Production of a reference transcriptome and transcriptomic database (EdwardsiellaBase) for the lined sea anemone, Edwardsiella lineata, a parasitic cnidarian
- Derek J Stefanik†1,
- Tristan J Lubinski†1,
- Brian R Granger†1, 2,
- Allyson L Byrd2,
- Adam M Reitzel3,
- Lukas DeFilippo4,
- Allison Lorenc4 and
- John R Finnerty1, 2, 4Email author
© Stefanik et al.; licensee BioMed Central Ltd. 2014
Received: 18 July 2013
Accepted: 11 December 2013
Published: 28 January 2014
The lined sea anemone Edwardsiella lineata is an informative model system for evolutionary-developmental studies of parasitism. In this species, it is possible to compare alternate developmental pathways leading from a larva to either a free-living polyp or a vermiform parasite that inhabits the mesoglea of a ctenophore host. Additionally, E. lineata is confamilial with the model cnidarian Nematostella vectensis, providing an opportunity for comparative genomic, molecular and organismal studies.
We generated a reference transcriptome for E. lineata via high-throughput sequencing of RNA isolated from five developmental stages (parasite; parasite-to-larva transition; larva; larva-to-adult transition; adult). The transcriptome comprises 90,440 contigs assembled from >15 billion nucleotides of DNA sequence. Using a molecular clock approach, we estimated the divergence between E. lineata and N. vectensis at 215–364 million years ago. Based on gene ontology and metabolic pathway analyses and gene family surveys (bHLH-PAS, deiodinases, Fox genes, LIM homeodomains, minicollagens, nuclear receptors, Sox genes, and Wnts), the transcriptome of E. lineata is comparable in depth and completeness to N. vectensis. Analyses of protein motifs and revealed extensive conservation between the proteins of these two edwardsiid anemones, although we show the NF-κB protein of E. lineata reflects the ancestral structure, while the NF-κB protein of N. vectensis has undergone a split that separates the DNA-binding domain from the inhibitory domain. All contigs have been deposited in a public database (EdwardsiellaBase), where they may be searched according to contig ID, gene ontology, protein family motif (Pfam), enzyme commission number, and BLAST. The alignment of the raw reads to the contigs can also be visualized via JBrowse.
The transcriptomic data and database described here provide a platform for studying the evolutionary developmental genomics of a derived parasitic life cycle. In addition, these data from E. lineata will aid in the interpretation of evolutionary novelties in gene sequence or structure that have been reported for the model cnidarian N. vectensis (e.g., the split NF-κB locus). Finally, we include custom computational tools to facilitate the annotation of a transcriptome based on high-throughput sequencing data obtained from a “non-model system.”
Parasitism is arguably the dominant trophic strategy on earth, as the number of parasitic species is thought to exceed the number of free-living species, perhaps by 4-to-1 or more [1, 2]. Presumably, every cellular organism is subject to parasitism, and parasites affect their hosts in a number of profound ways. For instance, parasites have helped to drive the evolution of sex [3–5] and immune systems . They can markedly change the behavior of their hosts , influence host species’ mating strategies and genetic variation [8, 9], and contribute to the decline of locally threatened populations [10, 11]. However, despite the prevalence of parasitism and its clear ecological and evolutionary importance, parasitic species are relatively poorly characterized. For example, of the 1.5 million species currently named by taxonomists, less than 1% are known to be parasites [12, 13].
The evolution of parasitism from an ancestral free-living state can be accompanied by radical alterations to an organism’s ontogeny, bodyplan, and life history (e.g., polyembryony in parasitoid wasps; ). Despite this, relatively few studies have explored the developmental evolution of parasitism, mainly because there are practical and theoretical hurdles to such studies. Foremost, it is often difficult to culture parasites in a laboratory setting, as maintaining an obligate parasite requires co-culture of a suitable host. Furthermore, in long-established obligate parasites, the initial steps in their developmental evolution are often obscured by their lengthy evolutionary divergence from free-living outgroups. Finally, parasites are generally not regarded as “model” systems, since parasitic life cycles are often highly derived and therefore not representative of the ancestral free-living condition in major organismal lineages. However, it has been argued that parasites should be of particular interest to evolutionary-developmental biology precisely because their tight associations with host species create “highly integrated reproductive—developmental—ecological systems” that are persistent through space and time .
To inform our knowledge of the E. lineata gene repertoire, and how changes in expression of particular genes may contribute to ontogenetic changes associated with a derived life history, we sequenced and assembled the transcriptome of E. lineata from developmental stage-specific cDNA libraries. We created a database, EdwardsiellaBase, as a platform to share sequence information from E. lineata and facilitate queries of gene expression across developmental stages. Both the raw reads and assembled transcriptomic sequences are publicly accessible via the web interface of EdwardsiellaBase.
Construction and content
Sequencing and assembly
Relationship to edwardsiid type specimens
Molecular divergence dating
Taxonomic affinity and inferred phylogenetic antiquity of sequences
Gene ontology (GO) analysis
Metabolic pathway analysis
Recovery of specific genes and gene families from E. lineata
The data can be searched by Contig, Protein Family, Metabolic Pathway or Gene Ontology (Figure 13; red arrows). EdwardsiellaBase also supports the complete range of BLAST options to search the assembled contigs for matches to a query sequence. Finally, the JBrowse [69, 70] function enables one to view alignments of the raw reads to the assembled transcriptome to help assess validity of transcripts. A literature database allows users to search the published literature on Edwardsiella using matches to keywords or any user-entered text string. The database structure and entity relationships are depicted in Additional file 9.
Evidence that the transcriptome is representative
The present study describes a transcriptome assembly for E. lineata based on roughly 15 billion nucleotides of RNA sequencing. This is one of the largest transcriptomic datasets currently available for any cnidarian [28, 67, 71–85], and approximately 2.5 times the sequencing yield estimated to be sufficient for assembling a representative transcriptome . To ensure that we captured transcripts expressed throughout E. lineata’s complex life cycle, we generated cDNA libraries from five distinct developmental stages. Our saturation analysis showed that (Figure 3) additional sequencing of these libraries would result in identification of relatively few novel transcripts. Evidence that the transcriptome assembly is representative of the expressed gene repertoire of an edwardsiid anemone is the comparable recovery of GO terms (Figure 8; Additional file 6) and gene families (Table 1) (Figures 10, 11 and 12; Additional file 6) from E. lineata and N. vectensis. Taken together, these data suggests that our sequencing effort was sufficient to produce a representative transcriptome that captures a large fraction of the transcript variety encoded by the E. lineata genome. Undoubtedly, we have failed to capture some transcripts that are expressed at very low levels during the developmental stages studied here, or that are expressed only in different developmental, physiological, or environmental contexts.
