- Open Access
Comparative genomics reveals the distinct evolutionary trajectories of the robust and complex coral lineages
- Hua Ying1Email author,
- Ira Cooke2,
- Susanne Sprungala2,
- Weiwen Wang1,
- David C. Hayward1,
- Yurong Tang1, 3,
- Gavin Huttley1, 3,
- Eldon E. Ball1, 4,
- Sylvain Forêt^1, 4 and
- David J. Miller2, 4Email authorView ORCID ID profile
© The Author(s). 2018
- Received: 23 June 2018
- Accepted: 28 September 2018
- Published: 2 November 2018
Despite the biological and economic significance of scleractinian reef-building corals, the lack of large molecular datasets for a representative range of species limits understanding of many aspects of their biology. Within the Scleractinia, based on molecular evidence, it is generally recognised that there are two major clades, Complexa and Robusta, but the genomic bases of significant differences between them remain unclear.
Draft genome assemblies and annotations were generated for three coral species: Galaxea fascicularis (Complexa), Fungia sp., and Goniastrea aspera (Robusta). Whilst phylogenetic analyses strongly support a deep split between Complexa and Robusta, synteny analyses reveal a high level of gene order conservation between all corals, but not between corals and sea anemones or between sea anemones. HOX-related gene clusters are, however, well preserved across all of these combinations. Differences between species are apparent in the distribution and numbers of protein domains and an apparent correlation between number of HSP20 proteins and stress tolerance. Uniquely amongst animals, a complete histidine biosynthesis pathway is present in robust corals but not in complex corals or sea anemones. This pathway appears to be ancestral, and its retention in the robust coral lineage has important implications for coral nutrition and symbiosis.
The availability of three new coral genomes enabled recognition of a de novo histidine biosynthesis pathway in robust corals which is only the second identified biosynthetic difference between corals. These datasets provide a platform for understanding many aspects of coral biology, particularly the interactions of corals with their endosymbionts.
- Complex coral
- Robust coral
- Nucleotide substitution model
- Hox cluster
- Gene family expansion
- Histidine biosynthesis
Despite their ecological and economic significance, many aspects of the biology of the reef-building corals (anthozoan cnidarians belonging to the order Scleractinia) are poorly understood. The calcified Scleractinia made a dramatic appearance in the fossil record in the mid-Triassic (~ 240 MYA), but by this stage they were already morphologically diverse, implying a much earlier origin for the order [1–4]. Classical coral taxonomy relied heavily on a small number of morphological features, but molecular data often contradict groupings based on these traditional criteria. For example, many traditionally defined coral families were para- (or sometimes poly-) phyletic in molecular analyses [5, 6].
Although the timing of the origins and major divergences within the Scleractinia remains equivocal, all of the available molecular data imply that most extant corals fall into two major clades (“superfamilies”) known as the Complexa (complex corals) and Robusta (robust corals). This dichotomy was originally proposed based on partial 16S rDNA data  and is supported in the majority of molecular analyses [8–10]. The nomenclature (Complexa/Robusta) was chosen to reflect perceived differences in extent/density of calcification in the range of corals originally studied, “complex” corals being nominally less heavily calcified than “robust” corals . Although this generalisation is questionable, the Complexa/Robusta nomenclature still stands and the split is recognised as real, despite the fact that few morphological or biological criteria resolve the two groups.
One characteristic by which robust and complex corals can be distinguished is mitochondrial genome composition. The mt genomes of robust corals have significantly lower (G + C) content than those of complex corals or corallimorpharians, one consequence of which appears to be significantly higher phenylalanine content in mitochondrially encoded proteins . It has been speculated that these differences might reflect impaired mtDNA repair in robust corals , but empirical data in support of this are as yet lacking. Also, based on a limited number of species, differences appear to exist in the early development of robust and complex corals . Amongst corals, early development has been most extensively studied in Acropora (a complex coral) species, where gastrulation occurs from what is colloquially known as a “prawn chip”—essentially a bilayer of undifferentiated cells that lacks a blastocoel [13, 14]. Similar developmental patterns have been documented in a number of other complex corals, but not in robust corals, where gastrulation occurs by invagination of an essentially spherical blastula [12, 15, 16].
One reason for the lack of features distinguishing the two clades is the relative paucity of large molecular datasets for a representative range of corals. Until recently, whole genome data have been available for only two anthozoan cnidarians—the (complex) coral Acropora digitifera , which has endosymbiotic Symbiodinium, and the sea anemone Nematostella vectensis , which lacks them. More recently, genome assemblies for two other anthozoans which harbour endosymbiotic Symbiodinium have become available; those of the sea anemone Aiptasia  and the robust coral Stylophora pistillata . The availability of the latter assembly permitted the first whole-genome comparisons to be made between robust and complex corals . Note that in the present paper we have retained the usage “Aiptasia”, which was used by Baumgarten et al. , due to taxonomic uncertainty. Where coral genera are mentioned without an explicit statement of clade, a (C) or an (R) has been placed after the name of the genus or species, as appropriate.
To provide a platform for investigation of both differences between individual species and the broader question of general differences between complex and robust corals, genome sequencing and assembly was carried out on a number of corals selected to reflect phenotypic and physiological diversity .
To broaden the range of species for which data are available, here we report the assembly of the genomes of two robust corals, Goniastrea aspera (also known as Coelastrea aspera, NCBI:txid1540031) and Fungia sp. (NCBI:txid46712), and the complex coral Galaxea fascicularis (NCBI:txid46745). Goniastrea (R) and Galaxea (C) are both regarded as “massive” species, whereas Fungia (R) is a solitary coral (a single very large polyp, rather than a colony of smaller individual polyps). Whilst all three have widespread distribution ranges throughout the Indo-Pacific and occur in relatively shallow water, Goniastrea (R) is regarded as one of the most environmentally tolerant species on Indo-Pacific reefs , frequently dominating intertidal zones where it endures prolonged exposure. Indeed, Veron  has described it as being “encountered frequently in places where no coral might be expected to live”. The stress tolerance of Goniastrea is in marked contrast to the sensitivity of the two branching corals Acropora digitifera (C) and Stylophora pistillata (R)  for which genome data are available [17, 20]. Whilst all of these species harbour the photosynthetic endosymbiont Symbiodinium, heterotrophy is thought to play a major role in Galaxea (C) nutrition  and this species is atypical in that its polyps are frequently extended for feeding during the day. Other biological characteristics of these species are summarised in Additional file 1: Table S1.
The present study makes genome-wide comparisons amongst eight species of anthozoan cnidarians, of which four are complex corals, two are robust corals and two are sea anemones. It provides the strongest support available to date for the robust/complex split due to application of the non-stationary general Markov nucleotide substitution model which, at such time depth, is particularly significant. Synteny analyses indicated a remarkable degree of gene order conservation between all corals, but only limited conservation between corals and sea anemones. An exception to this is a cluster of homeobox genes, the order of which is conserved not only between complex and robust corals, but also between corals and the sea anemone, Nematostella. Coral species differed significantly in terms of PFAM-A domain numbers and distribution, and a correlation between stress tolerance and numbers of HSP20/α-crystallin domains was tentatively identified. The most surprising difference, however, was the presence of a fungal-like histidine biosynthesis pathway in robust corals, which is not present in complex corals or sea anemones. This pathway appears to be ancestral and assuming that it is functional, its retention in the robust coral lineage has important implications for coral nutrition and symbiosis.
Genome assembly and annotation
Genome assembly and annotation statistics for the three sequenced coral genomes
Assembled genome size (Mb)a
Number of genesb
Total repeat (%)
Interspersed repeat (%)
For each genome, annotation of protein-coding genes was accomplished by ab initio prediction, supported by ultra-deep transcriptome sequencing (~ 200 million reads per sample, Additional file 2: Table S2) and homologue-based analyses. In total, 35,901, 38,209, and 22,418 genes were identified from Goniastrea (R), Fungia sp.(R), and Galaxea (C) respectively (Table 1; Additional file 2: Table S6). Of these, 70% to 80% appeared to be complete and over 90% were found to have clear homologues from the NR database (Additional file 2: Table S7). The completeness of genome assemblies and gene models was assessed using the Core Eukaryotic Genes Mapping Approach (CEGMA)  and Benchmarking Universal Single-copy Orthologs (BUSCO) . These assessments indicate that the core gene set in the three genomes from this study is within the same range as previously published cnidarian genomes (Additional file 2: Table S8). Moreover, biological names could be assigned to > 60% of genes from UniProt-Swissprot annotations (Additional file 2: Table S9). Well-defined PFAM-A protein domains were identified in approximately 65% of the annotated genes (Additional file 2: Table S10), which is within the same range as in a number of model organisms . In total, unambiguous KEGG K numbers could be assigned to ~ 50% of genes (Additional file 2: Table S11), enabling comprehensive metabolic pathway analyses. The overall consistency in level of functional annotations indicates a consistent high quality of gene models that are suitable for gene content analyses. However, the variability in number of ab initio annotated genes likely reflects the general uncertainties associated with short-read-based assemblies, where gene number estimates can be biased by assembly and annotation artefacts , complicating direct comparisons of gene copy numbers amongst species. To provide broader perspectives on likely differences between complex and robust corals, the Galaxea (C) data were supplemented with genome data from three other members of the Complexa – Acropora digitifera , Acropora millepora (Ying et al., unpublished), and Porites lutea (Robbins et al., unpublished).
