et al: Environmental genome shotgun sequencing of the Sargasso Sea. Science. 2004, 304: 66-74. 10.1126/science.1093857.PubMedView ArticleGoogle Scholar
- Enright AJ, Kunin V, Ouzounis CA: Protein families and TRIBES in genome sequence space. Nucleic Acids Res. 2003, 31: 4632-4638. 10.1093/nar/gkg495.PubMedPubMed CentralView ArticleGoogle Scholar
- Gophna U, Doolittle WF, Charlebois RL: Weighted genome trees: refinements and applications. J Bacteriol. 2005, 187: 1305-1316. 10.1128/JB.187.4.1305-1316.2005.PubMedPubMed CentralView ArticleGoogle Scholar
- Zdobnov EM, von Mering C, Letunic I, Bork P: Consistency of genome-based methods in measuring Metazoan evolution. FEBS Lett. 2005, 579: 3355-3361. 10.1016/j.febslet.2005.04.006.PubMedView ArticleGoogle Scholar
- Yooseph S, Sutton G, Rusch DB, Halpern AL, Williamson SJ, Remington K, Eisen JA, Heidelberg KB, Manning G, Li W, et al: The Sorcerer II global ocean sampling expedition: expanding the universe of protein families. PLoS Biol. 2007, 5: e16-10.1371/journal.pbio.0050016.PubMedPubMed CentralView ArticleGoogle Scholar
- Luger K, Mader AW, Richmond RK, Sargent DF, Richmond TJ: Crystal structure of the nucleosome core particle at 2.8 A resolution. Nature. 1997, 389: 251-260. 10.1038/38444.PubMedView ArticleGoogle Scholar
- Woese C: The universal ancestor. Proc Natl Acad Sci USA. 1998, 95: 6854-6859. 10.1073/pnas.95.12.6854.PubMedPubMed CentralView ArticleGoogle Scholar
- Chothia C, Gerstein M: Protein evolution. How far can sequences diverge?. Nature. 1997, 385: 579-581. 10.1038/385579a0.PubMedView ArticleGoogle Scholar
- Fischer D, Eisenberg D: Finding families for genomic ORFans. Bioinformatics. 1999, 15: 759-762. 10.1093/bioinformatics/15.9.759.PubMedView ArticleGoogle Scholar
- Daubin V, Ochman H: Bacterial genomes as new gene homes: the genealogy of ORFans in E. coli. Genome Res. 2004, 14: 1036-1042. 10.1101/gr.2231904.PubMedPubMed CentralView ArticleGoogle Scholar
- Charlebois RL, Doolittle WF: Computing prokaryotic gene ubiquity: rescuing the core from extinction. Genome Res. 2004, 14: 2469-2477. 10.1101/gr.3024704.PubMedPubMed CentralView ArticleGoogle Scholar
- Harris JK, Kelley ST, Spiegelman GB, Pace NR: The genetic core of the universal ancestor. Genome Res. 2003, 13: 407-412. 10.1101/gr.652803.PubMedPubMed CentralView ArticleGoogle Scholar
- Siew N, Fischer D: Analysis of singleton ORFans in fully sequenced microbial genomes. Proteins. 2003, 53: 241-251. 10.1002/prot.10423.PubMedView ArticleGoogle Scholar
- Ochman H, Lerat E, Daubin V: Examining bacterial species under the specter of gene transfer and exchange. Proc Natl Acad Sci USA. 2005, 102 (Suppl 1): 6595-6599. 10.1073/pnas.0502035102.PubMedPubMed CentralView ArticleGoogle Scholar
- Blaxter M: Opinion piece. Genome sequencing: time to widen our horizons. Brief Funct Genomic Proteomic. 2002, 1: 7-9. 10.1093/bfgp/1.1.7.PubMedView ArticleGoogle Scholar
- Daffonchio D, Borin S, Brusa T, Brusetti L, van der Wielen PW, Bolhuis H, Yakimov MM, D'Auria G, Giuliano L, Marty D, et al: Stratified prokaryote network in the oxic-anoxic transition of a deep-sea halocline. Nature. 2006, 440: 203-207. 10.1038/nature04418.PubMedView ArticleGoogle Scholar
- Hughes J, Longhorn SJ, Papadopoulou A, Theodorides K, de Riva A, Mejia-Chang M, Foster PG, Vogler AP: Dense taxonomic EST sampling and its applications for molecular systematics of the Coleoptera (beetles). Mol Biol Evol. 2006, 23: 268-278. 10.1093/molbev/msj041.PubMedView ArticleGoogle Scholar