Utility of E. lineata for comparative transcriptomics and genomics
The utility of any species for comparative transcriptomic and genomic studies depends on its relationship to other taxa for which extensive sequence data are available. Molecular, morphological, and developmental characters support the placement of E. lineata within the family Edwardsiidae and the subfamily Milneedwardsiinae, a clade comprising the genera Edwardsiella, Nematostella, Drillactis, and Paraedwardsia[25, 33, 87]. The 18S phylogenetic analysis performed here confirms the specimens we characterized as E. lineata. This confirmation is important, given that we are seeking to establish a reference transcriptome for the species. The 18S phylogenetic analysis also supports the placement of Edwardsiella and Nematostella within the Milneedwardsiinae (Figure 4). Thus, this study supports the conclusion that E. lineata is one of the closest living relatives of N. vectensis. Our molecular clock estimate (Figure 5) suggests the divergence between Nematostella and Edwardsiella occurred sometime between the early Triassic Period (215 mya) and the early Devonian (>360 mya). As N. vectensis protein-coding genes appear to evolve at a rate comparable to, or even slower than vertebrates , the evolutionary distance between Edwardsiella and Nematostella is likely sufficient to facilitate the identification of functional conservation in protein sequence and structure; i.e., at this distance, sequence conservation is not likely to reflect mere phylogenetic inertia. Looking forward, comparing genome sequences between these two edwardsiid anemones is likely to be useful in identifying conserved cis-regulatory sequences, as has been done for echinoderm species spanning divergences from 35–500 mya [88, 89].
BLAST based annotation
Forty percent of the assembled contigs in the E. lineata transcriptome produced BLAST hits to sequences in NCBI’s non-redundant (NR) protein database, while 60% did not match any protein sequences in the database (Figure 6). This ratio between BLAST hits and misses for contigs within the E. lineata transcriptome is comparable to another published cnidarian transcriptome assembly for the coral Pocillopora damicornis. The high percentage of contigs in the E. lineata assembly that do not produce BLAST hits may be a function of contig size. Ninety-one percent of the contigs that fail to produce BLAST hits are relatively short (100–500 nucleotides in length; Additional file 4). Since BLAST scores are influenced by sequence match length, shorter sequences will produce lower scores, and may also be more likely to represent assembly artifacts or truncated transcript models. Over two-thirds of the raw reads (>71%) map to contigs that produce BLAST hits (Additional file 3).
Another explanation for the presence of contigs in the E. lineata transcriptome assembly that produced no BLAST hits to NR protein database is that some of the contigs may represent assembly of long, non-coding RNA transcripts, for which no cognate protein would exist in the NR database. We used BLASTn to query the NONCODE database  with the set of contigs that produced no hits against the NR protein database. This search yielded matches for 354 contigs. The E. lineata transcriptome assembly therefore contains non-coding transcripts, but these transcripts represent a small fraction of the total contigs that produced no BLAST hits to the NR protein database.
Given the key position of cnidarians in metazoan phylogeny — as the likely sister group to triploblastic bilaterians — there is widespread interest in pinpointing the evolutionary origin of cnidarian genes. For example, which genes have been conserved since the time of the eumetazoan common ancestor, and which genes are cnidarian inventions? We approached this question using taxonomically restricted BLAST searches (Figure 7). Using this approach, we can ascribe putative origins to the genes that encode the E. lineata transcripts we recovered. For example, 19.2% of the E. lineata contigs generated significant matches to sequences from other Eukaryota, plus Eubacteria, and Archaea, suggesting (1) that these genes originated prior to the origin of Eukaryota, and (2) they have been conserved in eukaryotes and prokaryotes since that time. The number of hits produced from this analysis can be influenced by a few confounding factors, which should be considered when viewing the results. While it is possible that these sequences represent shared transcripts of essential function common to the organisms to which we ascribed their origin and their descendent lineages; a potentially confounding variable is that it is also possible that some of these sequences are transcripts produced by other organisms residing within and/or on the focal taxon, and which were subsequently sequenced and deposited in the nr database, or represent unintended taxonomic sampling from the holobiont of the anemone in this study. Due to potentially confounding factors, and the relatively permissive BLAST cutoff threshold utilized, the analysis of taxonomic affinity in this study represents a provisional phylogenetic stratigraphy of gene origins. To achieve a more robust assignment of origin across the taxonomic breadth of this study, one would need to produce multiple sequence alignment and phylogenetic trees for each of the 90,440 transcripts in the E. lineata transcriptome.
The BLAST-based approach used here is currently limited by the uneven representation of major taxonomic groups in the NCBI database, including the phylum Cnidaria. While over 16% of the E. lineata sequences generated significant matches to N. vectensis alone, only 2.2% generated matches to other cnidarians in addition to Nematostella. This disparity is likely a reflection of the relatively large amount of data from N. vectensis in the database. As more cnidarian taxa are sequenced, we expect many of the sequences from E. lineata that currently generate hits to Nematostella alone will be shared across the phylum.
Gene ontology (GO) analysis
We were able to assign 17 GO subcategory terms under the “Molecular Function” ontology to transcripts from either the N. vectensis ESTs and E. lineata sequencing produced from this study (Figure 8). Sixteen of these subcategories were represented in transcripts from both sea anemones. However, the Molecular Function subcategory of “morphogen activity” was only assigned to sequences from E. lineata. Of the remaining 16 subcategories, there is a generally close correspondence in presence/absence of subcategories within each ontology between the expressed sequence resources from each sea anemone. Taken together, these findings suggest that the transcriptome assembly produced for E. lineata is comparably representative of the expressed transcript repertoire of an edwardsiid sea anemone as the N. vectensis ESTs. This interpretation is based on the assumption that these two confamilial sea anemones would exhibit similar gene ontology distributions in their expressed transcripts as a function of shared, derived physiological and genomic characteristics.
Recovery of selected gene families in E. lineata
The largely consistent recovery of orthologous genes from seven divergent gene families in E. lineata and N. vectensis suggests that the genetic repertoire of these two edwardsiid anemones is well conserved and that the reference assembly described here provides thorough coverage of the E. lineata transcriptome. Figure 10 depicts a Maximum Likelihood phylogenetic analysis of Wnt sequences from E. lineata, N. vectensis, and human, alongside a MEME analysis of the protein coding domains of these transcripts. This analysis reveals extensively conserved protein motif architecture across Wnt proteins between cnidarians and human (the deuterostome representative). Additionally, motif conservation is high between the two sea anemones, with the entire motif complement for each protein being conserved between N. vectensis and E. lineata, with the exception of three transcripts (Wnt10, Wnt6, and Wnt7B) in which one or more motifs are discordant between the two taxa. All E. lineata sequences used in this analysis represent single contigs (with the exception of Wnt3, Wnt1 and Wnt7B, which were conceptually spliced). Taken together with the degree of protein coding motif conservation between the two sea anemones, this suggests that many contigs represent full-length transcripts. The detailed analysis of Wnt7 sequences (Figure 11) also clearly supports the conclusion that the Wnt7A/7B splice variants are conserved between N. vectensis and E. lineata.
No evidence for pervasive change in the gene repertoire of this parasite
This study has produced no evidence for pervasive changes in the gene repertoire of E. lineata that might have evolved in concert with the evolution of its novel parasitic life cycle. In contrast, a recent study on four cestodes identified extensive losses of genes and pathways that are broadly conserved in other animals as well as the origin of specialized metabolic pathways adapted to extract nutrients from the host . This is to be expected given that cestodes are an ancient lineage of obligate internal parasites. Although we cannot date the antiquity of parasitism in E. lineata, except to say that it must postdate the last common ancestor with N. vectensis, we should not expect extensive gene losses in E. lineata, as this parasitic anemone retains all of the life cycle stages present in related free-living anemones. Therefore, it would presumably require the same developmental regulatory genes and metabolic pathways. Despite its derived life cycle, we expect that there will be genes and proteins for which E. lineata reflects the primitive condition, while the free-living N. vectensis, an important cnidarian model system, exhibits a derived condition. NF-κB is such an example, as the NF-κB protein of E. lineata reflects the ancestral protein structure, in which the DNA-binding domain and inhibitory domain are contained within the same transcript, whereas these domains are split between two separate loci in N. vectensis (Figure 12). As an interesting aside, NF-κB appears to be one of the genes lost in parasitic cestodes . We expect that E. lineata has evolved some genetic modifications that would make it better able to exploit its host ctenophore, though these may be few in number. A detailed analysis of differential gene expression between developmental stages, which is beyond the scope of this paper, is currently underway.
