Skip to main content

Development of a transcriptomic database for 14 species of scleractinian corals



Scleractinian corals are important reef builders, but around the world they are under the threat of global climate change as well as local stressors. Molecular resources are critical for understanding a species’ stress responses and resilience to the changing environment, but such resources are unavailable for most scleractinian corals, especially those distributed in the South China Sea. We therefore aimed to provide transcriptome resources for 14 common species, including a few structure forming species, in the South China Sea.


We sequenced the transcriptome of 14 species of scleractinian corals using high-throughput RNA-seq and conducted de novo assembly. For each species, we produced 7.4 to 12.0 gigabases of reads, and assembled them into 271 to 762 thousand contigs with a N50 value of 629 to 1427 bp. These contigs included 66 to 114 thousand unigenes with a predicted open reading frame, and 74.3 to 80.5% of the unigenes were functionally annotated. In the azooxanthelate species Tubastraea coccinea, 41.5% of the unigenes had at least a best-hit sequence from corals. In the other thirteen species, 20.2 to 48.9% of the annotated unigenes had best-hit sequences from corals, and 28.3 to 51.6% from symbiotic algae belonging to the family Symbiodinaceae. With these resources, we developed a transcriptome database (CoralTBase) which features online BLAST and keyword search for unigenes/functional terms through a user friendly Internet interface.

Short conclusion

We developed comprehensive transcriptome resources for 14 species of scleractinian corals and constructed a publicly accessible database ( CoralTBase will facilitate not only functional studies using these corals to understand the molecular basis of stress responses and adaptation, but also comparative transcriptomic studies with other species of corals and more distantly related cnidarians.


Coral reefs are ecologically and economically important, but around the world they are threatened by global climate change such as ocean warming and acidification [1, 2], as well as local stressors such as poor fishing practices, pollution, coastal development, and unsustainable recreational activities [3,4,5]. Over the last several decades, coral reefs in many regions have degraded dramatically [6, 7]. A comprehensive assessment of 704 species of reef-building corals around the world placed 231 species (32.8%) in categories with elevated risk of extinction [8]. In Southeast Asia, around 50% of coral reefs are facing high or very high threat of degradation [9]. Along the northern coasts of the South China Sea, dramatic reduction in live coral cover and changes in dominant coral species have occurred over the last several decades in Hainan [10] and Guangdong [11] provinces.

Scleractinia, commonly called hard corals or stony corals due to their calcified skeleton, are often important reef builders. Around the world there are 1605 extant scleractinian species, which are classified into 304 genera and 40 families [12]. In recent years, it has been increasingly realized that developing molecular resources, especially transcriptome and genome sequences, can facilitate studies aiming to understand mechanisms underlying coral stress responses and resilience in the changing environment [13, 14]. Nevertheless, our survey in January 2019 showed that only a small fraction of scleractinian species (i.e. 35 species representing 20 genera and 11 families) have transcriptome data deposited in the National Center for Biotechnology Information (NCBI) database and Reefgenomics (Additional file 1:Table S1). An analysis of the datasets with collection site information shows that the geographic distribution of such transcriptomic resources is biased: 6, 9, and 9 of the transcriptomes were produced based on samples collected from the Great Barrier Reef, the Caribbean Sea, and East Asia, respectively. Only 5 were based on species distributed in the South China Sea, which in total hosts 571 species of scleractinians [15]. In addition, there were reports showing genetic differentiation among coral populations in different regions [16,17,18,19], therefore it is valuable to develop population-specific transcriptomes.