Gene-based phylogeny and synteny across the Hexacorallia
Phylogenetic analyses of high-quality single-copy orthologous genes, making use of a recently developed general nucleotide substitution model (see “Methods”) , produced a tree (Fig. 1a) congruent with a monophyletic clade of complex and robust corals. Whilst it is widely recognised that maximum likelihood (ML) methods based on nucleotide substitution models most accurately represent the true underlying evolutionary processes  and are therefore superior to amino acid substitution models , they are usually not used for deeply diverged species due to concerns over sequence divergence saturation . However, amino acid models have been demonstrated to be non-Markovian [31, 33], and their congruence with the underlying Markovian process operating on nucleotides is likely to be rare [34, 35]. This necessitates use of nucleotide-based models of sequence evolution. In order to apply nucleotide-based ML analyses to the coral dataset, 687 (of a total of 2573 identified by OrthoFinder) one-to-one orthologs matching the same SwissProt gene were selected using the criterion of > 60% target coverage. For the analyses, the general nucleotide model, which removes the unrealistic ubiquitous assumptions of stationarity and time-reversible conditions, was employed and time-heterogeneity permitted throughout the phylogeny. To avoid overfitting and reduce the computational burden, a progressive approach (including a model selection strategy) was adopted, starting with four taxa and gradually increasing the number to ultimately resolve the phylogeny relationships for species of interest. The model was fitted to each individual gene, and only genes that satisfied the identifiability conditions (see Methods) were retained, allowing robust inferences to be drawn. Ultimately, this resulted in 91 genes being used for reliable branch length estimation for the full phylogeny (Fig. 1a). The resulting consensus phylogenetic tree clearly separates robust corals (Goniastrea and Fungia) from complex corals (Galaxea, Porites, and Acropora) using the sea anemone Nematostella as outgroup. The same topology was obtained using IQ-TREE built-in amino acid models under partition mode (Additional file 3: Figure S4) [35, 36]. However, the modelling processes are not directly comparable between nucleotide and amino acid models, and the former should be strongly preferred in any future phylogenetic analyses that include more coral species.
Whilst the variable quality of the assembled genomes being compared complicates synteny analyses (fragmented genomes potentially reducing the apparent degree of synteny detected), this issue did not affect the major conclusions being drawn in the present case. Amongst the species included in the analyses, the Galaxea (C) and Nematostella genome assemblies are represented by the largest numbers of scaffolds, but the N50 for the Nematostella assembly was much longer than in the case of the Galaxea (C) assembly (Additional file 2: Table S3). Therefore, the limited extent of synteny observed in the anemone lineage was not an artefact of assembly quality, and despite accurate estimates of divergence times not being available, the analysis presented here provides compelling evidence that extensive intra- and inter-chromosomal rearrangements have occurred in the sea anemone lineage.
In contrast to sea anemones, the extensive synteny observed between complex and robust corals, a divergence that also occurred in deep time, suggests that ancestral gene arrangements may be better preserved in coral genomes than in other anthozoans so far examined. One major caveat to this, however, is that no comparable data are yet available for members of the other major anthozoan sub-class, the Octocorallia. Hopefully, this deficiency will be addressed in the near future, providing broader perspectives on ancestral gene organisation in the Anthozoa.
Clustered organisation of HOX-related genes
Whilst the synteny analyses presented here are consistent with quite different patterns of organisation of HOX-related genes in Aiptasia and Nematostella, our results imply that Aiptasia is atypical and that the cluster structure seen in corals and Nematostella reflects the ancestral state. In some ways, this is not particularly surprising as these two anemones are widely diverged in sea anemone phylogenies based on two nuclear and three mitochondrial genes . Although some cases of inversion and duplication have clearly occurred, the apparent conservation of an anthozoan HOX-related cluster over at least 500 MY suggests that strong selection has acted to maintain the organisation of these genes, presumably reflecting conservation of function . Note that the cluster of HOX-related genes in cnidarians is not orthologous with the “true” HOX cluster of bilaterians, as the cnidarian/bilaterian divergence predated the origins of the latter [39, 47].
In addition to the cluster of HOX-related genes discussed above (“H1” in Fig. 3), a second pair of HOX-related homeobox genes was identified in several corals as well as both sea anemones (although the orientation of the genes differs in the case of the sea anemones; “H2” in Fig. 3; Additional file 2: Table S13). The gene referred to here as HOX2A corresponds to cnox2 in A. millepora , and this linkage was first identified in Nematostella [39, 47]. HOX2A/cnox2 is the cnidarian homologue of the ParaHox gene Gsx, and HOX2B most closely matches Xlox– also a ParaHox gene—although it has been suggested that the cnidarian gene corresponds to both Xlox and the third ParaHox gene, Cdx . This region is syntenic across all the species studied except for Nematostella; the genes flanking the ParaHox gene pair (CD027 and POMP, which encode homologues of the Histone PARylation factor1 and the Proteosome maturation protein UMP1 respectively) are also conserved single-copy genes in each case (with the apparent exception of Nematostella, which has two copies of both CD027 and POMP). The “H2” gene pair represents the cnidarian ParaHox cluster [49, 50], and the conservation of this genomic region across the range of species studied suggests that ParaHox diversification may have been incomplete at the time of the cnidarian/bilaterian divergence.
Patterns of domain and gene distribution: expanded gene families are often tightly linked
Lineage restricted PFAM-A domains
Present in ANEMONE not in coral
Protein of unknown function (DUF3445)
Present in COMPLEX not in robust corals
BCL7, N-terminal conserver region
Bacterial protein of unknown function (DUF853)
Nuclear RNA-splicing-associated protein
Translation initiation factor IF-3, N-terminal domain
Clostridium neurotoxin, N-terminal receptor binding
Histone chaperone domain CHZ
Domain of unknown function (DUF4557)
Protein of unknown function (DUF455)
MotA/TolQ/ExbB proton channel family
Carbohydrate binding domain (family 11)
Cytochrome c oxidase subunit VIIa
DNA replication and checkpoint protein
CutA1 divalent ion tolerance protein
MacB-like periplasmic core domain
Protein of unknown function (DUF2414)
Glycosyl transferase 4-like
Cytoplasmic dynein 1 intermediate chain 2
Bacterial protein of unknown function (HtrL_YibB)
Glycosyltransferase family 17
Telomere-length maintenance and DNA damage repair
Protein of unknown function (DUF1762)
VWA-like domain (DUF2201)
FAM216B protein family
Multi-glycosylated core protein 24 (MGC-24)
Domain of unknown function (DUF3598)
Membrane transport protein
Domain of unknown function (DUF4606)
Starch binding domain
Translation initiation factor IF-2, N-terminal region
Acetohydroxy acid isomeroreductase, catalytic domain
Present in CORAL not in anemone
Present in ROBUST not in complex corals
Recombination-activation protein 1 (RAG1)
Leucine rich repeat N-terminal domain
Domain of unknown function (DUF2341)
Myb/SANT-like DNA-binding domain
Putative toxin 60
Parvovirus coat protein VP1
Protein of unknown function DUF84
Transmembrane secretion effector
Berberine and berberine like
Lamin-B receptor of TUDOR domain
ATP-dependent DNA helicase recG C-terminal
Sigma-70, region 4
GH3 auxin-responsive promoter
Smoothelin cytoskeleton protein
Domain of unknown function (DUF1982)
Hexapeptide repeat of succinyl-transferase
Mitochondrial ribosomal protein L28
Domain of unknown function (DUF4094)
HisG, C-terminal domain
Secretory pathway protein Sec39
Domain of unknown function (DUF1864)
Domain of unknown function (DUF3496)
Phage T7 tail fibre protein
Domain of unknown function (DUF4613)
Hydroxyethylthiazole kinase family
GDSL-like Lipase/Acylhydrolase family
Protein of unknown function DUF72
Signal recognition particle 9 kDa protein (SRP9)
STAT protein, protein interaction domain
2′,3′-cyclic nucleotide 3′-phosphodiesterase (CNP or CNPase)
Uncharacterised protein family UPF0066
NAD(P)H binding domain of trans-2-enoyl-CoA reductase
Growth arrest and DNA-damage-inducible proteins-interacting protein 1
BRCA2, oligonucleotide/oligosaccharide-binding, domain 3
Although relatively few PFAM-A domains met the restrictive criterion of being present in all members of one of the groups (i.e. sea anemones or corals; complex corals or robust corals) but being absent from all members of the other group(s), comparative analyses revealed that 161 and 62 domains differed significantly in copy number between corals and anemones, and between complex and robust corals, respectively (see “Methods”; Additional file 2: Table S15 and S16; Additional file 3: Figure S6 and S7). Some differences between corals and sea anemones in domain counts are likely to be associated with calcification in the former—for example, the EGF_CA (calcium-binding EGF domain) is greatly expanded in all corals—whereas other differences in domain or gene distributions may be associated with the symbiotic lifestyle. The fact that Acropora is particularly enriched with respect to glycosyl transferase domains (Glyco_trans_1_4, Glycos_trans_1) has previously been documented ; it is now clear that this is a general feature of corals. However, using the size of gene classes alone as a criterion of difference may also be inappropriate in some cases, as the depth of the coral/anemone divergence suggests that some similarities may be consequences of convergent evolution rather than conservation of function.