Functionality of EdwardsiellaBase
EdwardsiellaBase was modeled after the previously published species-specific cnidarian databases PocilloporaBase , and StellaBase [92, 93], but it expands upon their functionality in key ways. As with these published databases, an html-based interface allows users to search the assembled contigs using contig identifiers, enzyme names or EC numbers, protein families (Pfam), protein names, and Gene Ontology (GO) information (Figure 13). The database also features a fully equipped BLAST interface for searching the assembled contigs based on sequence similarity to known genes and proteins. New functions include a literature search, JBrowse alignment viewer [69, 70], and individual contig pages. The literature search allows the user to query the E. lineata literature, much of which has been published in relatively inaccessible venues, such as books that are out of print. The JBrowse feature allows users to view alignments of reads to assembled contigs to and visualize the relative abundance of transcripts, including alternate splice forms. The individual contig page summarizes available information, and also provides a notes section, to which users can submit entries. Provisional gene names have been assigned to each contig that produced a BLAST hit using Blast2GO. The database may be searched using these gene names, and when a name has been assigned to a given contig, that name is provided on the contig information page.
It is also possible to search for matches to a query sequence using the complete set of BLAST options. BLAST searches return a standard BLAST page, with a few additional features.
We describe the sequencing and assembly of a reference transcriptome for the parasitic cnidarian, the lined sea anemone, E. lineata. This dataset represents a significant contribution to the comparative study of cnidarian transcriptomes because of (1) the overall sequencing yield (~15,000 Mb of nucleotide sequence), (2) the phylogenetic placement of E. lineata as the closest cnidarian taxa to N. vectensis for which appreciable molecular sequence data exist, and (3) the fact that E. lineata is a recently evolved parasite whose novel life cycle is tractable to laboratory investigation. The assembled transcripts published in this study capture the large majority of the transcriptome of this sea anemone. The diversity of Gene Ontology terms, metabolic pathways components, and gene family members we were able to recover from the E. lineata contigs compares favorably with published EST data from N. vectensis. The assembled contigs are available in a searchable database, EdwardsiellaBase, that will serve as a platform for studying the evolutionary developmental genomics of E. lineata’s novel, derived parasitic life history, and will be useful for comparative transcriptomic studies between cnidarian taxa, particularly between E. lineata and N. vectensis. The scripts and computational tools employed in this study are included in the supplementary files to facilitate the annotation of transcriptome assemblies from other emerging model systems for which genomic data are not available.
Availability and requirements
EdwardsiellaBase is freely available at http://edwardsiellabase.org.
Animal collection and developmental sampling
Ctenophores (Mnemiopsis leidyi) infected with E. lineata were collected from July through October of 2009 and 2010 at Woods Hole, MA as previously described . Approximately two-hundred E. lineata parasites were extracted from approximately 70 M. leidyi using forceps and a scalpel. Approximately 30 of these excised parasites (Figure 1C) were immediately harvested for RNA isolation. The remaining parasites were transferred to full-strength artificial seawater (Instant Ocean; salinity = 36 parts per thousand) and maintained at room temperature, so they could continue their development . Individuals were then selected to represent particular developmental stages based on the duration of their incubation and their gross morphological appearance. To represent the parasite-to-planula transition stage (Figure 1D), approximately 30 of the developing anemones were collected for RNA isolation 12–24 h after their excision from the host. The anemones at this stage of development exhibited the following three phenotypic and/or behavioral criteria: (1) reduction in pharynx length relative to the parasitic stage, (2) ability to move via cilia, and (3) an overall body shape that was intermediate between the vermiform parasite and the ovoid planula. To represent the larval stage (the planula; Figure 1E), approximately 30 anemones were allowed to develop for 2–4 days post host excision. The planulae exhibited the following characteristics: (1) lack of transparency, (2) vigorous swimming ability, and (3) ovoid shape. Thirty of the remaining larvae were allowed to develop until they began showing signs of metamorphosis into polyps (Figure 1F), such as (1) cessation of swimming and (2) tentacle eruption. The adult stage (Figure 1G) was represented by individuals that successfully metamorphosed into polyps capable of using their tentacles to feed on freshly hatched brine shrimp larvae (Artemia salina). Six individuals were harvested for RNA isolation at this stage.
RNA isolation, library preparation and sequencing
Total RNA was isolated from pooled specimens for each of the five developmental stages (Figure 1C-G). For the four pre-adult stages (parasites, the parasite-to-larva transition, larvae, and the larva-to-polyp transition), we used ~30 individuals in each case, which is equivalent to ~100 mg of tissue. For the adult polyp we isolated RNA from 6 individuals. For the pre-adult stages, total RNA was isolated using TRIzol (Life Technologies) according to the manufacturers protocol. From adult polyps, total RNA was isolated using the Omega Biotek Mollusk RNA Isolation Kit. Subsequently, mRNA was isolated from each pool of total RNA using the Poly(A) Purist mRNA isolation kit (Ambion). Separate cDNA libraries were prepared for each of the five developmental stages using the mRNA Sample Preparation Kit from Illumina. Sequencing of cDNA libraries was performed on a Genome Analyzer IIx (Illumina). Each library was sequenced on an individual lane of a flow cell using 40-bp, paired-end reads. Overall, the five libraries yielded a total of 376,243,854 sequencing reads that passed the Illumina GAIIx quality filter.
Each stage-specific library was individually assembled using Velvet (version 1.1.05; ) and Oases (version 0.1.22; ). For the adult, we used a kmer range of 25–39; for all other stages we used a kmer range of 21–39. For all other assembly parameters, we used the default settings for Velvet and Oases. The individual assemblies were then merged using both Velvet and Oases to produce a single reference transcriptome. The merged assemblies comprise 90,440 contigs.
Assessment of sequencing coverage
We used a random re-sampling approach to assess how sequencing depth affected recovery of transcripts. All reads from all stages were aligned to the reference file using Bowtie 2 (v. 2.0.0-beta; ). The resulting sam file was then parsed with a custom python script (Additional file 10) that randomly selects a given number of reads from the total reads without replacement. This script then returns a file listing the nominal coverage of all contigs, based on the contig length, read length, and number of reads aligned to each contig. The file can then be easily parsed to assess the amount of contigs above each coverage threshold. We evaluated subsets of the total reads ranging from 0 to all of the reads in increments of 50 million. The analysis was performed 3 times for each subset size, except for the 0 and “all” read sets, as the replicates of these sets are guaranteed to be exactly the same each time. For each data point, the standard deviation was calculated, and found to be negligible (all less than 0.1% of the total contigs that pass a given coverage threshold).