We therefore aimed to provide comprehensive transcriptomic resources for a set of common scleractinian corals in the South China Sea. Based on samples collected from Hong Kong, we sequenced and assembled the transcriptomes for 14 species of scleractinians representing 8 families and 14 genera: Fungiidae (Lithophyllon undulatum), Faviidae (Leptastrea purpurea), Merulinidae (Favites acuticollis, Platygyra carnosa, Hydnaphora exesa, Dipsastraea rotuman), Acroporidae (Montipora peltiformis, Acropora digitifera), Euphylliidae (Galaxea fascicularis), Agariciidae (Pavona decussata), Poritidae (Goniopora lobata, Porites lutea), Dendrophylliidae (Turbinaria peltata, Tubastraea coccinea). These species covered the most common species of scleractinian corals in Hong Kong, including several species (i.e. A. digitifera, P. carnosa, M. peltiformis and P. decussata) that are important in forming reef structures [20]. Although a transcriptome of P. carnosa from Hong Kong is already available [21], its completeness is quite low, with only 73.42% complete BUSCOs (Benchmarking Universal Single-Copy Orthologs). In recent years, the health of some of these coral species has been affected by various stressors including excessive bioerosion [22,23,24], skeletal growth anomalies [25], bleaching [26], and recreational activities [27, 28]. To facilitate easy access to the transcriptome data, we constructed a relational database with a user-friendly Internet interface.

Construction and content

Collection of coral samples

The following 14 species of stony corals were collected from six sites in Hong Kong from June to July 2017 by SCUBA diving (Fig. 1): P. decussata from Sharp Island North; G. lobata, P. lutea, L. undulatum, L. purpurea and G. fascicularis from Crescent Island; A. digitifera, T. peltata, M. peltiformis, D. rotumana and F. acuticollis from Bluff Island; H. exesa from Pak A; T. coccinea from Basalt Island; and P. carnosa from Lai Chi Wo. For each species, three small colonies (~ 2 cm2) were collected, put in a cooler with dry ice immediately once they were brought out of the sea surface, transported to Hong Kong Baptist University where they were stored in a freezer at − 80 °C until use.

Fig. 1

Corals included in the database construction. a, Map of Hong Kong showing the coral collection sites: Crescent Island (); Lai Chi Wo (); Bluff Island (); Pak A (); Sharp Island North (); Basalt Island (). b-o Photographs showing the external morphology of the coral polyps: Platygyra carnosa (b) Favites acuticollis (c) Dipsastraea rotumana (d) Leptastrea purpurea (e) Montipora peltiformis (f) Lithophyllon undulatum (g) Hydnophora exesa (h) Goniopora lobate (i) Galaxea fascicularis (j) Pavona decussata (k) Porites lutea (l) Acropora digitifera (m) Turbinaria peltata (n) Tubastraea coccinea (o)

RNA extraction and RNA-seq

Total RNA was extracted from each sample using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) following the manufacturer’s protocol. After treatment with RNase-free DNase I (ThermoFisher Scientific, Waltham, MA, USA), the quality of RNA samples was determined using 1% agarose gel electrophoresis and the quantity was determined using a NanoDrop 2000c Spectrophotometer (ThermoFisher Scientific, Waltham, MA, USA). RNA samples from three colonies for each species were pooled, then sent to Beijing Genomics Institute (BGI), Shenzhen for transcriptomic sequencing using an Illumina X-TEN platform. Before the library preparation, the concentration of the RNA samples was further analyzed using a Bioanalyzer 2100 (Agilent Technologies, CA, USA). Oligo dT enrichment was used during the library construction with a NEBNext Poly(A) mRNA Magnetic Isolation Module kit (New England Biolabs, MA, USA). The library was prepared using a NEBNext Ultra RNA Library Prep Kit for Illumina (New England Biolabs, MA, USA). Sequencing was conducted under the paired-end mode to produce reads 151 bp in length. All sequences were cleaned to remove adaptors and low-quality reads with a high proportion of N (> 10%) or high proportion of low-quality (Phred value Q ≤ 20) nucleotide base (> 40%). The clean reads are deposited in the Sequence Read Archive (SRA) of NCBI under accession number PRJNA512264.