- Kenyon F, Welsh M, Parkinson J, Whitton C, Blaxter ML, Knox DP: Expressed sequence tag survey of gene expression in the scab mite Psoroptes ovis - allergens, proteases and free-radical scavengers. Parasitology. 2003, 126: 451-460. 10.1017/S0031182003003044.PubMedView ArticleGoogle Scholar
- Mineta K, Nakazawa M, Cebria F, Ikeo K, Agata K, Gojobori T: Origin and evolutionary process of the CNS elucidated by comparative genomics analysis of planarian ESTs. Proc Natl Acad Sci USA. 2003, 100: 7666-7671. 10.1073/pnas.1332513100.PubMedPubMed CentralView ArticleGoogle Scholar
- Parkinson J, Mitreva M, Whitton C, Thomson M, Daub J, Martin J, Schmid R, Hall N, Barrell B, Waterston RH, et al: A transcriptomic analysis of the phylum Nematoda. Nat Genet. 2004, 36: 1259-1267. 10.1038/ng1472.PubMedView ArticleGoogle Scholar
- Parkinson J, Anthony A, Wasmuth J, Schmid R, Hedley A, Blaxter M: PartiGene - constructing partial genomes. Bioinformatics. 2004, 20: 1398-1404. 10.1093/bioinformatics/bth101.PubMedView ArticleGoogle Scholar
- Peregrin-Alvarez JM, Yam A, Sivakumar G, Parkinson J: PartiGeneDB - collating partial genomes. Nucleic Acids Res. 2005, D303-307. 33 Database
- Global Analysis of Sequence Diversity. [http://www.compsysbio.org/GASD]
- Kunin V, Cases I, Enright AJ, de Lorenzo V, Ouzounis CA: Myriads of protein families, and still counting. Genome Biol. 2003, 4: 401-10.1186/gb-2003-4-2-401.PubMedPubMed CentralView ArticleGoogle Scholar
- Simpson AG, Roger AJ: The real 'kingdoms' of eukaryotes. Curr Biol. 2004, 14: R693-696. 10.1016/j.cub.2004.08.038.PubMedView ArticleGoogle Scholar
- Koonin EV: Comparative genomics, minimal gene-sets and the last universal common ancestor. Nat Rev Microbiol. 2003, 1: 127-136. 10.1038/nrmicro751.PubMedView ArticleGoogle Scholar
- Janssen P, Enright AJ, Audit B, Cases I, Goldovsky L, Harte N, Kunin V, Ouzounis CA: COmplete GENome Tracking (COGENT): a flexible data environment for computational genomics. Bioinformatics. 2003, 19: 1451-1452. 10.1093/bioinformatics/btg161.PubMedView ArticleGoogle Scholar
- The NCBI Taxonomy Homepage. [http://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html/]
- Comas I, Moya A, Azad RK, Lawrence JG, Gonzalez-Candelas F: The evolutionary origin of Xanthomonadales genomes and the nature of the horizontal gene transfer process. Mol Biol Evol. 2006, 23: 2049-2057. 10.1093/molbev/msl075.PubMedView ArticleGoogle Scholar
- Ochman H, Lawrence JG, Groisman EA: Lateral gene transfer and the nature of bacterial innovation. Nature. 2000, 405: 299-304. 10.1038/35012500.PubMedView ArticleGoogle Scholar
- Zhaxybayeva O, Gogarten JP, Charlebois RL, Doolittle WF, Papke RT: Phylogenetic analyses of cyanobacterial genomes: quantification of horizontal gene transfer events. Genome Res. 2006, 16: 1099-1108. 10.1101/gr.5322306.PubMedPubMed CentralView ArticleGoogle Scholar
- Douzery EJ, Snell EA, Bapteste E, Delsuc F, Philippe H: The timing of eukaryotic evolution: does a relaxed molecular clock reconcile proteins and fossils?. Proc Natl Acad Sci USA. 2004, 101: 15386-15391. 10.1073/pnas.0403984101.PubMedPubMed CentralView ArticleGoogle Scholar
- Hedges SB, Blair JE, Venturi ML, Shoe JL: A molecular timescale of eukaryote evolution and the rise of complex multicellular life. BMC Evol Biol. 2004, 4: 2-10.1186/1471-2148-4-2.PubMedPubMed CentralView ArticleGoogle Scholar
- Peterson KJ, Lyons JB, Nowak KS, Takacs CM, Wargo MJ, McPeek MA: Estimating metazoan divergence times with a molecular clock. Proc Natl Acad Sci USA. 2004, 101: 6536-6541. 10.1073/pnas.0401670101.PubMedPubMed CentralView ArticleGoogle Scholar