The small heatshock protein (HSP20) family provides examples of uneven expansions not only between the complex and robust coral suborders, but also between representatives of the suborders. For example, despite similar numbers of HSP90 and HSP70 loci being present in all of the coral species studied, numbers of HSP20/α-crystallin genes varied more than twofold (Additional file 2: Table S17); the Porites (C) and Goniastrea (R) genomes encode 17 and 18 HSP20s respectively, whereas numbers were much smaller in A. digitifera (C) (9), A. millepora (C) (6), Fungia (R) (7), and Galaxea (C)(7). The same variability appears to hold for sea anemones; Nematostella encodes 18 HSP20s, whereas only five genes were identified in the Aiptasia gene set. Branching patterns observed in phylogenetic analyses (Additional file 3: Figure S8; Additional file 5) are consistent with the HSP20 sequences having undergone independent expansions in the range of anthozoans studied, and in many cases, the HSP20 paralogs were tightly linked. For example, many (14 of 17) of the Porites (C) HSP20 sequences fell into two major clades in phylogenetic analyses. Eight genes comprising one of these clades were on a single scaffold (Sc0000065) (Additional file 3: Figure S8); likewise, nine sequences comprising the major clade of Goniastrea (R) HSP20 sequences were on Sc0000418. Tight linkage of loci was also observed in corals with smaller numbers of HSP20 genes. For example, four of the nine A. digitifera (C) HSP20 genes were on a single scaffold (Additional file 2: Table S17); interestingly, transcription of the assumed orthologs of each of these genes was strongly upregulated in A. millepora (C) under CO2 stress . Whilst data are presently available for relatively few species, an intriguing correlation can be seen between stress tolerance and numbers of HSP20 loci across the range of species studied here; those coral species containing greater numbers of HSP20 loci are substantially more stress tolerant than those with smaller numbers. For example, increases in terms of both colony abundance and spatial coverage following bleaching have been documented for Porites lutea (C) , and Goniastrea fascicularis (R) is one of the most stress-tolerant of Indo-Pacific corals. The same pattern holds for the two sea anemones for which whole genome data are available; by contrast with Aiptasia, Nematostella is remarkably stress tolerant, coping with wide ranges of both salinity (8.96 to 51.54 PSU) and water temperature (− 1 °C to 28 °C) (summarised in ). The apparent correlation between numbers of HSP20 loci in anthozoan species and stress tolerance deserves further exploration.
Tight linkage of paralogs, as observed in the case of HSP20 loci, appears to be a general characteristic of coral genomes—for example, in the case of the secreted and membrane associated type of carbonic anhydrase (CA) whose expansion has been associated with calcification . All of the nine sequences of this type present in Porites (C) are located in a region of approximately 150 kb in the genome (Additional file 3: Figure S9). Tight linkage has also been observed in the case of independently duplicated homeobox genes (Nk2, Dmbx1 and Msx) in A. millepora (C) .
Robust corals have a fungal-like histidine biosynthetic pathway that is absent from complex corals and sea anemones
Genes involved in histidine biosynthesis pathway in cnidarians
KEGG K Identifier
Activity (Fig. 5)
Step catalysed (Fig. 5)
SwissProt Accession ID
2, 3, 9 and 10
Histidine biosynthesis trifunctional protein
1-(5-phosphoribosyl)-5-[(5-phosphoribosylamino)methylideneamino] imidazole-4-carboxamide isomerase
Imidazole glycerol phosphate synthase hisHF
2, 3, 9 and 10
Histidine biosynthesis trifunctional protein
1-(5-phosphoribosyl)-5-[(5-phosphoribosylamino)methylideneamino] imidazole-4-carboxamide isomerase
Imidazole glycerol phosphate synthase hisHF
Imidazole glycerol phosphate synthase hisHF
Searching the genome assemblies allowed the identification of syntenic blocks of genes surrounding five of the histidine biosynthesis genes in Fungia (R) and Goniastrea (R) (Additional file 2: Table S19), all but one of which (K04486) were not found in complex corals. The corresponding syntenic blocks of genes (but lacking the histidine biosynthesis gene) were identified in at least one complex coral (Additional file 2: Table S19), and some were also found in sea anemones. The genes neighbouring K00765, K01663, K01814, and K14152 in the robust corals can be matched as direct syntenic orthologs in complex coral genomes. K04486 is the only histidine pathway gene which is also found in complex corals, and in this case, synteny around the gene is shared between the robust and complex corals.
To verify that complex corals have actually lost the histidine pathway genes, sequence similarity searches were carried out on the regions between YIPE1 and SYFA (which flank the K14152 genes in robust corals; Fig. 7) in complex corals and sea anemones. These searches failed to detect any sequences homologous to K14152 (or any other gene) in that region in any complex coral or sea anemone, confirming that the loss(es) of K14152 in complex corals and sea anemones did not occur recently.
Although only two robust corals were included in the comparative survey described here, analyses of published and publicly available data support the hypothesis that histidine biosynthesis is a general property of robust corals and is likely to be an ancestral trait in the Scleractinia. Based on the genome annotation of Voolstra et al. , the complete pathway is also present in the robust coral Stylophora pistillata (Additional file 2: Table S22) . Whilst it is not possible for us to directly demonstrate that the histidine biosynthesis pathway of robust corals is functional, a preliminary analysis of the publicly available transcriptome data for corals [9, 62, 63] provides further support for the hypothesis that a complete histidine pathway is present and functional in robust corals but not in complex corals or sea anemones (Additional file 2: Table S23). Moreover, the proteins that constitute the putative histidine biosynthetic pathway in Fungia and Goniastrea have all of the residues associated with function in the corresponding SwissProt reference sequences (Additional file 2: Table S24; Additional file 8). Draft genome assemblies for two representatives of the Corallimorpharia, sister order to the Scleractinia [10, 64], were recently reported , and although neither corallimorpharian genome encodes a complete histidine biosynthesis pathway, all of the necessary genes are present in one or other (or both) species (Additional file 2: Table S22), suggesting that the complete pathway is ancestral. Thus, a yeast-like histidine biosynthesis pathway is likely to be ancestral in the Scleractinia and ubiquitous across the Robusta—we hypothesise that it has been lost in complex corals and sea anemones rather than gained via lateral transfer. Histidine biosynthesis is an energetically demanding process, and there are numerous examples of genes being lost when their function is redundant, as utilisation of energy to produce non-functional proteins will presumably be selected against. Thus, it was advantageous for complex corals and sea anemones as well as “higher” animals (members of the Bilateria) to lose the pathway.
Significance of these findings for coral research
The results presented here demonstrate how comparative genomics can inform understanding of biological characteristics of corals, including stress tolerance and host-symbiont interactions. Although so far data are only available for a limited number of species, the observation that anthozoans showing higher stress tolerances have larger numbers of HSP20 loci than do their more stress-sensitive counterparts is intriguing and worthy of further exploration. The fact that robust corals have a complete, and therefore presumably functional, histidine biosynthetic pathway means that they are not dependent on the resident photosymbiont (or heterotrophy) for supply of this “essential” amino acid, whereas this is the case with complex corals.
Because both robust and complex clades contain numerous symbiotic genera, only further research will clarify whether the pattern of presence/absence of the histidine biosynthesis pathway reported here is universal, and if it is, how it can be explained.
The most significant implication of these comparative analyses is that, uniquely amongst animals, robust corals are capable of de novo histidine biosynthesis. Previously, the only known difference between corals with respect to biosynthetic capacity was the lack of the enzyme cystathionine β-synthase (suggesting a requirement for cysteine) in Acropora spp. (C) but not in other corals . Whilst these metabolic differences may play roles in the selection of compatible Symbiodinium strains, experimental support for this idea is presently lacking. Indeed, the robust corals studied here host strains of clade C and clade D Symbiodinium (Additional file 1: Table S1), as do many complex corals, including Acropora and Galaxea. Note, however, that enormous variation exists within the clades (particularly clade C), and few genome data are available, so the possibility of metabolic influences on strain selection cannot be dismissed.
Based on comparative analyses of protein families that are represented in both S. pistillata (R) and A. digitifera (C), it has been suggested that many genes have been independently duplicated in the two corals . However, the tandem organisation of several expanded gene families reported here suggests that concerted evolution might be at least partly responsible for the patterns observed when the corresponding sequences are subjected to phylogenetic analysis.
Both the amino acid and nucleotide-based analyses strongly support the separation of the robust and complex clades and the implied relationships amongst complex corals are consistent with recent phylogenetic studies [5, 66, 67]. The branch length leading to A. digitifera (C) (particularly evident in the nt-based tree) is surprising given that the fossil record implies a relatively recent origin of the genus (~ 55 MYA) [68, 69]. Nevertheless, the phylogenetic and synteny analyses are consistent with corals forming a tight grouping by comparison with sea anemones. However, sea anemones are an ancient and highly diverse lineage, clearly represented in the Cambrian fossil record [70, 71], within which Nematostella and Aiptasia are only distant relatives [72–74], so extensive divergence at the genome level should perhaps have been anticipated. Genome sequence data for a more representative range of sea anemones are required in order to determine whether extensive genome rearrangements are the norm, or whether Aiptasia and Nematostella are truly atypical in this respect.