Transcriptome assembly, completeness assessment, and annotation

Clean reads of each species were assembled using Trinity 2.5.1 [29] under the default settings. Transcript abundance was estimated as transcripts per kilobase million read (TPM) using RSEM 1.2.19 [30], and those without expression or very low expression (TPM < 0.5) were removed manually. Candidate open reading frames (ORFs) and peptides were identified from the transcripts using TransDecoder, and duplicate sequences with 100% similarity in predicted peptides were removed using CD-HIT [31]. For each species, the completeness of the assembled transcriptome was assessed using BUSCO (benchmarking universal single-copy orthologs) v1.1b [32] with a set of 978 conserved single-copy metazoan genes as the reference. Unigenes (i.e. the longest isoform for each gene) were annotated using both Diamond v0.9.19.120 [33] and InterProScan-5.13-52.0 [34]. Specifically, general sequence annotation was conducted using Diamond v0.9.19.120, which applied BLASTp search against NCBI’s non-redundant (nr) database with an E-value of 1 × 10− 5. To determine the protein domain structure and its functional features, Gene Ontology (GO) function, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathways for each unigene were classified using InterProScan-5.13-52.0 under default settings.

For each of the 14 species, RNA-seq produced 7.4 to 12 Gb clean reads (Table 1). Transcriptome assembly produced 271,569 to 762,693 contigs with an N50 of 629 to 1610. These contigs contained 259,788 to 495,155 predicted proteins. After removing unigenes with low expression level (TPM < 0.5) and the identical sequences, there were 66,342 to 113,634 unigenes left in the sequenced stony corals for use in downstream analyses.

Table 1 Summary of transcriptome assembly results for 14 species of corals

The transcriptomes were assessed for the presence of the 978 core metazoan BUSCOs, which showed that they contained 86.09 to 94.58% complete BUSCOs, and 2.76–9.00% partial BUSCOs (Table 1). These metrics are comparable with those of recently published coral transcriptomes [35, 36], indicating the high completeness of our transcriptome assemblies.

Proportion of sequences from coral and symbiotic algae

Unigenes from each species were annotated by BLAST search against NCBI nr database and InterProscan. For each species, 51,685 to 86,253 unigenes were successfully annotated, which accounted for 74.3 to 80.5% of the total unigenes (Table 1). Consistent with the expectation that members of the genus Tubastraea are azooxanthellate, 43.5% of the annotated T. coccinea unigenes had best hits from corals; only 0.3% of the annotated unigenes had best hit sequences from Cladocopium (formerly Symbiodinium clade C [37]), which likely came from the environmental water or reef inhabitants that had symbiotic algae. Among the annotated unigenes from the 13 zooxanthellate species, 20.2 to 48.9% unigenes had best-hit sequences from corals, and 28.3 to 51.6% from symbiotic algae. Among the unigenes, 45.8 to 61.6% were successfully annotated with GO terms, and 9.8 to 17.3% with KEGG and Reactome.

The identities of symbiotic algae

To determine the identities of symbiotic algae in the corals, we searched our coral transcriptome data for several gene fragments in two ways. First, we conducted local BLAST against the GeoSymbio database [38] to search for ITS2 genes, after adding the ITS2 Symbiodiniaceae sequences reported from several species of corals in Hong Kong [39]. Our query returned subclade C1 as the best hit sequence in 10 of 13 sequenced corals that have symbionts (i.e. G. lobata, P. lutea, L. undulatum, L. purpurea, A. digitifera, T. peltata, F. acuticollis, H. exesa, P. decussata, P. carnosa) (Additional file 1: Table S2A). Subclade C15 was the best hit for Porites lutea. However, there was no ITS2 BLAST result for the symbionts of G. fascicularis, M. peltiformis and D. rotumana, probably because the Oligo dT enrichment procedure used in the library construction had removed all of the ribosomal RNA sequences including ITS2 in these three species.