- Pisani D, Poling LL, Lyons-Weiler M, Hedges SB: The colonization of land by animals: molecular phylogeny and divergence times among arthropods. BMC Biol. 2004, 2: 1-10.1186/1741-7007-2-1.PubMedPubMed CentralView ArticleGoogle Scholar
- Andersson JO: Lateral gene transfer in eukaryotes. Cell Mol Life Sci. 2005, 62: 1182-1197. 10.1007/s00018-005-4539-z.PubMedView ArticleGoogle Scholar
- Brocchieri L, Karlin S: Protein length in eukaryotic and prokaryotic proteomes. Nucleic Acids Res. 2005, 33: 3390-3400. 10.1093/nar/gki615.PubMedPubMed CentralView ArticleGoogle Scholar
- Rusch DB, Halpern AL, Sutton G, Heidelberg KB, Williamson S, Yooseph S, Wu D, Eisen JA, Hoffman JM, Remington K, et al: The Sorcerer II global ocean sampling expedition: Northwest Atlantic through eastern tropical Pacific. PLoS Biol. 2007, 5: e77-10.1371/journal.pbio.0050077.PubMedPubMed CentralView ArticleGoogle Scholar
- Martin AP, Palumbi SR: Body size, metabolic rate, generation time, and the molecular clock. Proc Natl Acad Sci USA. 1993, 90: 4087-4091. 10.1073/pnas.90.9.4087.PubMedPubMed CentralView ArticleGoogle Scholar
- Marri PR, Hao W, Golding GB: The role of laterally transferred genes in adaptive evolution. BMC Evol Biol. 2007, 7 (Suppl 1): S8-10.1186/1471-2148-7-S1-S8.PubMedPubMed CentralView ArticleGoogle Scholar
- Medini D, Donati C, Tettelin H, Masignani V, Rappuoli R: The microbial pan-genome. Curr Opin Genet Dev. 2005, 15: 589-594. 10.1016/j.gde.2005.09.006.PubMedView ArticleGoogle Scholar
- Tettelin H, Masignani V, Cieslewicz MJ, Donati C, Medini D, Ward NL, Angiuoli SV, Crabtree J, Jones AL, Durkin AS, et al: Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae : implications for the microbial "pan-genome". Proc Natl Acad Sci USA. 2005, 102: 13950-13955. 10.1073/pnas.0506758102.PubMedPubMed CentralView ArticleGoogle Scholar
- He X, Zhang J: Rapid subfunctionalization accompanied by prolonged and substantial neofunctionalization in duplicate gene evolution. Genetics. 2005, 169: 1157-1164. 10.1534/genetics.104.037051.PubMedPubMed CentralView ArticleGoogle Scholar
- Moran NA, Plague GR: Genomic changes following host restriction in bacteria. Curr Opin Genet Dev. 2004, 14: 627-633. 10.1016/j.gde.2004.09.003.PubMedView ArticleGoogle Scholar
- Blair Hedges S, Kumar S: Genomic clocks and evolutionary timescales. Trends Genet. 2003, 19: 200-206. 10.1016/S0168-9525(03)00053-2.PubMedView ArticleGoogle Scholar
- Chao L, Carr D: The molecular clock and the relationship between population size and generation time. Evolution. 1993, 47: 688-690. 10.2307/2410082.View ArticleGoogle Scholar
- Laird CD, McConaughy BL, McCarthy BJ: Rate of fixation of nucleotide substitutions in evolution. Nature. 1969, 224: 149-154. 10.1038/224149a0.PubMedView ArticleGoogle Scholar
- Jermiin LS, Poladian L, Charleston MA: Evolution. Is the "Big Bang" in animal evolution real?. Science. 2005, 310: 1910-1911. 10.1126/science.1122440.PubMedView ArticleGoogle Scholar
- Fraser C, Hanage WP, Spratt BG: Recombination and the nature of bacterial speciation. Science. 2007, 315: 476-480. 10.1126/science.1127573.PubMedPubMed CentralView ArticleGoogle Scholar
- Gillooly JF, Allen AP, West GB, Brown JH: The rate of DNA evolution: Effects of body size and temperature on the molecular clock. Proc Natl Acad Sci USA. 2005, 102: 140-145. 10.1073/pnas.0407735101.PubMedPubMed CentralView ArticleGoogle Scholar
- Wheeler DL, Church DM, Federhen S, Lash AE, Madden TL, Pontius JU, Schuler GD, Schriml LM, Sequeira E, Tatusova TA, et al: Database resources of the National Center for Biotechnology. Nucleic Acids Res. 2003, 31: 28-33. 10.1093/nar/gkg033.PubMedPubMed CentralView ArticleGoogle Scholar