Divergence date estimation
To estimate the divergence between E. lineata and N. vectensis, we used a molecular clock approach based on the published multi-gene alignment of Erwin et al. . This alignment comprises seven nuclear housekeeping genes (aldolase, methionine adenosyltransferase, ATP synthase beta chain, catalase, elongation factor 1 alpha, triosephosphate isomerase and phosphofructokinase; ) and three ribosomal DNAs (5.5S, 18S, and 28S rDNA) from 119 taxa. We restricted our analysis to taxa for which Erwin et al. included fossil calibration points (Additional file 2). The resulting alignment included 87 taxa (Additional file 11). We used BLAST searches to identify orthologs of all these genes from E. lineata. The E. lineata sequences were manually added to the alignment.
The alignment of protein coding and ribosomal genes was input into MrBayes (version 3.1.2 , as implemented in the CIPRES Science Gateway, version 3.3), and a phylogeny was estimated using mixed models for the protein and nucleotide partitions of the alignment. We set up one run of four chains using two unlinked GTR + gamma models: an amino acid GTR + gamma model was applied to the amino acid partition, and a nucleotide GTR + gamma model was applied to the rDNA partition. The shape of the gamma distribution was estimated using four rate categories for each partition. Chains were allowed to run for 1,000,000 generations, with a burn-in of 25%, and sampling every 5,000. The resulting tree for the full set of 87 taxa can be viewed in Additional file 2.
Bayesian estimation of divergence dating was carried out using the program Phylobayes (version 3.3b; [95, 96]). The current iteration of Phylobayes does not support mixed (protein and nucleotide) datasets for divergence dating, so we followed the example established by Erwin et al.  and used just the protein-coding characters for the divergence dating analysis. The chronogram resulting from Phylobayes is available in Additional file 2.
All 90,440 contigs were compared against the non-redundant (NR) database on NCBI using BLASTx at a threshold Expect value of 1E-03. Contigs with no match were BLASTed against a database of noncoding nucleotides on the NONCODE database  to search for homology to transcribed RNAs that are not translated into protein.
From the BLAST results, the taxonomic source of the top five hits obtained for each contig were stored in EdwardsiellaBase. To estimate the phylogenetic origin of sequences in the E. lineata transcriptome, protein lists were downloaded from NCBI using a series of scripts (Additional file 12) for a selection of taxonomic categories encompassing taxa of increasingly distant evolutionary relationship to E. lineata. The taxonomic categories used were: (1) N. vectensis, (2) Cnidaria excluding N. vectensis, (3) Bilateria, (4) Metazoa excluding Cnidaria and Bilateria, (5) Eukaryota excluding Metazoa, (6) Archaea, (7) Eubacteria, and (8) viruses. For this search, we also used BLASTx at a threshold Expect value of 1E-03.
GO terms were assigned to contigs through the Blast2GO servers after importing the BLAST results. Production of informative graphs about the GO data was generated through analysis of the data via a custom Python script (Additional file 5) which parses a file (gene_ontology.obo) from the Gene Ontology ftp site containing information about each node and its parent(s) and children. From this, information about the GO hierarchy is parsed by the script, and stored temporarily. Using the recovered GO data, and a starting node in the hierarchy, the script then looks for nodes below the starting node in the hierarchy for which GO data was recovered in the transcriptome data in order to determine the coverage of the sub-hierarchy. With this script, a user can identify all the contigs associated with a particular GO term and its subtree. In our analysis, we grouped all contigs according to the highest sub-category under the principal GO categories: Biological Process, Cellular Component, and Molecular Function (Figure 8; Additional file 6).
Blast2GO annotated contigs with Enzyme Commission (E.C.) numbers when applicable. Available E.C. numbers for N. vectensis were obtained through the Kyoto Encyclopedia of Genes and Genomes (KEGG; ). The E.C. numbers for E. lineata and N. vectensis were compared to see which enzymes were in both sets, and which were exclusive to one anemone or the other. Enzymes were then formatted, and cross-referenced to an edge list file from the interactive tree of life to produce a file (Additional file 13), which was uploaded to the iPath2.0 program for visualization (Figure 9; Additional file 7; ).
Recovery of gene families from E. lineata
We compiled FASTA files containing published protein sequences from N. vectensis for bHLH-PAS genes, deiodinases, Fox genes, LIM homeodomains, minicollagens, nuclear receptors, Sox genes, and Wnts. We then queried the E. lineata transcriptome with these sequences using tBLASTn. The top 10 hits from E. lineata were retained from each query. These were used to perform reciprocal BLASTx searches versus the FASTA file containing the protein sequences from N. vectensis to verify that each E. lineata sequence is most similar to the original query sequence. This sequence of BLAST searches was performed using a custom Python script (Additional file 14). In the case of all gene families except minicollagens (which are unique to Cnidaria), predicted protein sequences were obtained from N. vectensis, E. lineata, and Homo sapiens. Sequences were aligned using MUSCLE , and amino acid characters with gaps were removed from the alignment. The resulting gap-free alignments were then analyzed using ProtTest (v.3; ) to determine the best-fit model of amino acid replacement according to the Akaike Information Criterion. Maximum-likelihood phylogenies were estimated from the edited alignments using the default parameters of RaxML-HPC2  as implemented at the CIPRES Science Gateway . To evaluate the support for interior nodes, 1000 replicates of the bootstrap were performed .
A complete 18S rDNA transcript was recovered from the specimens sequenced for this study via a BLAST search of EdwardsiellaBase using N. vectensis 18S rDNA as a query sequence. This 18S sequence was then aligned to published 18S sequences for eight other edwardsiid anemones using the default parameters of MUSCLE . Gaps and poorly-aligned regions were removed with Gblocks . The edited alignment is available in Additional file 1. A maximum-likelihood phylogeny was estimated from this edited alignment using the default parameters of RaxML-HPC2  as implemented at the CIPRES Science Gateway . To evaluate the support for interior nodes, 1000 replicates of the bootstrap were performed .
For all protein families examined here, we used MEME (Multiple Expectation Maximization for Motif Elicitation; ) to identify conserved motifs in orthologs and paralogs from the various species sampled. Motif searches were performed under the following settings: maximum number of motifs = 10; occurrences of a single motif = any number; minimum length of a motif = 5 amino acids; maximum length of a motif = 100. Conserved motifs are depicted in the relevant figures to the right of each gene’s name (Figures 10, 12; Additional file 8).
EdwardsiellaBase is a relational database constructed in PostgreSQL (version 8.4.4). It houses the E. lineata contigs generated in this study in addition to the results from a number of bioinformatics analyses performed on these contigs. The database structure and entity relationships are depicted in Additional file 9. Files to construct the database were prepared and parsed from resulting data, and available data from NCBI, Expasy, and amiGO. Web pages are generated in real time using Python scripts that query the database through the pgdb module for Python. The BLAST suite of programs (v. 2.2.24+) is installed on the server, and is run with a query against specific BLAST-formatted databases using the subprocess module of Python. The raw sequencing reads were aligned to the assembled contigs and preloaded into a file structure that allows the user to quickly locate and display alignment to a contig of interest through JBrowse (v. 1.7.6; [69, 70]).