Second, we conducted local BLAST against several Symbiodiniaceae markers (chloroplast 23S rRNA genes, 18S rRNA, ITS1, 5.8S rRNA and 28S rRNA) that have been used to identify symbiotic algal types. The accession numbers of sequences of these other markers used in local BLAST are listed in Additional file 1: Table S3. To improve the accuracy of the BLAST results, the e-value threshold was set as 1e × 10− 100 and the identity larger than 98%. Our query returned Symbiodinium clade C (i.e. Cladocopium [37]) as the best-hit taxon for most of our transcriptomes, with some annotations also contained the subclade information (Additional file 1: Table S2B). Specifically, for the three species whose symbiont type could not be identified based on ITS2, both subclade C1 and C3 were the best hit for G. fascicularis and D. rotumana (based on 5.8S rRNA, ITS2, 28S rRNA and chloroplast 23S rRNA) and subclade C1 for M. peltiformis (based on 5.8S rRNA, ITS2, 28S rRNA). For the azooxanthellate coral Tubastraea coccinea, BLAST returned only one sequence from Symbiodiniaceae but its very low expression level (TMP = 0.56) indicated that the sequences were contaminants from the environment.

Database structure

CoralTBase, a relational database, was constructed using a method described previously [21, 40] to provide access to the 14 assembled coral transcriptomes through the Internet. Users can search data from one species or multiple species at the same time. The database, constructed using MySQL v5.6.34, is hosted on an Apache HTTP server. The data include DNA and protein sequences of all unigenes, which are linked with their corresponding NCBI nr, GO and KEGG and Reactome annotations by unigene ID. The database contains two relation tables (“GO_relation” and “KEGG_and_Reactome_relation”) and five entity tables (“NCBI annotation”, “Proteins”, “DNAs”, “GO” and “KEGG and Reactome”). A stand-alone web server, powered by ViroBLAST [41], was incorporated in the database to allow for BLAST search.

Utility and discussion

Layout of CoralTBase

CoralTBase can be accessed at Users can search the data from one or multiple species in several ways by BLAST or by a number of other query terms (Fig. 2). BLAST supports queries using DNA/protein sequence or fasta-format file against NCBI nr database (Fig. 2d). The output is a list of gene or protein sequences that match the query sequence with an E-value and similarity score (Fig. 2e). The returned DNA or protein sequence contains an attribute “Unigene ID” as well as its corresponding annotation. General Annotation Search allows users to query gene annotation (i.e. NCBI annotation) by gene name (e.g. ammonium transporter 2, Fig. 2f and g) or sequence ID. GO Annotation Search is the search method according to the GO class ID (Fig. 2b). A successful search will return a table that contains the matched Go class ID, and the unigene ID. KEGG and Reactome Annotation Search will return a table containing the KEGG or Reactome pathway and the matched unigenes (Fig. 2c). The DNA and protein sequences of all unigenes for each species can be downloaded from the Downloads area.

Fig. 2

The web interface of CoralTBase. a The front page. b Illustration of query by GO annotation. c Illustration of query by KEGG and Reactome annotation. d Illustration of the Basic and Advanced BLAST search options. e An example of the search result of a BLAST search, showing matched sequences, each with their BLAST statistics. f Illustration of the general annotation search. g An example of the search result of general annotation search

We used the host genes in the transcriptome of A. digitifera as an example to show the potential utility of the resource. We prepared a figure showing the GO annotations of the host genes (Additional file 3: Figure S1a). For the same species, we also plot the Wnt pathway (Additional file 3: Figure S1b). The Wnt pathway plays important roles in biomineralization and osteogenesis in vertebrates [42, 43] and has been reported in the transcriptome of the stony coral Stylophora pistillata [44]. We found that all Wnt genes in the KEGG pathway for A. digitifera can be found in our transcriptome obtained in this study. Moreover, we found a few more genes (in red boxes) in the Wnt signaling pathway from our transcriptome, which is currently not present in the KEGG networks for A. digitifera. This example indicates that the transcriptome obtained in this study has a high coverage and it will be useful for further analysis of coral biology.

We obtained 132 one-to-one homologous genes from 18 species including all species we sequenced as well as four species whose data were downloaded from the GenBank. Based on these homologous genes we constructed a phylogenetic tree to show their evolutionary relationships (Fig. 3), using a method detailed in Additional file 3: Methods. We also provided the sequences alignment in Additional file 2: Alignment.