Sample collection and sequencing
Single colonies of Galaxea fascicularis, Goniastrea aspera, and Fungia sp. were collected near Orpheus Island, Far North Queensland, Australia, during November 2012. They were subsequently maintained in an aquarium at the Orpheus Island Research Station of James Cook University for a few days until they spawned and (Symbiodinium-free) sperm could be collected. Genomic DNA was isolated at James Cook University using the phenol method in April–May 2014. Illumina paired-end and mate-pair libraries with insert sizes in the range of 250 bp to 15 kb were prepared according to the manufacturer’s protocol at the Australian Genome Research Facility (AGRF), Melbourne, Australia. Sequencing was performed on an Illumina HiSeq2500. Table S2 in Additional file 2 summarises the library types and sizes on which the assemblies were based. In total, 81.9 Gb (152× coverage), 171.6 Gb (245× coverage), and 190.6 Gb (222× coverage) of sequence data were generated for Galaxea fascicularis, Fungia sp., and Goniastrea aspera respectively. To facilitate genome annotation, RNA samples from Galaxea fascicularis, Fungia sp., and Goniastrea sp. were collected from adult coral tissues from Orpheus Island and processed and sequenced at AGRF, Melbourne.
FastQC  was applied for quality checking of every library. In addition, paired-end read quality, genome size, and genomic features were assessed using sga-preqc package . Adaptors and low-quality bases were trimmed using libngs  with a minimum quality of 20 and a minimum read size of 130 bp. Only reads with sufficient quality from both pairs were retained. The genome assemblies were performed using ALLPATHS-LG  v52188 in haplodify mode. Gapcloser v1.12-r6  was employed afterwards for additional scaffolding. Randomly selected de novo assembled transcripts were mapped to these de novo assemblies, as a result of which many were identified as duplicated copies (data not shown). This suggested that both haplotypes were present in part of the assembly despite the effort of the assembler to haplodify the sequences. Therefore, we used Haplomerger  to merge the two parental alleles into a single reference sequence. Finally, a blast approach was conducted to remove small redundant scaffolds less than 1 kb in length.
Mitochondrial genome identification
Mitochondrial genome sequences for 17 robust corals and 39 complex corals were obtained from NCBI nucleotide database (Additional file 2: Table S4). To identify assembled mitochondrial scaffolds, coral genome sequences used in the present study were blasted against mitochondrial sequences from a close relative (Additional file 2: Table S4).
Raw RNA-seq reads were trimmed by the same methods as DNA reads. Trinity c2.0.6  was then applied for de novo assembly (TDN) and genome-guided assembly (TGG). Default parameters were used except for jaccard_clip and strand-specific library type options. Similar TDN transcripts were merged using cd-hit [82, 83] with 90% identity threshold. Because RNA samples from adult tissues are a mixture of coral and Symbiodinium RNA molecules, we applied PSyTrans , which is based on support vector machine classification, to separate host (coral) and symbiont (Symbiodinium) transcripts from TDN transcripts. The GC content for the whole transcriptome before and after separation is shown in Additional file 3: Figure S1.
The gene models were generated by ab initio prediction based on carefully selected training genes and external evidence.
Firstly, PASA  was applied to assemble TDN and TGG transcripts to the genome, followed by transdecoder  to produce a set of likely ORFs. Only complete ORFs containing both plausible start (ATG) and stop codons were selected. This resulted in 18,723 complete ORFs for Galaxea. For Fungia and Goniastrea, since RNA samples were collected from closely related species, this step produced many fewer complete ORFs adequate for subsequent analyses. To overcome this problem, we chose to run MAKER2  using TDN and TGG transcripts as transcript evidence and proteins from the uniref90 database  for protein alignment. This yielded 32,208 and 39,568 complete ORFs for Fungia and Goniastrea respectively.
Secondly, these complete ORFs were blasted against the SwissProt database using E-value threshold 1E−20. We retained full-length complementary DNAs (fl-cDNA) whose target coverages and query coverages are greater than 80% and 70%, respectively. These fl-cDNAs were subjected to the following multiple filtering steps: (i) multiple exon transcripts coding for peptides containing at least 100 amino acids were required and transcripts overlapping the same genomic loci were removed; (ii) redundant fl-cDNAs were merged using cdhit with 80% similarity threshold on translated proteins, and the longer fl-cDNAs were retained; (iii) putative transposable elements were excluded based on transposonPSI  and hhblits  searches to transposon databases; (iv) we employed the perl script prepare_golden_genes_for_predictors.pl from JAMg  to enhance the accuracy of PASA predictions which made use of a splice aware aligner (exonerate) and output refined gene models. We randomly selected ~ 1000 refined gene models as a training dataset, and the rest were set aside for testing purposes.
Finally, the MAKER2  annotation pipeline was run for ab initio prediction. The training gene set was used to train AUGUSTUS  and SNAP . The resulting parameters were employed by corresponding programs from MAKER. The combined TDN and TGG transcripts were provided as EST evidence, and the proteins downloaded from uniref90 database were taken as external evidence for protein alignment. Finally, putative transposons in the gene model were removed as described above.
Repetitive elements were detected from two analyses for all the genomes compared in this study. Firstly, a de novo repeat library was generated with Repeat-Modeller (Version 1.0.8)  with default parameters. This library was combined with RepBase databases  and used as input for RepeatMasker  to identify repeat categories and locations. A summary of repeat components is presented in Additional file 2: Table S5.
Homologue search against public protein databases
Throughout the analyses, we performed similarity searches against three public protein databases using BLASTP with an E-value cut-off of 1E−05. The annotated coral proteins were used as query, and the curated database proteins were used as target. These databases are as follows: (1) The high-quality curated Universal Protein Resource (UniProt) SwissProt database  was our major resource to indicate gene functions and queried first. We defined the target (query) coverage as the percentage of the target (query) length in the alignment. Gene biological descriptions were assigned by their best E-value hit. (2) The UniProt TreMBL protein database  was queried for proteins that did not have significant hits from SwissProt. (3) The NCBI non-redundant protein database (NR) was downloaded from the NCBI ftp site . The top 10 and 100 hits were retained from UniProt and NR database queries, respectively.
Gene space completeness assessment
CEGMA software version 2.5  was conducted to assess the completeness of genome assembly and annotated gene models. The download included the reference dataset of 248 ultra-conserved core eukaryotic genes (CEGs). The program was run with default parameters, which define the presence of a CEG in a query sequence if the outcome from the HMM search exceeds a pre-computed minimum alignment score, and the alignment covers over 70% of a CEG.
BUSCO software version 1.1  was applied to further assess the completeness of genome assembly and annotated gene models. The program was run with default parameters and the eukaryotic gene set was chosen as reference dataset.
Functional annotation was performed by homologue searching of the protein domain PFAM-A database  and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database . HMMER (hmmer3)  was used to perform alignments to Pfam-A hmm profile, and protein domains with E-value and c-Evalue lower than 1E−05 were selected. KEGG K number (KEGG orthology KO identifier) assignment followed the algorithm described by Mao et al. , which selected the first UniProt (SwissProt, if not, TreMBL) hit that had a corresponding K number with E-value lower than 1E−05 and fewer than five lower E-value hits. ID mapping file, idmapping.dat.gz, was downloaded from UniProt ftp site . An in-house-developed script (kindly provided by Francesco Rubino (email@example.com University of Queensland Australia) was used to convert UniProt hits to K numbers.
Genome phylogeny construction
The first step in this process was the identification of orthologous groups (OGs) from the eight cnidarian species sampled in the present study using OrthoFinder (version 0.2.5)  with default parameters. Single-copy orthologous genes were identified from one-to-one relationship OGs, and the results filtered by requiring the same SwissProt gene name match with target coverage greater than 60%. Genes whose predicted protein sequence, when translated from the gene model GFF3 files, did not agree with the downloaded protein sequence were also excluded. This resulted in 687 high-quality single-copy ortholog groups.
The alignments to be used for phylogenetic analyses were prepared as follows. Protein sequence alignments were generated for the single-copy one-to-one orthologs described above using MAFFT v7  with the E-INS-i strategy, MLOSUM62 matrix, and 1000 maxiterate. The corresponding protein coding sequence (CDS) alignments were derived from these protein alignments using functions implemented in PyCogent . The former was used for amino acid (AA) model-based phylogeny construction, and the latter was used for nucleotide (NT) model based phylogeny construction.
Amino acid models of sequence substitution are conventionally employed for phylogenetic analyses of highly diverged lineages to reduce the potential impact of saturation of substitutions—the point past which any additional changes in sequence cannot be identified. In general, substitution models with a small number of character states will saturate earlier than models with larger numbers of states. For this reason, AA models typically have been preferred over NT models . Unfortunately, it has been shown that biological sequence evolution violates fundamental assumptions of AA substitution models . As a consequence, the validity of inferences made using AA models alone is suspect and a substitution model that operates on the DNA sequence is required. Accordingly, we employed both a conventional AA model-based approach and separately one using a nucleotide substitution model . These are described in more detail below.
The continuous-time general Markov nucleotide (GN) substitution model  was employed for the NT-based phylogenetic analysis. This is a non-reversible and non-stationary model, properties that have been demonstrated to improve robustness of phylogenetic inference [30, 33]. Using GN allows drawing on mathematical results concerning model identifiability [30, 105] to establish that sufficient phylogenetic signal exists (i.e. that the sequences are not saturated) for robust inferences to be drawn. These conditions are of Diagonal Largest in Column [DLC, 105] and the existence of a unique mapping between continuous and discrete time Markov processes .