This research was supported by NSF grants MCB-0924749 and IOS-0818831 to JRF. DJS was supported by training grant NIH/NIGMS F31 GM095289-01 to JRF. Data generation and analysis were also supported by the budget of “Marine Genomics,” a course in the Boston University Marine Semester. AMR received funding from the University of North Carolina at Charlotte. We are grateful to T. Gilmore for helpful comments on the manuscript. We would also like to thank Kevin Peterson for advice on the evolutionary divergence dating, and two annonymous reviewers, whose comments improved this manuscript.
- Price PW: Evolutionary biology of parasites. Monogr Pop Biol. 1980, 15: 1-237.Google Scholar
- Windsor DA: Most of the species on Earth are parasites. Int J Parasitol. 1998, 28 (12): 1939-1941. 10.1016/S0020-7519(98)00153-2.PubMedGoogle Scholar
- Howard RS, Lively CM: Parasitism, mutation accumulation and the maintenance of sex. Nature. 1994, 367 (6463): 554-557. 10.1038/367554a0.PubMedGoogle Scholar
- Lively CM: Host-parasite coevolution and sex. Bioscience. 1996, 46 (2): 107-114. 10.2307/1312813.Google Scholar
- Morran LT, Schmidt OG, Gelarden IA, Parrish RC, Lively CM: Running with the Red Queen: host-parasite coevolution selects for biparental sex. Science. 2011, 333 (6039): 216-218. 10.1126/science.1206360.PubMed CentralPubMedGoogle Scholar
- Schulenburg H, Kurtz J, Moret Y, Siva-Jothy MT: Introduction. Ecological immunology. Phil Trans Royal Soc London B. 2009, 364 (1513): 3-14. 10.1098/rstb.2008.0249.Google Scholar
- Lafferty K, Morris A: Altered behavior of parasitized killifish increases susceptibility to predation by bird final hosts. Ecology. 1996, 77 (5): 1390-1397. 10.2307/2265536.Google Scholar
- Coltman DW, Pilkington JG, Smith JA, Pemberton JM: Parasite-mediated selection against inbred Soay sheep in a free-living, island population. Evolution. 1999, 53: 1259-1267. 10.2307/2640828.Google Scholar
- Moore SL, Wilson K: Parasites as a viability cost of sexual selection in natural populations of mammals. Science. 2002, 297 (5589): 2015-2018. 10.1126/science.1074196.PubMedGoogle Scholar
- Lafferty K: How environmental stress affects the impacts of parasites. Limnol Oceanog. 1999, 44: 925-931. 10.4319/lo.1999.44.3_part_2.0925.Google Scholar
- Smith KF, Sax DF, Lafferty KD: Evidence for the role of infectious disease in species extinction and endangerment. Cons Biol. 2006, 20 (5): 1349-1357. 10.1111/j.1523-1739.2006.00524.x.Google Scholar
- Poulin R: Evolutionary Ecology of Parasites. 2006, Princeton, NJ: Princeton University Press, 2Google Scholar
- Poulin R, Morand S: Parasite Biodiversity. 2004, Washington, D.C.: Smithsonian Institution Scholarly PressGoogle Scholar
- Grbić M, Strand MR: Shifts in the life history of parasitic wasps correlate with pronounced alterations in early development. Proc Natl Acad Sci U S A. 1998, 95 (3): 1097-1101. 10.1073/pnas.95.3.1097.PubMed CentralPubMedGoogle Scholar
- Brooks DR: Lessons from a quiet classic. J Parasitol. 2003, 89 (5): 878-885. 10.1645/GE-3226DF.PubMedGoogle Scholar
- Crowell S: An edwardsiid larva parasitic in Mnemiopsis. Coelenterate Ecology and Behavior. Edited by: Mackie GO. 1976, New York: Plenum Press, 247-250.Google Scholar
- Reitzel AM, Sullivan JC, Brown BK, Chin DW, Cira EK, Edquist SK, Genco BM, Joseph OC, Kaufman CA, Kovitvongsa K, et al: Ecological and developmental dynamics of a host-parasite system involving a sea anemone and two ctenophores. J Parasitol. 2007, 93 (6): 1392-1402. 10.1645/GE-1250.1.PubMedGoogle Scholar
- Selander E, Møller LF, Sundberg P, Tiselius P: Parasitic anemone infects the invasive ctenophore Mnemiopsis leidyi in the North East Atlantic. Biol Invasions. 2010, 12: 1003-1009. 10.1007/s10530-009-9552-y.Google Scholar
- Reitzel AM, Sullivan JC, Finnerty JR: Qualitative shift to indirect development in the parasitic sea anemone Edwardsiella lineata. Integr Comp Biol. 2006, 46 (6): 827-837. 10.1093/icb/icl032.PubMedGoogle Scholar
- Reitzel A, Daly M, Sullivan J, Finnerty J: Comparative anatomy of developmental and parasitic stages in the life cycle of the lined sea anemone Edwardsiella lineata. J Parasitol. 2009, 95 (1): 100-112. 10.1645/GE-1623.1.PubMedGoogle Scholar
- Darling JA, Reitzel AR, Burton PM, Mazza ME, Ryan JF, Sullivan JC, Finnerty JR: Rising starlet: the starlet sea anemone. Nematostella vectensis. Bioessays. 2005, 27 (2): 211-221. 10.1002/bies.20181.PubMedGoogle Scholar
- Wolenski FS, Layden MJ, Martindale MQ, Gilmore TD, Finnerty JR: Characterizing the spatiotemporal expression of RNAs and proteins in the starlet sea anemone, Nematostella vectensis. Nat Protoc. 2013, 8 (5): 900-915. 10.1038/nprot.2013.014.PubMedGoogle Scholar
- Stefanik DJ, Friedman LE, Finnerty JR: Collecting, rearing, spawning and inducing regeneration of the starlet sea anemone, Nematostella vectensis. Nat Protoc. 2013, 8 (5): 916-923. 10.1038/nprot.2013.044.PubMedGoogle Scholar
- Stefanik DJ, Wolenski FS, Friedman LE, Gilmore TD, Finnerty JR: Isolation of DNA, RNA and protein from the starlet sea anemone Nematostella vectensis. Nat Protoc. 2013, 8 (5): 892-899. 10.1038/nprot.2012.151.PubMedGoogle Scholar
- Daly M: A systematic revision of the Edwardsiidae (Cnidaria, Anthozoa). Invert Biol. 2002, 2002 (3): 212-225.Google Scholar
- Zerbino DR, Birney E: Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Biotechfor. 2008, 18 (5): 821-829.Google Scholar
- Schulz MH, Zerbino DR, Vingron M, Birney E: Oases: robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics. 2012, 28 (8): 1086-1092. 10.1093/bioinformatics/bts094.PubMed CentralPubMedGoogle Scholar
- Shinzato C, Shoguchi E, Kawashima T, Hamada M, Hisata K, Tanaka M, Fujie M, Fujiwara M, Koyanagi R, Ikuta T, et al: Using the Acropora digitifera genome to understand coral responses to environmental change. Nature. 2011, 476 (7360): 320-323. 10.1038/nature10249.PubMedGoogle Scholar