Fig. 3

Phylogenetic tree of Scleractinia constructed based on one-to-one orthologous genes of 18 species. An image of the skeleton of each coral species is shown on the right of the species name. Numbers on main branches are bootstrap values in ML analysis. The transcriptomes of the stony coral Pocillopora damicornis, Pseudodiploria strigosa, Stylophora pistillata were downloaded from NCBI GenBank TSA database. Heliopora coerulea belongs to the order Helioporacea was used as the outgroup

Potential applications and expansion

The resources produced in this study can be used to understand basic coral biology such as stress responses, development, reproduction, symbiosis and calcification. They can also be used as a transcriptomic reference for Tag-seq, which is more cost-effective and accurate traditional RNA-seq at quantifying gene expression [45]. Such studies can be conducted to understand the molecular mechanisms underlying various responses to stressors, such as high temperature, low salinity and disease development [46,47,48]. In a broader taxonomic context, these resources can be used in comparative genomic studies aiming to understand the evolution of early development [49], biomineralization [50], and immunity [51]. In the future, CoralTBase can be expanded to include more scleractinian and non-scleractinian species. For the species that have been included in the database, the transcriptome can be updated with data from more developmental stages or from different populations.


This work has generated high-throughput transcriptome data for 14 species of scleractinian corals. It has increased the number of scleractinian corals around the world with transcriptome dataset from 35 species to 45 species, 20 genera to 26 genera and 11 families to 13 families. For some species with published transcriptome database already, our new data are either more comprehensive (i.e. Platygyra carnosa) or are based on specimens collected from different geographical areas and therefore represent different populations (i.e. A. digitifera, G. fascicularis and P. lutea). We have also organized the transcriptome data into a relational database to facilitate easy access by the public.



Beijing Genomics Institute


Gene ontology


Internal transcribed spacer


Kyoto Encyclopedia of Genes and Genomes


Maximum likelihood

nr database:

non-redundant database


Open reading frames


ribosomal RNA


Transcripts per million


  1. 1.

    Hoegh-Guldberg O, Poloczanska ES, Skirving W, Dove S. Coral reef ecosystems under climate change and ocean acidification. Front Mar Sci. 2017;4:158.

    Article  Google Scholar 

  2. 2.

    Anthony KR, Maynard JA, Guillermo D-P, Mumby PJ, Marshall PA, Cao L, Hoegh-Guldberg OVE. Ocean acidification and warming will lower coral reef resilience. Glob Chang Biol. 2011;17:1798–808.

    Article  Google Scholar 

  3. 3.

    Brodie JE, Kroon FJ, Schaffelke B, Wolanski EC, Lewis SE, Devlin MJ, et al. Terrestrial pollutant runoff to the great barrier reef: an update of issues, priorities and management responses. Mar Pollut Bull. 2012;65:81–100.

    CAS  Article  Google Scholar 

  4. 4.

    Baum G, Januar HI, Ferse SC, Kunzmann A. Local and regional impacts of pollution on coral reefs along the Thousand Islands north of the megacity Jakarta, Indonesia. PLoS One. 2015;10:e0138271.

    Article  Google Scholar 

  5. 5.

    Magesh NS, Krishnakumar S. The Gulf of Mannar marine biosphere reserve, southern India. In: Sheppard C, editor. World seas: an environmental evaluation. Cambridge: Academic Press; 2019. p. 169–84.

  6. 6.

    Wilkinson CC. Status of coral reefs of the world. Townsville: Australian Institute of Marine Science; 2004.

    Google Scholar 

  7. 7.

    Toth LT, Kuffner IB, Stathakopoulos A, Shinn EA. A 3,000-year lag between the geological and ecological shutdown of Florida's coral reefs. Glob Chang Biol. 2018;24:5471–83.

    Article  Google Scholar 

  8. 8.

    Carpenter KE, Abrar M, Aeby G, Aronson RB, Banks S, Bruckner A, et al. One-third of reef-building corals face elevated extinction risk from climate change and local impacts. Science. 2008;321:560–3.

    CAS  Article  Google Scholar 

  9. 9.