- Bapteste E, Boucher Y, Leigh J, Doolittle WF: Phylogenetic reconstruction and lateral gene transfer. Trends Microbiol. 2004, 12: 406-411. 10.1016/j.tim.2004.07.002.PubMedView ArticleGoogle Scholar
- Allers T, Mevarech M: Archaeal genetics - the third way. Nat Rev Genet. 2005, 6: 58-73. 10.1038/nrg1504.PubMedView ArticleGoogle Scholar
- Blaxter ML, De Ley P, Garey JR, Liu LX, Scheldeman P, Vierstraete A, Vanfleteren JR, Mackey LY, Dorris M, Frisse LM, et al: A molecular evolutionary framework for the phylum Nematoda. Nature. 1998, 392: 71-75. 10.1038/32160.PubMedView ArticleGoogle Scholar
- James TY, Kauff F, Schoch CL, Matheny PB, Hofstetter V, Cox CJ, Celio G, Gueidan C, Fraker E, Miadlikowska J, et al: Reconstructing the early evolution of fungi using a six-gene phylogeny. Nature. 2006, 443: 818-822. 10.1038/nature05110.PubMedView ArticleGoogle Scholar
- Regier JC, Shultz JW, Kambic RE: Pancrustacean phylogeny: hexapods are terrestrial crustaceans and maxillopods are not monophyletic. Proc Biol Sci. 2005, 272: 395-401. 10.1098/rspb.2004.2917.PubMedPubMed CentralView ArticleGoogle Scholar
- Cameron CB, Garey JR, Swalla BJ: Evolution of the chordate body plan: new insights from phylogenetic analyses of deuterostome phyla. Proc Natl Acad Sci USA. 2000, 97: 4469-4474. 10.1073/pnas.97.9.4469.PubMedPubMed CentralView ArticleGoogle Scholar
- Winchell CJ, Sullivan J, Cameron CB, Swalla BJ, Mallatt J: Evaluating hypotheses of deuterostome phylogeny and chordate evolution with new LSU and SSU ribosomal DNA data. Mol Biol Evol. 2002, 19: 762-776.PubMedView ArticleGoogle Scholar
- Aguinaldo AM, Turbeville JM, Linford LS, Rivera MC, Garey JR, Raff RA, Lake JA: Evidence for a clade of nematodes, arthropods and other moulting animals. Nature. 1997, 387: 489-493. 10.1038/387489a0.PubMedView ArticleGoogle Scholar
- Philippe H, Lartillot N, Brinkmann H: Multigene analyses of bilaterian animals corroborate the monophyly of Ecdysozoa, Lophotrochozoa, and Protostomia. Mol Biol Evol. 2005, 22: 1246-1253. 10.1093/molbev/msi111.PubMedView ArticleGoogle Scholar
- Wolf YI, Rogozin IB, Koonin EV: Coelomata and not Ecdysozoa: evidence from genome-wide phylogenetic analysis. Genome Res. 2004, 14: 29-36. 10.1101/gr.1347404.PubMedPubMed CentralView ArticleGoogle Scholar
- Jones M, Blaxter M: Evolutionary biology: animal roots and shoots. Nature. 2005, 434: 1076-1077. 10.1038/4341076a.PubMedView ArticleGoogle Scholar
- Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997, 25: 3389-3402. 10.1093/nar/25.17.3389.PubMedPubMed CentralView ArticleGoogle Scholar
- PostgreSQL. [http://www.postgresql.org]
- Pellegrini M, Marcotte EM, Thompson MJ, Eisenberg D, Yeates TO: Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proc Natl Acad Sci USA. 1999, 96: 4285-4288. 10.1073/pnas.96.8.4285.PubMedPubMed CentralView ArticleGoogle Scholar
- Enright AJ, Van Dongen S, Ouzounis CA: An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 2002, 30: 1575-1584. 10.1093/nar/30.7.1575.PubMedPubMed CentralView ArticleGoogle Scholar
- Chen N, Harris TW, Antoshechkin I, Bastiani C, Bieri T, Blasiar D, Bradnam K, Canaran P, Chan J, Chen CK, et al: WormBase: a comprehensive data resource for Caenorhabditis biology and genomics. Nucleic Acids Res. 2005, D383-389. 33 Database
- Kanehisa M, Goto S: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000, 28: 27-30. 10.1093/nar/28.1.27.PubMedPubMed CentralView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.