- Park E, Hwang DS, Lee JS, Song JI, Seo TK, Won YJ: Estimation of divergence times in cnidarian evolution based on mitochondrial protein-coding genes and the fossil record. Mol Phylogenet Evol. 2012, 62 (1): 329-345. 10.1016/j.ympev.2011.10.008.PubMedGoogle Scholar
- Pick KS, Philippe H, Schreiber F, Erpenbeck D, Jackson DJ, Wrede P, Wiens M, Alie A, Morgenstern B, Manuel M, et al: Improved phylogenomic taxon sampling noticeably affects nonbilaterian relationships. Mol Biol Evol. 2010, 27 (9): 1983-1987. 10.1093/molbev/msq089.PubMed CentralPubMedGoogle Scholar
- Kerr AM: Molecular and morphological supertree of stony corals (Anthozoa: Scleractinia) using matrix representation parsimony. Biol Rev Camb Philos Soc. 2005, 80 (4): 543-558. 10.1017/S1464793105006780.PubMedGoogle Scholar
- Daly M, Fautin DG, Cappola VA: Systematics of the Hexcorallia (Cnidaria: Anthozoa). Zool J Linn Soc. 2003, 139: 419-437. 10.1046/j.1096-3642.2003.00084.x.Google Scholar
- Daly M, Lipscomb DL, Allard MW: A simple test: evaluating explanations for the relative simplicity of the Edwardsiidae (Cnidaria: Anthozoa). Evolution. 2002, 56 (3): 502-510.PubMedGoogle Scholar
- Rodriguez E, Daly M: Phylogenetic relationships among deep-sea and chemosynthetic sea anemones: actinoscyphiidae and actinostolidae (Actiniaria: Mesomyaria). PloS One. 2010, 5 (6): e10958-10.1371/journal.pone.0010958.PubMed CentralPubMedGoogle Scholar
- Daly M, Chaudhuri A, Gusmao L, Rodriguez E: Phylogenetic relationships among sea anemones (Cnidaria: Anthozoa: Actiniaria). Mol Phylogenet Evol. 2008, 48 (1): 292-301. 10.1016/j.ympev.2008.02.022.PubMedGoogle Scholar
- Berntson EA, France SC, Mullineaux LS: Phylogenetic relationships within the class Anthozoa (phylum Cnidaria) based on nuclear 18S rDNA sequences. Mol Phylogenet Evol. 1999, 13 (2): 417-433. 10.1006/mpev.1999.0649.PubMedGoogle Scholar
- Peterson KJ, Lyons JB, Nowak KS, Takacs CM, Wargo MJ, McPeek MA: Estimating metazoan divergence times with a molecular clock. Proc Natl Acad Sci USA. 2004, 101 (17): 6536-6541. 10.1073/pnas.0401670101.PubMed CentralPubMedGoogle Scholar
- Rota-Stabelli O, Campbell L, Brinkmann H, Edgecombe GD, Longhorn SJ, Peterson KJ, Pisani D, Philippe H, Telford MJ: A congruent solution to arthropod phylogeny: phylogenomics, microRNAs and morphology support monophyletic Mandibulata. Proceedings Biological sciences/The Royal Society. 2011, 278 (1703): 298-306. 10.1098/rspb.2010.0590.PubMed CentralPubMedGoogle Scholar
- Sperling EA, Robinson JM, Pisani D, Peterson KJ: Where’s the glass? Biomarkers, molecular clocks, and microRNAs suggest a 200-Myr missing Precambrian fossil record of siliceous sponge spicules. Geobiology. 2010, 8 (1): 24-36. 10.1111/j.1472-4669.2009.00225.x.PubMedGoogle Scholar
- Maloof AC, et al: The earliest Cambrian record of animals and ocean geochemical change. Geol Soc Am Bull. 2010, 122: 1731-10.1130/B30346.1.Google Scholar
- Xingliang Zhang WL, Yuanlong Z: Cambrian Burgess Shale-type Lagerstatten in South China: distribution and significance. Gondwana Res. 2008, 14 (1–2): 255-262.Google Scholar
- Peterson KJ, Cotton JA, Gehling JG, Pisani D: The Ediacaran emergence of bilaterians: congruence between the genetic and the geological fossil records. Philos T Roy Soc B. 2008, 363 (1496): 1435-1443. 10.1098/rstb.2007.2233.Google Scholar
- Paulyn Cartwright SLH, Hendricks JR, Jarrard RD, Marques AC, Collins AG, Lieberman BS: Exceptionally preserved jellyfishes from the Middle Cambrian. PloS one. 2007, 2 (10): e1121-10.1371/journal.pone.0001121.PubMed CentralPubMedGoogle Scholar
- Benton MJ, Donoghue PC: Paleontological evidence to date the tree of life. Mol Biol Evol. 2007, 24 (1): 26-53.PubMedGoogle Scholar
- Xian-Guang Hou GS, Jie Z, Xiao-Ya M: Cambrian anemones with preserved soft tissue from the Chengjiang biota, China. Lethaia. 2005, 38 (3): 193-203. 10.1080/00241160510013295.Google Scholar
- Peterson KJ, Butterfield NJ: Origin of the Eumetazoa: testing ecological predictions of molecular clocks against the Proterozoic fossil record. Proc Natl Acad Sci U S A. 2005, 102 (27): 9547-9552. 10.1073/pnas.0503660102.PubMed CentralPubMedGoogle Scholar
- Muller KJ, Walossek D, Zakharov A: Orsten' type phosphatized soft-integument preservation and a new record from the Middle Cambrian Kuonamka Formation in Siberia. N Jb Geol Paläontol Abh. 1997, 197 (101): 1-118.Google Scholar
- Erwin DH, Laflamme M, Tweedt SM, Sperling EA, Pisani D, Peterson KJ: The Cambrian conundrum: early divergence and later ecological success in the early history of animals. Science. 2011, 334 (6059): 1091-1097. 10.1126/science.1206375.PubMedGoogle Scholar
- Putnam NH, Srivastava M, Hellsten U, Dirks B, Chapman J, Salamov A, Terry A, Shapiro H, Lindquist E, Kapitonov VV, et al: Sea anemone genome reveals ancestral eumetazoan gene repertoire and genomic organization. Science. 2007, 317 (5834): 86-94. 10.1126/science.1139158.PubMedGoogle Scholar
- Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M: KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic acids research. 1999, 27 (1): 29-34. 10.1093/nar/27.1.29.PubMed CentralPubMedGoogle Scholar
- Letunic I, Yamada T, Kanehisa M, Bork P: iPath: interactive exploration of biochemical pathways and networks. Trends Biochem Sci. 2008, 33 (3): 101-103. 10.1016/j.tibs.2008.01.001.PubMedGoogle Scholar
- Kusserow A, Pang K, Sturm C, Hrouda M, Lentfer J, Schmidt HA, Technau U, von Haeseler A, Hobmayer B, Martindale MQ, et al: Unexpected complexity of the Wnt gene family in a sea anemone. Nature. 2005, 433 (7022): 156-160. 10.1038/nature03158.PubMedGoogle Scholar
- Srivastava M, Larroux C, Lu DR, Mohanty K, Chapman J, Degnan BM, Rokhsar DS: Early evolution of the LIM homeobox gene family. BMC Biol. 2010, 8: 4-10.1186/1741-7007-8-4.PubMed CentralPubMedGoogle Scholar