    Burke L, Reytar K, Spalding M, Perry A. Reefs at risk revisited. Washington. DC: World resources institute; 2011.

    Google Scholar 

  10. 10.

    Zhao M, Yu K, Zhang Q, Shi Q, Price GJ. Long-term decline of a fringing coral reef in the northern South China Sea. J Coastal Res. 2012;28:1088–99.

    Article  Google Scholar 

  11. 11.

    Chen TR, Yu KF, Li S, Price GJ, Shi Q, Wei GJ. Heavy metal pollution recorded in Porites corals from Daya Bay, northern South China Sea. Mar Environ Res. 2010;70:318–26.

    CAS  Article  Google Scholar 

  12. 12.

    Hoeksema BW, Cairns S. World list of Scleractinia. Scleractinia. Accessed through: World Register of Marine Species at: Accessed on 2019-01-23.

  13. 13.

    Savolainen O, Lascoux M, Merilä J. Ecological genomics of local adaptation. Nat Rev Genet. 2013;14:807.

    CAS  Article  Google Scholar 

  14. 14.

    Roche RC, Williams GJ, Turner JR. Towards developing a mechanistic understanding of coral reef resilience to thermal stress across multiple scales. Curr Clim Change Rep. 2018;4:51–64.

    Article  Google Scholar 

  15. 15.

    Huang D, Licuanan WY, Hoeksema BW, Chen CA, Ang PO, Huang H, et al. Extraordinary diversity of reef corals in the South China Sea. Mar Biodivers. 2015;45:157–68.

    Article  Google Scholar 

  16. 16.

    Underwood JN. Genetic diversity and divergence among coastal and offshore reefs in a hard coral depend on geographic discontinuity and oceanic currents. Evol Appl. 2009;2:222–33.

    Article  Google Scholar 

  17. 17.

    Prada C, DeBiasse MB, Neigel JE, Yednock B, Stake JL, Forsman ZH, et al. Genetic species delineation among branching Caribbean Porites corals. Coral Reefs. 2014;33:1019–30.

    Article  Google Scholar 

  18. 18.

    Van der Ven RM, Triest L, De Ryck DJ, Mwaura JM, Mohammed MS, Kochzius M. Population genetic structure of the stony coral Acropora tenuis shows high but variable connectivity in East Africa. J Biogeogr. 2016;43:510–9.

    Article  Google Scholar 

  19. 19.

    Concepcion GT, Crepeau MW, Wagner D, Kahng SE, Toonen RJ. An alternative to ITS, a hypervariable, single-copy nuclear intron in corals, and ITS use in detecting cryptic species within the octocoral genus Carijoa. Coral Reefs. 2008;27:323–36.

    Article  Google Scholar 

  20. 20.

    Chan ALK, Chan KK, Choi CLS, McCorry D, Lee MW, Ang PO. Field guide to the hard corals of Hong Kong. Hong Kong: Hong Kong: agriculture, fisheries and conservation department, Hong Kong SAR government and Cosmos book ltd; 2005.

    Google Scholar 

  21. 21.

    Sun J, Chen Q, Lun JC, Xu J, Qiu JW. PcarnBase: development of a transcriptomic database for the brain coral Platygyra carnosus. Mar Biotechnol. 2013;15:244–51.

    Article  Google Scholar 

  22. 22.

    Dumont CP, Lau DCC, Astudillo JC, Fong KF, Chak STC, Qiu JW. Coral bioerosion by the sea urchin Diadema setosum in Hong Kong: susceptibility of different coral species. J Exp Mar Biol Ecol. 2013;441:71–9.

    Article  Google Scholar 

  23. 23.

    Qiu JW, Lau DC, Cheang CC, Chow WK. Community-level destruction of hard corals by the sea urchin Diadema setosum. Mar Pollut Bull. 2014;85:783–8.

    CAS  Article  Google Scholar 

  24. 24.

    Xie JY, Wong JC, Dumont CP, Goodkin N, Qiu JW. Borehole density on the surface of living Porites corals as an indicator of sedimentation in Hong Kong. Mar Pollut Bull. 2016;108:87–93.