Important drawbacks in using GN arise from its large number of parameters. First, the computational time required for model fitting is considerably greater than that for standard models. Second, when a time-heterogenous substitution model is desirable, there is also a risk of over fitting . The set of possible models ranged from a globally time-homogeneous model (a single rate matrix) to the maximally time-heterogenous model (a separate rate matrix per branch). For a single tree with 5, 6, or 7 taxa, the total number of possible models is 877, 21,147, and 678,570 respectively. To eliminate the issue of over fitting, we employed a model selection approach that uses the corrected Aikake Information Criteria (AICc) to identify the optimal model from the complete solution space. Because of computational limitations, we were only able to assess the complete solution space for a tree with five taxa, i.e. the optimal model was chosen from the 877 possible models.
The species phylogenetic tree for eight taxa was constructed using maximum-likelihood estimation based on GN  as implemented in PyCogent . All possible tree topologies were evaluated for the five taxon cases (15 possible trees) outlined below. Each CDS alignment was split into three separate alignments, one for each codon position. For a given phylogenetic tree, a separate optimal model (described above) was identified for each codon position alignment and the log-likelihood for the tree for the CDS was the sum of the log-likelihoods from the three optimal models for the codon position alignments. (Only alignments that passed the model identifiability tests for all codon positions for all tree topologies were used.) The tree with the maximum-likelihood was chosen as the “best” tree for each CDS alignment and the likelihood weights method  was employed to determine the consensus tree and quantify support for different branching orders.
A sequential approach was adopted to resolve the branching order of the coral species. Firstly, a four taxa phylogenetic tree was generated for Nematostella, Galaxea (C), A. digitifera (C), and Fungia (R). Nematostella was used as an outgroup to indicate the root position for corals. Secondly, separate five taxa analyses were conducted to infer the position of Goniastrea (R) and Porites (C), relative to the four species in the tree. The results clearly placed Goniastrea (R) with the robust coral Fungia, and Porites (C) with complex corals (Additional file 3: Figure S3). This outcome allowed us to combine the two topologies unambiguously. Finally, Aiptasia was added to the sea anemone group with Nematostella and A. millepora (C) was clustered with A. digitifera (C), to complete the eight taxon phylogenetic tree, from which the branch length from GN was estimated based on the method described in Kaehler et al. .
In addition, a conventional AA substitution model-based phylogenetic analysis was undertaken by maximum likelihood using IQ-TREE 1.5.5 . The protein alignments from one-to-one orthologs were concatenated into a supermatrix and partitioned by genes. The best partitioning scheme and evolutionary model for each partition was assessed by ModelFinder . By default, ModelFinder chooses the model that minimises the Bayesian information criterion (BIC) score. To assess branch support, the ultrafast bootstrap approximation (UFboot) was used, with 1000 replicates .
Conserved syntenic blocks between species were identified using the MCScanX  package based on collinearity of orthologous genes. The first step was blastall through blastp to identify homologous genes. The second step made use of gene location information to generate syntenic blocks containing a minimum of three collinear orthologous genes separated by no more than 10 non-orthologous genes. Circos v0.68  was employed to draw syntenic blocks amongst selected species.
HOX gene cluster analyses
We identified HOX genes as homeobox containing genes that matched best to a HOX gene in the SwissProt database. Putative HOX cluster genes were defined as consecutive HOX genes, of which two clusters were discovered in most of the species in the present study. Examining neighbouring upstream and downstream linked genes, we defined the HOX cluster H1 as multiple HOX genes linked with Evx, Mnx, and Rough genes, and the other cluster as H2. HOX gene sequences from Baumgarten et al.  were used as a reference to classify HOX genes. All HOX protein sequences were aligned with MAFFT. The resulting alignment was trimmed to a single 56 amino acid alignable region and used for maximum likelihood phylogenetic analyses using IQ-TREE. Tree visualisation was performed using the R package, ggtree .
A few HOX and HOX-related genes were missing from the gene model in some species. They were manually corrected using Blast-based methods, as follows. (1) The lengths of the protein coding regions annotated as Rough sequences from A. millepora and Fungia were approximately the combined size of Mnx1 and Rough proteins in other species. We confirmed that they were erroneous mergers and accordingly should be separated into Mnx1 and Rough genes. (2) The Rough gene is not present in the A. digitifera gene model(s). We used the A. millepora Rough gene coding sequence as reference and identified one Rough exon at the corresponding location (99% nucleic acid identity with the A. millepora sequence). The genomic position where the second Rough exon is expected is located in a sequencing gap in the A. digitifera genomic scaffold. This suggests that the problem is due to incomplete assembly and that a Rough gene is present and linked to Mnx1 in A. digitifera. (3) The Mnx1 gene is not annotated in Goniastrea. From the HOX cluster H1 gene arrangement, we isolated the genome sequences at the expected Mnx1 location and identified a Mnx1 gene using the Fungia Mnx1 sequence as reference. (4) The A. digitifera HOX2A gene (XP_015763498.1) was identified using the A. millepora sequence as reference. The first exon and 54 nt of the second exon of the HOX2B gene are also present on the same scaffold as the HOX2A gene in A. digitifera; the rest of the predicted sequence is missing due to the presence of a sequencing gap in the scaffold. Thus, A. digitifera is likely to have linked HOX2A and HOX2B genes, as is the case in A. millepora.
Protein family analyses
Fisher-exact tests were carried out to find expanded PFAM-A domains in a lineage . Tests were performed between coral (robust and complex corals) and anemones (Nematostella and Aiptasia); and between complex and robust corals. In each test, the background and specific domain content were calculated as the total number of genes that were identified as PFAM-A domain-containing and genes with a specific domain in the corresponding lineage respectively. The resulting two-sided p values were subjected to Benjamini-Hochberg multiple test correction (FDR)  and a FDR threshold of 0.01 was chosen for significantly enriched domains.
Gene phylogenetic tree construction
For the phylogenetic analyses of other genes presented in the present study, the protein sequences were aligned using MAFFT, poorly aligned regions (< 20% alignable sequences) were trimmed, and the phylogeny was then constructed using IQ-TREE. ModelFinder was applied to find the best fit model and 1000 UFBoot replicates were used to generate node support values.
Conserved domain and functional residue search
To identify functional residues, we searched the Conserved Domain Database (CDD) using the NCBI Batch Web CD-Search Tool [114, 115]. The query sequences included putative histidine biosynthesis proteins from Fungia, Goniastrea, and their best matching SwissProt proteins. In cases where the matching protein was from a fungal species, the homologous protein from the model organism Saccharomyces cerevisiae was used, on the basis that structure/function relationships have been most extensively studied in this species. The CD search outputs enabled identification of domains shared between reference and coral proteins, and the resulting alignments were manually inspected for the presence of functional residues in the robust coral proteins. In addition, two proteins (Additional file 2: Table S24 (b)) that have functional residue information in the UniProt database  were aligned and compared to their corresponding robust coral proteins (Additional file 8).
We acknowledge the staff of the Orpheus Island Research Station for their assistance in field work and the contribution of the Great Barrier Reef Project consortium (https://data.bioplatforms.com/organization/about/bpa-great-barrier-reef) in the generation of data used in this publication. We also thank Patrick Schaeffer and Lionel Hebbard for commenting on an early version of the manuscript.
Photo acknowledgements for Fig. 1:
Panels b1–b3 Australian Institute of Marine Science, (2017). AIMS Coral Fact Sheets -Goniastrea aspera. Viewed 23 November, 2017 http://coral.aims.gov.au/factsheet.jsp?speciesCode=0187.
Panel c1 Courtesy Patrick Libourel http://liboupat2.free.fr/aquafaun/tropiq/Cnidaire/fungia.htm.
Panels c2–c3 Australian Institute of Marine Science, (2017). AIMS Coral Fact Sheets -Fungia fungites. Viewed 23 November, 2017
Panels d1–d3 Australian Institute of Marine Science, (2017). AIMS Coral Fact Sheets-Galaxea fascicularis. Viewed 23 November, 2017 http://coral.aims.gov.au/factsheet.jsp?speciesCode=0185.
Panel e Courtesy Andrew Baird.
Panel f “Acropora digitifera” Courtesy MDC Seamarc Maldives Licensed under Creative Commons International Attributions 4.0.
Panel g “Porites lutea”, Australian Institute of Marine Science, Photograph by Dr. Paul Muir (2014). Licenced under Creative Commons Attributions 3.0 Australia. Available at http://eatlas.org.au/media/1626.
Panel h “Aiptasia pallida” Courtesy Ricardo González-Muñoz, Nuno Simões, José Luis Tello-Musi, Estefanía Rodríguez under Creative Commons Attributions 3.0.
Panel i Courtesy Chiara Sinigaglia.
The authors gratefully acknowledge the support for the Great Barrier Reef Project enabled by funding from Bioplatforms Australia through the Australian Government National Collaborative Research Infrastructure Strategy (NCRIS), Rio Tinto, a private family Foundation and the Great Barrier Reef Foundation. The work was also supported in part by of the Australian Research Council through Grant CE140100020 to DJM and to SF via the ARC Centre of Excellence for Coral Reef Studies at James Cook University.
Availability of data and materials
The sequencing datasets (genome and transcriptome sequencing data) generated by the present study are publicly available at the European Nucleotide Archive (ENA). The accession numbers are PRJEB23333, PRJEB23312, and PRJEB23371 for Galaxea, Fungia, and Goniastrea respectively [116–118]. Genome assembly and annotation are publicly accessible through Reefgenomics data repository [119–121]. Functional annotations supporting the conclusions of this article are included within the article and its additional files. Protein sequences used for gene phylogeny construction are included in the additional files.