- Ryan JF, Burton PM, Mazza ME, Kwong GK, Mullikin JC, Finnerty JR: The cnidarian-bilaterian ancestor possessed at least 56 homeoboxes: evidence from the starlet sea anemone, Nematostella vectensis. Genome Biol. 2006, 7 (7): R64-10.1186/gb-2006-7-7-r64.PubMed CentralPubMedGoogle Scholar
- Reitzel AM, Tarrant AM: Nuclear receptor complement of the cnidarian Nematostella vectensis: phylogenetic relationships and developmental expression patterns. BMC Evol Biol. 2009, 9: 230-10.1186/1471-2148-9-230.PubMed CentralPubMedGoogle Scholar
- Zenkert C, Takahashi T, Diesner MO, Ozbek S: Morphological and molecular analysis of the Nematostella vectensis cnidom. PloS one. 2011, 6 (7): e22725-10.1371/journal.pone.0022725.PubMed CentralPubMedGoogle Scholar
- Simionato E, Ledent V, Richards G, Thomas-Chollier M, Kerner P, Coornaert D, Degnan BM, Vervoort M: Origin and diversification of the basic helix-loop-helix gene family in metazoans: insights from comparative genomics. BMC Evol Biol. 2007, 7: 33-10.1186/1471-2148-7-33.PubMed CentralPubMedGoogle Scholar
- Reitzel AM, Sullivan JC, Finnerty JR: Discovering SNPs in protein coding regions with StellaSNP: Illustrating the characterization and geographic distribution of polymorphisms in the estuarine anemone Nematostella vectensis. Estuar Coast. 2010, 33 (4): 930-943. 10.1007/s12237-009-9231-3.Google Scholar
- Magie CR, Pang K, Martindale MQ: Genomic inventory and expression of Sox and Fox genes in the cnidarian Nematostella vectensis. Dev Genes Evol. 2005, 215 (12): 618-630. 10.1007/s00427-005-0022-y.PubMedGoogle Scholar
- Sullivan JC, Wolenski FS, Reitzel AM, French CE, Traylor-Knowles N, Gilmore TD, Finnerty JR: Two alleles of NF-kappaB in the sea anemone Nematostella vectensis are widely dispersed in nature and encode proteins with distinct activities. PloS one. 2009, 4 (10): e7311-10.1371/journal.pone.0007311.PubMed CentralPubMedGoogle Scholar
- Wolenski FS, Bradham CA, Finnerty JR, Gilmore TD: NF-kappaB is required for cnidocyte development in the sea anemone Nematostella vectensis. Dev Biol. 2013, 373 (1): 205-215. 10.1016/j.ydbio.2012.10.004.PubMedGoogle Scholar
- Wolenski FS, Chandani S, Stefanik DJ, Jiang N, Chu E, Finnerty JR, Gilmore TD: Two polymorphic residues account for the differences in DNA binding and transcriptional activation by NF-kappaB proteins encoded by naturally occurring alleles in Nematostella vectensis. J Mol Evol. 2011, 73 (5–6): 325-336.PubMedGoogle Scholar
- Wolenski FS, Garbati MR, Lubinski TJ, Traylor-Knowles N, Dresselhaus E, Stefanik DJ, Goucher H, Finnerty JR, Gilmore TD: Characterization of the core elements of the NF-kappaB signaling pathway of the sea anemone Nematostella vectensis. Mol Cell Biol. 2011, 31 (5): 1076-1087. 10.1128/MCB.00927-10.PubMed CentralPubMedGoogle Scholar
- Sullivan JC, Kalaitzidis D, Gilmore TD, Finnerty JR: Rel homology domain-containing transcription factors in the cnidarian Nematostella vectensis. Dev Genes Evol. 2007, 217 (1): 63-72. 10.1007/s00427-006-0111-6.PubMedGoogle Scholar
- Gilmore TD, Wolenski FS: NF-kappaB: where did it come from and why?. Immunol Rev. 2012, 246 (1): 14-35. 10.1111/j.1600-065X.2012.01096.x.PubMedGoogle Scholar
- Sullivan JC, Ryan JF, Mullikin JC, Finnerty JR: Conserved and novel Wnt clusters in the basal eumetazoan Nematostella vectensis. Dev Genes Evol. 2007, 217 (3): 235-239. 10.1007/s00427-007-0136-5.PubMedGoogle Scholar
- Traylor-Knowles N, Granger BR, Lubinski T, Parikh JR, Garamszegi S, Xia Y, Marto JA, Kaufman L, Finnerty JR: Production of a reference transcriptome and a transcriptomic database (PocilloporaBase) for the cauliflower coral, Pocillopora damicornis. BMC Genomics. 2011, 12 (1): 585-10.1186/1471-2164-12-585.PubMed CentralPubMedGoogle Scholar
- Langmead B, Salzberg SL: Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012, 9 (4): 357-359. 10.1038/nmeth.1923.PubMed CentralPubMedGoogle Scholar
- Skinner ME, Uzilov AV, Stein LD, Mungall CJ, Holmes IH: JBrowse: a next-generation genome browser. Biotechfor. 2009, 19 (9): 1630-1638.Google Scholar
- Westesson O, Skinner M, Holmes I: Visualizing next-generation sequencing data with JBrowse. Brief Bioinform. 2013, 14 (2): 172-177. 10.1093/bib/bbr078.PubMed CentralPubMedGoogle Scholar
- Krishna S, Nair A, Cheedipudi S, Poduval D, Dhawan J, Palakodeti D, Ghanekar Y: Deep sequencing reveals unique small RNA repertoire that is regulated during head regeneration in Hydra magnipapillata. Nucleic Acids Res. 2013, 41 (1): 599-616. 10.1093/nar/gks1020.PubMed CentralPubMedGoogle Scholar
- Lehnert EM, Burriesci MS, Pringle JR: Developing the anemone Aiptasia as a tractable model for cnidarian-dinoflagellate symbiosis: the transcriptome of aposymbiotic A. pallida. BMC Genomics. 2012, 13: 271-10.1186/1471-2164-13-271.PubMed CentralPubMedGoogle Scholar
- Meyer E, Aglyamova GV, Wang S, Buchanan-Carter J, Abrego D, Colbourne JK, Willis BL, Matz MV: Sequencing and de novo analysis of a coral larval transcriptome using 454 GSFlx. BMC Genomics. 2009, 10: 219-10.1186/1471-2164-10-219.PubMed CentralPubMedGoogle Scholar
- Moya A, Huisman L, Ball EE, Hayward DC, Grasso LC, Chua CM, Woo HN, Gattuso JP, Foret S, Miller DJ: Whole transcriptome analysis of the coral Acropora millepora reveals complex responses to CO(2)-driven acidification during the initiation of calcification. Mol Ecol. 2012, 21 (10): 2440-2454. 10.1111/j.1365-294X.2012.05554.x.PubMedGoogle Scholar
- Polato NR, Vera JC, Baums IB: Gene discovery in the threatened elkhorn coral: 454 sequencing of the Acropora palmata transcriptome. PloS one. 2011, 6 (12): e28634-10.1371/journal.pone.0028634.PubMed CentralPubMedGoogle Scholar
- Portune KJ, Voolstra CR, Medina M, Szmant AM: Development and heat stress-induced transcriptomic changes during embryogenesis of the scleractinian coral Acropora palmata. Mar Genom. 2010, 3 (1): 51-62. 10.1016/j.margen.2010.03.002.Google Scholar