    CAS  Article  Google Scholar 

  25. 25.

    Zhang Y, Sun J, Mu H, Lun JCY, Qiu JW. Molecular pathology of skeletal growth anomalies in the brain coral Platygyra carnosa: a meta-transcriptomic analysis. Mar Pollut Bull. 2017;124:660–7.

    CAS  Article  Google Scholar 

  26. 26.

    Xie JY, Lau DC, Kei K, Vriko PF, Chow WK, Qiu JW. The 2014 summer coral bleaching event in subtropical Hong Kong. Mar Pollut Bull. 2017;124:653–9.

    CAS  Article  Google Scholar 

  27. 27.

    Au ACS, Zhang L, Chung SS, Qiu JW. Diving associated coral breakage in Hong Kong: differential susceptibility to damage. Mar Pollut Bull. 2014;85:789–96.

    CAS  Article  Google Scholar 

  28. 28.

    Zhang LY, Chung S, Qiu JW. Assessing perceived crowding of diving sites in Hong Kong. Ocean Coast Manage. 2015;116:177–84.

    Article  Google Scholar 

  29. 29.

    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. Nat Biotechnol. 2011;29:644.

    CAS  Article  Google Scholar 

  30. 30.

    Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12:323.

    CAS  Article  Google Scholar 

  31. 31.

    Huang Y, Niu B, Gao Y, Fu L, Li W. CD-HIT suite: a web server for clustering and comparing biological sequences. Bioinformatics. 2010;26:680–2.

    CAS  Article  Google Scholar 

  32. 32.

    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.

    Article  Google Scholar 

  33. 33.

    Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2014;12:59.

    Article  Google Scholar 

  34. 34.

    Jones P, Binns D, Chang HY, Fraser M, Li W, McAnulla C, et al. InterProScan 5: genome-scale protein function classification. Bioinformatics. 2014;30:1236–40.

    CAS  Article  Google Scholar 

  35. 35.

    Kenkel CD, Bay LK. Novel transcriptome resources for three scleractinian coral species from the indo-Pacific. GigaScience. 2017;6:1–4.

    Article  Google Scholar 

  36. 36.

    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.

    CAS  Article  Google Scholar 

  37. 37.

    LaJeunesse TC, Parkinson JE, Gabrielson PW, Jeong HJ, Reimer JD, Voolstra CR, Santos SR. Systematic revision of Symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr Biol. 2018;28:2570–80.

    CAS  Article  Google Scholar 

  38. 38.

    Franklin EC, Stat M, Pochon X, Putnam HM, Gates RD. GeoSymbio: a hybrid, cloud-based web application of global geospatial bioinformatics and ecoinformatics for Symbiodinium-host symbioses. Mol Ecol Resour. 2012;12:369–73.

    Article  Google Scholar 

  39. 39.

    Wong JC, Thompson P, Xie JY, Qiu JW, Baker DM. Symbiodinium clade C generality among common scleractinian corals in subtropical Hong Kong. Reg Stud Mar Sci. 2016;8:439–44.

    Article  Google Scholar 

  40. 40.

    Ip JC, Mu H, Chen Q, Sun J, Ituarte S, Heras H, et al. AmpuBase: a transcriptome database for eight species of apple snails (Gastropoda: Ampullariidae). BMC Genomics. 2019;19:179.

    Article  Google Scholar 

  41. 41.

    Deng W, Nickle DC, Learn GH, Maust B, Mullins JI. ViroBLAST: a stand-alone BLAST web server for flexible queries of multiple databases and user's datasets. Bioinformatics. 2007;23:2334–6.

    CAS  Article  Google Scholar 

  42. 42.

    Canalis E, Economides AN, Gazzerro E. Bone morphogenetic proteins, their antagonists, and the skeleton. Endocr Rev. 2003;24:218–35.

    CAS  Article  Google Scholar 

  43. 43.

    Rodda SJ, McMahon AP. Distinct roles for hedgehog and canonical Wnt signaling in specification, differentiation and maintenance of osteoblast progenitors. Development. 2006;133:3231–44.