Additional whole genome data used for comparative analyses are available from the following resources. Acropora digitifera data were obtained from the NCBI ftp site  with the assembly accession GCF_000222465.1 and annotation release ID 100. The Acropora millepora genome was assembled and annotated by author SF. Genome-related data for this species have been deposited to NCBI under the accession number PRJNA473876 . Porites lutea genome data are publicly available via the Reefgenomics data repository . Nematostella vectensis genome data were downloaded from Ensembl genome metazoan release 29 . Aiptasia genome v1.0 data were obtained from the Reefgenomics data repository .
This project was instigated and initially supervised by DM and SF. Since SF’s untimely death in December 2017, the project has been coordinated by HY and DM. SS collected the corals and prepared the DNA. HY, SF, IC, and WW performed the bioinformatics analyses. HY, DM, IC, EB, and DH performed the biological data analyses. YT conducted the species phylogeny analyses, supervised by GH. GH provided advice and performed the species phylogeny analyses. DM, EB, HY, and GH wrote the manuscript. DH, IC, and WW assisted in the manuscript production. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Stanley GD, Fautin DG. The origins of modern corals. Science. 2001;291:1913–4.View ArticlePubMedPubMed CentralGoogle Scholar
- Stanley GD. The evolution of modern corals and their early history. Earth Sci Rev. 2003;60:195–225.View ArticleGoogle Scholar
- Stolarski J, Kitahara MV, Miller DJ, Cairns SD, Mazur M, Meibom A. The ancient evolutionary origins of Scleractinia revealed by azooxanthellate corals. BMC Evol Biol. 2011;11:316.PubMed CentralView ArticlePubMedGoogle Scholar
- Frankowiak K, Wang XT, Sigman DM, Gothmann AM, Kitahara MV, Mazur M, et al. Photosymbiosis and the expansion of shallow-water corals. Sci Adv. 2016;2:e1601122.PubMed CentralView ArticlePubMedGoogle Scholar
- Kitahara MV, Cairns SD, Stolarski J, Blair D, Miller DJ. A comprehensive phylogenetic analysis of the Scleractinia (Cnidaria, Anthozoa) based on mitochondrial CO1 sequence data. PLoS One. 2010;5:e11490.PubMed CentralView ArticlePubMedGoogle Scholar
- Huang D. Threatened reef corals of the world. PLoS One. 2012;7:e34459.PubMed CentralView ArticlePubMedGoogle Scholar
- Romano SL, Palumbi SR. Evolution of scleractinian corals inferred from molecular systematics. Science. 1996;271:640–2.View ArticleGoogle Scholar
- Romano SL, Cairns SD. Molecular phylogenetic hypotheses for the evolution of scleractinian corals. Bull Mar Sci. 2000;67:1043–68.Google Scholar
- Bhattacharya D, Agrawal S, Aranda M, Baumgarten S, Belcaid M, Drake JL, et al. Comparative genomics explains the evolutionary success of reef-forming corals. elife. 2016;5:e13288.PubMed CentralView ArticlePubMedGoogle Scholar
- Lin MF, Chou WH, Kitahara MV, Chen CL, Miller DJ, Forêt S. Corallimorpharians are not “naked corals”: insights into relationships between Scleractinia and Corallimorpharia from phylogenomic analyses. PeerJ. 2016;4:e2463.PubMed CentralView ArticlePubMedGoogle Scholar
- Kitahara MV, Lin MF, Forêt S, Huttley G, Miller DJ, Chen CA. The “naked coral” hypothesis revisited–evidence for and against scleractinian monophyly. PLoS One. 2014;9:e94774.PubMed CentralView ArticlePubMedGoogle Scholar
- Okubo N, Mezaki T, Nozawa Y, Nakano Y, Lien YT, Fukami H, et al. Comparative embryology of eleven species of stony corals (Scleractinia). PLoS One. 2013;8:e84115.PubMed CentralView ArticlePubMedGoogle Scholar
- Hayashibara T, Ohike S, Kakinuma Y. Embryonic and larval development and planula metamorphosis of four gamete-spawning Acropora (Anthozoa, Scleractinia). In proceedings of the 8th international coral reef symposium. Panama. 1997;2:1231–6.Google Scholar
- Miller DJ, Ball EE. The coral Acropora: what it can contribute to our knowledge of metazoan evolution and the evolution of developmental processes. BioEssays. 2000;22:291–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Heyward AJ, Yamazato K, Yeemin T, Minei M. Sexual reproduction of corals in Okinawa. Galaxea. 1987;6:331–43.Google Scholar
- Okubo N, Hayward DC, Forêt S, Ball EE. A comparative view of early development in the corals Favia lizardensis, Ctenactis echinata, and Acropora millepora-morphology, transcriptome, and developmental gene expression. BMC Evol Biol. 2016;16:48.PubMed CentralView ArticlePubMedGoogle Scholar
- Shinzato C, Shoguchi E, Kawashima T, Hamada M, Hisata K, Tanaka M, et al. Using the Acropora digitifera genome to understand coral responses to environmental change. Nature. 2011;476:320–3.View ArticleGoogle Scholar
- Putnam NH, Srivastava M, Hellsten U, Dirks B, Chapman J, Salamov A, et al. Sea anemone genome reveals ancestral eumetazoan gene repertoire and genomic organization. Science. 2007;317:86–94.View ArticlePubMedPubMed CentralGoogle Scholar
- Baumgarten S, Simakov O, Esherick LY, Liew YJ, Lehnert EM, Michell CT, et al. The genome of Aiptasia, a sea anemone model for coral symbiosis. Proc Natl Acad Sci. 2015;112:11893–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Voolstra CR, Li Y, Liew YJ, Baumgarten S, Zoccola D, Flot JF, et al. Comparative analysis of the genomes of Stylophora pistillata and Acropora digitifera provides evidence for extensive differences between species of corals. Sci Reports. 2017;7:17583.View ArticleGoogle Scholar
- Voolstra CR, Miller DJ, Ragan MA, Hoffmann A, Hoegh-Guldberg O, Bourne D, et al. The ReFuGe 2020 consortium—using “omics” approaches to explore the adaptability and resilience of coral holobionts to environmental change. Front Mar Sci. 2015;2:68.Google Scholar
- Brown BE, Dunne RP, Phongsuwan N, Patchim L, Hawkridge JM. The reef coral Goniastrea aspera: a ‘winner’becomes a ‘loser’during a severe bleaching event in Thailand. Coral Reefs. 2014;33:395–401.View ArticleGoogle Scholar
- Veron JEN. Corals of Australia and the Indo-Pacific. North Ryde: Angus and Robertson; 1986.Google Scholar
- McClanahan TR, Baird AH, Marshall PA, Toscano MA. Comparing bleaching and mortality responses of hard corals between southern Kenya and the great barrier reef. Mar Poll Bull. 2004;48:327–35.View ArticleGoogle Scholar
- Wijgerde T, Diantari R, Lewaru MW, Verreth JA, Osinga R. Extracoelenteric zooplankton feeding is a key mechanism of nutrient acquisition for the scleractinian coral Galaxea fascicularis. J Exp Biol. 2011;214:3351–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Parra G, Bradnam K, Ning Z, Keane T, Korf I. Assessing the gene space in draft genomes. Nucleic Acids Res. 2009;37:289–97.View ArticlePubMedPubMed CentralGoogle Scholar
- Simão FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31:3210–2.View ArticlePubMedPubMed CentralGoogle Scholar
- Finn RD, Coggill P, Eberhardt RY, Eddy SR, Mistry J, Mitchell AL, et al. The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res. 2016;44(D1):D279–85.View ArticleGoogle Scholar
- Denton JF, Lugo-Martinez J, Tucker AE, Schrider DR, Warren WC, Hahn MW. Extensive error in the number of genes inferred from draft genome assemblies. PLoS Comp Biol. 2014;10:e1003998.View ArticleGoogle Scholar
- Kaehler BD, Yap VB, Zhang R, Huttley GA. Genetic distance for a general non-stationary Markov substitution process. Syst Biol. 2015;64:281–93.View ArticlePubMedPubMed CentralGoogle Scholar
- Kosiol C, Goldman N. Markovian and non-Markovian protein sequence evolution: aggregated Markov process models. J Mol Biol. 2011;411:910–23.PubMed CentralView ArticlePubMedGoogle Scholar
- Philippe H, Brinkmann H, Lavrov DV, Littlewood DT, Manuel M, Wörheide G, et al. Resolving difficult phylogenetic questions: why more sequences are not enough. PLoS Biol. 2011;9:e1000602.PubMed CentralView ArticlePubMedGoogle Scholar
- Jayaswal,V, Jermiin LS, Robinson J. Estimation of phylogeny using a general Markov model. Evol Bioinformatics Online. 2007;1:62–80.Google Scholar
- Vera-Ruiz VA, Lau KW, Robinson J, Jermiin LS. Statistical tests to identify appropriate types of nucleotide sequence recoding in molecular phylogenetics. BMC Bioinformatics. 2014;15:S8.PubMed CentralView ArticlePubMedGoogle Scholar
- Nguyen LT, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2014;32:268–74.PubMed CentralView ArticlePubMedGoogle Scholar