- Sabourault C, Ganot P, Deleury E, Allemand D, Furla P: Comprehensive EST analysis of the symbiotic sea anemone, Anemonia viridis. BMC Genomics. 2009, 10: 333-10.1186/1471-2164-10-333.PubMed CentralPubMedGoogle Scholar
- Soza-Ried J, Hotz-Wagenblatt A, Glatting KH, del Val C, Fellenberg K, Bode HR, Frank U, Hoheisel JD, Frohme M: The transcriptome of the colonial marine hydroid Hydractinia echinata. FEBS J. 2010, 277 (1): 197-209. 10.1111/j.1742-4658.2009.07474.x.PubMedGoogle Scholar
- Sun J, Chen Q, Lun JCY, Xu JL, Qiu JW: PcarnBase: development of a transcriptomic database for the brain coral platygyra carnosus. Mar Biotechnol. 2013, 15 (2): 244-251. 10.1007/s10126-012-9482-z.PubMedGoogle Scholar
- Sunagawa S, Wilson EC, Thaler M, Smith ML, Caruso C, Pringle JR, Weis VM, Medina M, Schwarz JA: Generation and analysis of transcriptomic resources for a model system on the rise: the sea anemone Aiptasia pallida and its dinoflagellate endosymbiont. BMC Genomics. 2009, 10: 258-10.1186/1471-2164-10-258.PubMed CentralPubMedGoogle Scholar
- Siebert S, Robinson MD, Tintori SC, Goetz F, Helm RR, Smith SA, Shaner N, Haddock SH, Dunn CW: Differential gene expression in the siphonophore Nanomia bijuga (Cnidaria) assessed with multiple next-generation sequencing workflows. PloS one. 2011, 6 (7): e22953-10.1371/journal.pone.0022953.PubMed CentralPubMedGoogle Scholar
- Vize PD: Transcriptome analysis of the circadian regulatory network in the coral Acropora millepora. Biol Bull. 2009, 216 (2): 131-137.PubMedGoogle Scholar
- Voolstra CR, Schwarz JA, Schnetzer J, Sunagawa S, Desalvo MK, Szmant AM, Coffroth MA, Medina M: The host transcriptome remains unaltered during the establishment of coral-algal symbioses. Mol Ecol. 2009, 18 (9): 1823-1833. 10.1111/j.1365-294X.2009.04167.x.PubMedGoogle Scholar
- Helm RR, Siebert S, Tulin S, Smith J, Dunn CW: Characterization of differential transcript abundance through time during Nematostella vectensis development. BMC Genomics. 2013, 14: 266-10.1186/1471-2164-14-266.PubMed CentralPubMedGoogle Scholar
- Tulin S, Aguiar D, Istrail S, Smith J: A quantitative reference transcriptome for Nematostella vectensis early embryonic development: a pipeline for de novo assembly in emerging model systems. Evo Devo. 2013, 4 (1): 16-Google Scholar
- Francis WR, Christianson LM, Kiko R, Powers ML, Shaner NC, D Haddock SH: A comparison across non-model animals suggests an optimal sequencing depth for de novo transcriptome assembly. BMC Genomics. 2013, 14: 167-10.1186/1471-2164-14-167.PubMed CentralPubMedGoogle Scholar
- Daly M: Taxonomy, anatomy, and histology of the lined sea anemone, Edwardsiella lineata (Verrill, 1873) (Cnidaria: Anthozoa: Edwardsiidae). Proc Biol Soc Wash. 2002, 115 (4): 868-877.Google Scholar
- Hinman VF, Nguyen AT, Cameron RA, Davidson EH: Developmental gene regulatory network architecture across 500 million years of echinoderm evolution. Proc Natl Acad Sci USA. 2003, 100 (23): 13356-13361. 10.1073/pnas.2235868100.PubMed CentralPubMedGoogle Scholar
- Romano LA, Wray GA: Conservation of Endo16 expression in sea urchins despite evolutionary divergence in both cis and trans-acting components of transcriptional regulation. Development. 2003, 130 (17): 4187-4199. 10.1242/dev.00611.PubMedGoogle Scholar
- Bu D, Yu K, Sun S, Xie C, Skogerbo G, Miao R, Xiao H, Liao Q, Luo H, Zhao G, et al: NONCODE v3.0: integrative annotation of long noncoding RNAs. Nucleic Acids Res. 2012, 40 (Database issue): D210-D215.PubMed CentralPubMedGoogle Scholar
- Tsai IJ, Zarowiecki M, Holroyd N, Garciarrubio A, Sanchez-Flores A, Brooks KL, Tracey A, Bobes RJ, Fragoso G, Sciutto E, et al: The genomes of four tapeworm species reveal adaptations to parasitism. Nature. 2013, 496 (7443): 57-63. 10.1038/nature12031.PubMed CentralPubMedGoogle Scholar
- Sullivan JC, Reitzel AM, Finnerty JR: Upgrades to StellaBase facilitate medical and genetic studies on the starlet sea anemone, Nematostella vectensis. Nucleic Acids Res. 2008, 36 (Database issue): D607-D611.PubMed CentralPubMedGoogle Scholar
- Sullivan JC, Ryan JF, Watson JA, Webb J, Mullikin JC, Rokhsar D, Finnerty JR: StellaBase: the Nematostella vectensis Genomics Database. Nucleic Acids Res. 2006, 34 (Database issue): D495-D499.PubMed CentralPubMedGoogle Scholar
- Ronquist F, Huelsenbeck JP: MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics. 2003, 19 (12): 1572-1574. 10.1093/bioinformatics/btg180.PubMedGoogle Scholar
- Lartillot N, Lepage T, Blanquart S: PhyloBayes 3: a Bayesian software package for phylogenetic reconstruction and molecular dating. Bioinformatics. 2009, 25 (17): 2286-2288. 10.1093/bioinformatics/btp368.PubMedGoogle Scholar
- Lartillot N, Rodrigue N, Stubbs D, Richer J: PhyloBayes MPI: phylogenetic reconstruction with infinite mixtures of profiles in a parallel environment. Syst Biol. 2013, 62 (4): 611-615. 10.1093/sysbio/syt022.PubMedGoogle Scholar
- Edgar RC: MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004, 32 (5): 1792-1797. 10.1093/nar/gkh340.PubMed CentralPubMedGoogle Scholar
- Abascal F, Zardoya R, Posada D: ProtTest: selection of best-fit models of protein evolution. Bioinformatics. 2005, 21 (9): 2104-2105. 10.1093/bioinformatics/bti263.PubMedGoogle Scholar
- Stamatakis A: RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics. 2006, 22 (21): 2688-2690. 10.1093/bioinformatics/btl446.PubMedGoogle Scholar
- The CIPRES Science Gateway: a community resource for phylogenetic analysis:http://www.phylo.org/index.php/portal/,
- Stamatakis A, Hoover P, Rougemont J: A rapid bootstrap algorithm for the RAxML Web servers. Syst Biol. 2008, 57 (5): 758-771. 10.1080/10635150802429642.PubMedGoogle Scholar
- Castresana J: Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol Biol Evol. 2000, 17: 540-552. 10.1093/oxfordjournals.molbev.a026334.PubMedGoogle Scholar
- Bailey TL: Discovering novel sequence motifs with MEME. Curr Protoc Bioinformatics. 2002, Chapter 2: Unit 2 4-Google Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.