    CAS  Article  Google Scholar 

  44. 44.

    Karako-Lampert S, Zoccola D, Salmon-Divon M, Katzenellenbogen M, Tambutté S, Bertucci A, et al. Transcriptome analysis of the scleractinian coral Stylophora pistillata. PLoS One. 2014;9:e88615.

    Article  Google Scholar 

  45. 45.

    Lohman BK, Weber JN, Bolnick DI. Evaluation of TagSeq, a reliable low-cost alternative for RNA seq. Mol Ecol Resour. 2016;16:1315–21.

    CAS  Article  Google Scholar 

  46. 46.

    Ross C, Ritson-Williams R, Olsen K, Paul VJ. Short-term and latent post-settlement effects associated with elevated temperature and oxidative stress on larvae from the coral Porites astreoides. Coral Reefs. 2013;32:71–9.

    Article  Google Scholar 

  47. 47.

    Maynard A, Bible JM, Pespeni MH, Sanford E, Evans TG. Transcriptomic responses to extreme low salinity among locally adapted populations of Olympia oyster (Ostrea lurida). Mol Ecol. 2018;27:4225–40.

    CAS  Article  Google Scholar 

  48. 48.

    Closek CJ, Sunagawa S, DeSalvo MK, Piceno YM, DeSantis TZ, Brodie EL, et al. Coral transcriptome and bacterial community profiles reveal distinct yellow band disease states in Orbicella faveolata. ISME J. 2014;8:2411.

    CAS  Article  Google Scholar 

  49. 49.

    Salinas-Saavedra M, Wikramanayake A. β-Catenin has an ancestral role in cell fate specification but not cell adhesion. bioRxiv. 2019:520957.

  50. 50.

    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.

    Article  Google Scholar 

  51. 51.

    Pinzón JH, Kamel B, Burge CA, Harvell CD, Medina M, Weil E, Mydlarz LD. Whole transcriptome analysis reveals changes in expression of immune-related genes during and after bleaching in a reef-building coral. Roy Soc Open Sci. 2015;2:140214.

    Article  Google Scholar 

Download references


We thank Dr. Alice Y.S. Law at Hong Kong Baptist University for technical support.


This project was supported by Shenzhen Science and Technology Innovation Committee (JCYJ20170307161326613), Environment and Conservation Fund (2017–03) and General Research Fund of Hong Kong (12102018).

Availability of data and materials

All clean reads are deposited in the NCBI Sequence Read Archive (SRA) under the project number PRJNA512264. The assembled and annotated transcriptomes are available at the following website:

Author information




JYX and YHY identified and collected coral samples. YZ extracted coral RNA, analyzed transcriptome data and drafted the manuscript. QC and JX designed and constructed the database website. JWQ, JX, BX and BL initiated the project, contributed resources and revised the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Jianliang Xu or Jian-Wen Qiu.

Ethics declarations

Ethics approval and consent to participate

Our research adheres to the Wild Animals Protection Ordinance of Hong Kong (Cap. 170).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Additional files

Additional file 1:

Table S1. Information on published transcriptome datasets from Scleractinia. Table S2. Symbiotic algae types determined by BLAST coral transcriptomes against the ITS2 and rRNA genes (i.e. 18S, 28S 23S rRNA) from GenBank database. Table S3. The accession numbers of sequences in the GenBank database used for symbiotic algae clade identification. (XLSX 179 kb)

Additional file 2:

Alignment. The alignment of one-to-one homologous genes of 18 stony coral species. (FASTA 3031 kb)

Additional file 3:

Method. Method for phylogeny of Scleractinia and Figure S1. (DOCX 361 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, 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 ( applies to the data made available in this article, unless otherwise stated.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, Y., Chen, Q., Xie, J.Y. et al. Development of a transcriptomic database for 14 species of scleractinian corals. BMC Genomics 20, 387 (2019).

Download citation


  • Coral
  • Coral reef
  • Database
  • Scleractinia
  • Symbiotic algae
  • Transcriptome