- Chernomor O, von Haeseler A, Minh BQ. Terrace aware data structure for phylogenomic inference from supermatrices. Syst Biol. 2016;65:997–1008.PubMed CentralView ArticlePubMedGoogle Scholar
- Simakov O, Marletaz F, Cho S-J, Edsinger-Gonzales E, Havlak P, Hellsten U, et al. Insights into bilaterian evolution from three spiralian genomes. Nature. 2013;493:526–31.View ArticleGoogle Scholar
- Finnerty JR, Pang K, Burton P, Paulson D, Martindale MQ. Origins of bilateral symmetry: Hox and dpp expression in a sea anemone. Science. 2004;304:1335–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Chourrout D, Delsuc F, Chourrout P, Edvardsen RB, Rentzsch F, Renfer E, et al. Minimal ProtoHox cluster inferred from bilaterian and cnidarian Hox complements. Nature. 2006;442:684–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Miller DJ, Miles A. Homeobox genes and the zootype. Nature. 1993;365:215.View ArticlePubMedPubMed CentralGoogle Scholar
- DuBuc TQ, Ryan JF, Shinzato C, Satoh N, Martindale MQ. Coral comparative genomics reveal expanded Hox cluster in the cnidarian–bilaterian ancestor. Int Comp Biol. 2012;52:835–41.View ArticleGoogle Scholar
- DuBuc TQ, Stephenson TB, Rock AQ, Martindale MQ. Hox and Wnt pattern the primary body axis of an anthozoan cnidarian before gastrulation. Nature Comm. 2018;9:2007.View ArticleGoogle Scholar
- Patel NH, Prince VE. Beyond the Hox complex. Genome Biol. 2000;1:reviews1027–1.PubMed CentralView ArticlePubMedGoogle Scholar
- Takatori N, Butts T, Candiani S, Pestarino M, Ferrier DE, Saiga H, et al. Comprehensive survey and classification of homeobox genes in the genome of amphioxus, Branchiostoma floridae. Dev Genes Evol. 2008;218:579–90.View ArticleGoogle Scholar
- Butts T, Holland PW, Ferrier DE. The urbilaterian super-hox cluster. Trends Genet. 2008;24:259–62.View ArticleGoogle Scholar
- Rodríguez E, Barbeitos MS, Brugler MR, Crowley LM, Grajales A, Gusmão L, et al. Hidden among sea anemones: the first comprehensive phylogenetic reconstruction of the order Actiniaria (Cnidaria, Anthozoa, Hexacorallia) reveals a novel group of hexacorals. PLoS One. 2014;9:e96998.PubMed CentralView ArticlePubMedGoogle Scholar
- Kamm K, Schierwater B, Jakob W, Dellaporta SL, Miller DJ. Axial patterning and diversification in the Cnidaria predate the Hox system. Curr Biol. 2006;16:920–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Hayward DC, Catmull J, Reece-Hoyes JS, Berghammer H, Dodd H, Hann SJ, et al. Gene structure and larval expression of cnox-2Am from the coral Acropora millepora. Dev Genes Evol. 2001;211:10–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Brooke NM, Garcia-Fernandez J, Holland PW. The ParaHox gene cluster is an evolutionary sister of the Hox gene cluster. Nature. 1998;392:920–2.View ArticlePubMedPubMed CentralGoogle Scholar
- Garstang M, Ferrier DE. Time is of the essence for ParaHox homeobox gene clustering. BMC Biol. 2013;11:72.PubMed CentralView ArticlePubMedGoogle Scholar
- Hamada M, Shoguchi E, Shinzato C, Kawashima T, Miller DJ, Satoh N. The complex NOD-like receptor repertoire of the coral Acropora digitifera includes novel domain combinations. Mol Biol Evol. 2012;30:167–76.View ArticlePubMedPubMed CentralGoogle Scholar
- Moya A, Huisman L, Forêt S, Gattuso JP, Hayward DC, Ball EE, et al. Rapid acclimation of juvenile corals to CO2-mediated acidification by upregulation of heat shock protein and Bcl-2 genes. Mol Ecol. 2015;24:438–52.View ArticlePubMedPubMed CentralGoogle Scholar
- Loya Y, Sakai K, Yamazato K, Nakano Y, Sambali H, van Woesik R. Coral bleaching: the winners and the losers. Ecol Lett. 2001;4:122–31.View ArticleGoogle Scholar
- Fraune S, Forêt S, Reitzel AM. Using Nematostella vectensis to study the interactions between genome, epigenome, and bacteria in a changing environment. Front Mar Sci. 2016;3:148.View ArticleGoogle Scholar
- Lin MF, Moya A, Ying H, Chen CA, Cooke I, Ball EE, et al. Analyses of corallimorpharian transcriptomes provide new perspectives on the evolution of calcification in the Scleractinia (corals). Genome Biol Evol. 2017;9:150–60.PubMed CentralView ArticlePubMedGoogle Scholar
- Hislop NR, de Jong D, Hayward DC, Ball EE, Miller DJ. Tandem organization of independently duplicated homeobox genes in the basal cnidarian Acropora millepora. Dev Genes Evol. 2005;215:268–73.View ArticlePubMedPubMed CentralGoogle Scholar
- Artamonova II, Mushegian AR. Genome sequence analysis indicates that the model eukaryote Nematostella vectensis harbors bacterial consorts. App Env Microbiol. 2013;79:6868–73.View ArticleGoogle Scholar
- Aranda M, Li Y, Liew YJ, Baumgarten S, Simakov O, Wilson MC, et al. Genomes of coral dinoflagellate symbionts highlight evolutionary adaptations conducive to a symbiotic lifestyle. Sci Reports. 2016;6:39734.View ArticleGoogle Scholar
- Lin S, Cheng S, Song B, Zhong X, Lin X, Li W, et al. The Symbiodinium kawagutii genome illuminates dinoflagellate gene expression and coral symbiosis. Science. 2015;350:691–4.View ArticlePubMedPubMed CentralGoogle Scholar
- Liu H, Stephens TG, González-Pech R, Beltran VH, Lapeyre B, Bongaerts P, et al. Symbiodinium genomes reveal adaptive evolution of functions related to coral-dinoflagellate symbiosis. Communications biology. In Press (accepted 21 June 2018).Google Scholar
- Shoguchi E, Shinzato C, Kawashima T, Gyoja F, Mungpakdee S, Koyanagi R, Takeuchi T, Hisata K, Tanaka M, Fujiwara M, Hamada M. Draft assembly of the Symbiodinium minutum nuclear genome reveals dinoflagellate gene structure. Curr Biol. 2013;23:1399–408.View ArticlePubMedPubMed CentralGoogle Scholar
- Kenkel CD, Bay LK. Novel transcriptome resources for three scleractinian coral species from the indo-Pacific. GigaScience. 2017;6:1–4.PubMed CentralView ArticlePubMedGoogle Scholar
- Kitchen SA, Crowder CM, Poole AZ, Weis VM, Meyer E. De novo assembly and characterization of four anthozoan (phylum Cnidaria) transcriptomes. G3 (Bethesda). 2015;5:2441–52.View ArticleGoogle Scholar
- Wang X, Drillon G, Ryu T, Voolstra CR, Aranda M. Genome-based analyses of six hexacorallian species reject the “naked coral” hypothesis. Genome Biol Evol. 2017;9:2626–34.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang X, Liew YJ, Li Y, Zoccola D, Tambutte S, Aranda M. Draft genomes of the corallimorpharians Amplexidiscus fenestrafer and Discosoma sp. Mol Ecol Res. 2017;17(6):e187–95.View ArticleGoogle Scholar
- Kerr AM. Molecular and morphological supertree of stony corals (Anthozoa: Scleractinia) using matrix representation parsimony. Biol Rev. 2005;80:543–58.View ArticlePubMedPubMed CentralGoogle Scholar
- Fukami H, Chen CA, Budd AF, Collins A, Wallace C, Chuang YY, et al. Mitochondrial and nuclear genes suggest that stony corals are monophyletic but most families of stony corals are not (order Scleractinia, class Anthozoa, phylum Cnidaria). PLoS One. 2008;3:e3222.PubMed CentralView ArticlePubMedGoogle Scholar
- Carbone F, Matteucci R, Pignatti JS, Russo A. Facies analysis and biostratigraphy of the Aradu limestone formation in the Berbera-sheikh area, northwestern Somalia. Geol Romana. 1994;29:213–35.Google Scholar
- Wallace CC. New species and records from the Eocene of England and France support early diversification of the coral genus Acropora. J Paleontol. 2008;82:313–28.View ArticleGoogle Scholar
- Hou XG, Stanley G, Zhao J, Ma XY. Cambrian anemones with preserved soft tissue from the Chengjiang biota, China. Lethaia. 2005;38:193–203.View ArticleGoogle Scholar
- Han J, Kubota S, Uchida HO, Stanley GD Jr, Yao X, Shu D, et al. Tiny Sea anemone from the lower Cambrian of China. PLoS One. 2010;5:e13276.PubMed CentralView ArticlePubMedGoogle Scholar
- Daly M, Chaudhuri A, Gusmão L, Rodriguez E. Phylogenetic relationships among sea anemones (Cnidaria: Anthozoa: Actiniaria). Mol Phyl Evol. 2008;48:292–301.View ArticleGoogle Scholar
- Rodríguez E, Barbeitos M, Daly M, Gusmao LC, Häussermann V. Toward a natural classification: phylogeny of acontiate sea anemones (Cnidaria, Anthozoa, Actiniaria). Cladistics. 2012;28:375–92.View ArticleGoogle Scholar
- Quattrini AM, Faircloth BC, Dueñas LF, Bridge TC, Brugler MR, Calixto-Botía IF, et al. Universal target enrichment baits for anthozoan (Cnidaria) phylogenomics: new approaches to long standing problems. Mol Ecol Res. 2017;18(2):281–95.View ArticleGoogle Scholar
- FastQC. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/. Accessed 1 Sept 2015.
- Simpson JT. Exploring genome characteristics and sequence quality without a reference. Bioinformatics. 2014;30:1228–35.PubMed CentralView ArticlePubMedGoogle Scholar
- libngs. https://github.com/sylvainforet/libngs. Accessed 28 Feb 2015.
- Gnerre S, MacCallum I, Przybylski D, Ribeiro FJ, Burton JN, Walker BJ, et al. High-quality draft assemblies of mammalian genomes from massively parallel sequence data. Proc Natl Acad Sci. 2011;108:1513–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Luo R, Liu B, Xie Y, Li Z, Huang W, Yuan J, et al. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. Gigascience. 2012;1:18.PubMed CentralView ArticlePubMedGoogle Scholar
- Huang S, Chen Z, Huang G, Yu T, Yang P, Li J, et al. HaploMerger: reconstructing allelic relationships for polymorphic diploid genome assemblies. Genome Res. 2012;22:1581–8.PubMed CentralView ArticlePubMedGoogle Scholar
- Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nature Biotech. 2011;29:644–52.View ArticleGoogle Scholar
- Li W, Godzik A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics. 2006;22:1658–9.View ArticleGoogle Scholar
- Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012;28:3150–2.PubMed CentralView ArticlePubMedGoogle Scholar
- PSyTrans. https://github.com/sylvainforet/psytrans. Accessed 5 Sept 2014.
- Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD, Bowden J, et al. De novo transcript sequence reconstruction from RNA-seq using the trinity platform for reference generation and analysis. Nat Protoc. 2013;8:1494–512.View ArticleGoogle Scholar
- TransDecoder. https://github.com/TransDecoder/TransDecoder/wiki. Accessed 1 Sept 2015.
- Holt C, Yandell M. MAKER2: an annotation pipeline and genome-database management tool for second-generation genome projects. BMC Bioinformatics. 2011;12:491.PubMed CentralView ArticlePubMedGoogle Scholar
- UniProt ftp site. ftp://ftp.uniprot.org. Accessed uniref90 8 July 2014, SwissProt and TreMBL January 2016, idmapping.dat.gz 27 July 2017.
- transposonPSI. http://transposonpsi.sourceforge.net. Accessed 1 Sept 2015.
- Remmert M, Biegert A, Hauser A, Söding J. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat Methods. 2012;9:173–5.View ArticleGoogle Scholar
- JAMg. https://github.com/genomecuration/JAMg/. Accessed 1 Sept 2015.
- Stanke M, Keller O, Gunduz I, Hayes A, Waack S, Morgenstern B. AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res. 2006;34(suppl_2):W435–9.PubMed CentralView ArticlePubMedGoogle Scholar
- Korf I. Gene finding in novel genomes. BMC Bioinformatics. 2004;5:59.PubMed CentralView ArticlePubMedGoogle Scholar
- RepeatModeler: Smit, AFA, Hubley, R. RepeatModeler Open-10. 2008–2015. http://www.repeatmasker.org. Accessed 27 Aug 2015.
- RepBase database. http://www.girinst.org/repbase. Accessed 27 Aug 2015.
- RepeatMasker: Smit, AFA, Hubley, R green, P. RepeatMasker Open-4.0. 2013- 2015. http://www.repeatmasker.org. Accessed 27 Aug 2015.
- NCBI non-redundant NR protein database. ftp://ftp.ncbi.nlm.nih.gov/blast/db. Accessed 4 Aug 2016.
- PFAM-A database. ftp://ftp.ebi.ac.uk/pub/databases/Pfam/current_release/Pfam-A.hmm.gz. Accessed 8 July 2014.
- Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genome, pathways, disease and drugs. Nucleic Acids Res. 2017;45(D1):D353–61.View ArticleGoogle Scholar
- Eddy SR. Accelerated profile HMM searches. PLoS Comp Biol. 2011;7:e1002195.View ArticleGoogle Scholar
- Mao X, Cai T, Olyarchuk JG, Wei L. Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary. Bioinformatics. 2005;21:3787–93.View ArticlePubMedPubMed CentralGoogle Scholar
- Emms DM, Kelly S. OrthoFinder: solving fundamental biases in whole genome comparisons dramatically improves orthogroup inference accuracy. Genome Biol. 2015;16:1.View ArticleGoogle Scholar
- Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.PubMed CentralView ArticlePubMedGoogle Scholar
- Knight R, Maxwell P, Birmingham A, Carnes J, Caporaso JG, Easton BC, et al. PyCogent: a toolkit for making sense from sequence. Genome Biol. 2007;8:R171.PubMed CentralView ArticlePubMedGoogle Scholar
- Chang JT. Full reconstruction of Markov models on evolutionary trees: identifiability and consistency. Math Biosci. 1996;137:51–73.View ArticlePubMedPubMed CentralGoogle Scholar
- Jayaswal V, Wong TK, Robinson J, Poladian L, Jermiin LS. Mixture models of nucleotide sequence evolution that account for heterogeneity in the substitution process across sites and across lineages. Syst Biol. 2014;63:726–42.View ArticleGoogle Scholar
- Holland BR, Jermiin LR, Moulton V. Improved consensus network techniques for genome-scale phylogeny. Mol Biol Evol. 2006;23:848–55.View ArticleGoogle Scholar
- Kalyaanamoorthy S, Minh BQ, Wong TK, von Haeseler A, Jermiin LS. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14:587–9.PubMed CentralView ArticlePubMedGoogle Scholar
- Hoang DT, Chernomor O, von Haeseler A, Minh BQ, Le SV. UFBoot2: Improving the Ultrafast Bootstrap Approximation. Mol Biol Evol. 2017;35:518–22.Google Scholar
- Wang Y, Tang H, DeBarry JD, Tan X, Li J, Wang X, et al. MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Res. 2012;40:e49.PubMed CentralView ArticlePubMedGoogle Scholar
- Krzywinski M, Schein J, Birol I, Connors J, Gascoyne R, Horsman D, et al. Circos: an information aesthetic for comparative genomics. Genome Res. 2009;19:1639–45.PubMed CentralView ArticlePubMedGoogle Scholar
- Yu G, Smith D, Zhu H, Guan Y, Lam TT. Ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol and Evol. 2017;8:28–36.View ArticleGoogle Scholar
- Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Statistical Soc Series B (Methodological). 1995;57:289–300.Google Scholar
- NCBI Batch Web CD-Search Tool. https://www.ncbi.nlm.nih.gov/Structure/bwrpsb/bwrpsb.cgi?cdsid=QM3-qcdsearch-216B441388126AC7&tdata=qopts. Accessed 13 Aug 2018.
- Marchler-Bauer A, Derbyshire MK, Gonzales NR, Lu S, Chitsaz F, Geer LY, Geer RC, He J, Gwadz M, Hurwitz DI, Lanczycki CJ. CDD: NCBI's conserved domain database. Nucleic Acids Res. 2014;43(D1):D222–6.PubMed CentralView ArticlePubMedGoogle Scholar
- Ying H, Cooke I, Sprungala S, Wang W, Hayward DC, Tang Y, et al. ENA. Comparative genomics reveals the distinct evolutionary trajectories of the robust and complex coral lineages. ENA. https://www.ebi.ac.uk/ena/data/view/PRJEB23333.
- Ying H, Cooke I, Sprungala S, Wang W, Hayward DC, Tang Y, et al. ENA. Comparative genomics reveals the distinct evolutionary trajectories of the robust and complex coral lineages. ENA https://www.ebi.ac.uk/ena/data/view/PRJEB23312.
- Ying H, Cooke I, Sprungala S, Wang W, Hayward DC, Tang Y, et al. ENA. Comparative genomics reveals the distinct evolutionary trajectories of the robust and complex coral lineages. ENA. https://www.ebi.ac.uk/ena/data/view/PRJEB23371.
- Galaxea fascicularis genome data. Reefgenomics. http://gfas.reefgenomics.org/.
- Fungia sp. genome data. Reefgenomics. http://ffun.reefgenomics.org/.
- Goniastrea aspera genome data. Reefgenomics. http://gasp.reefgenomics.org/.
- Acropora digitifera genome assembly and annotation release. NCBI ftp. ftp://ftp.ncbi.nih.gov/genomes/Acropora_digitifera. Accessed 22 Feb 2017.
- Acropora millepora genome data. GenBank. http://www.ncbi.nlm.nih.gov/bioproject/473876.
- Porites lutea genome data. Reefgenomics. http://plut.reefgenomics.org/.
- Kersey PJ, Allen JE, Christensen M, Davis P, Falin LJ, Grabmueller C, et al. Ensembl genomes 2013: scaling up access to genome-wide data. Nucleic Acids Res. 2014;42(D1):D546–52.View ArticlePubMedPubMed CentralGoogle Scholar
- Aiptasia genome data. Reefgenomics. http://aiptasia.reefgenomics.org/. Accessed 5 May 2016.
- LaJeunesse TC, Thornhill DJ, Cox EF, Stanton FG, Fitt WK, Schmidt GW. High diversity and host specificity observed among symbiotic dinoflagellates in reef coral communities from Hawaii. Coral Reefs. 2004;23:596–603.Google Scholar