Filter-feeding sponges and tiny gliding, pancake-like animals called placozoans are the only two major groups of animals that lack muscles, nerves and an internal gut. Sponges have historically been seen as the first to have branched off in animal phylogeny – the family tree of living organisms that shows how species are related. This is because it is assumed that they split from the other animals before features including muscles, nerves and internal guts evolved.

Sequences of their genetic material (the genome) support this view, although some argue that jellyfish-like animals called ctenophores branched first. One explanation for this disagreement is that ctenophores use different proportions of amino acids in their proteins, known as compositional heterogeneity. Computer algorithms that assume amino acid usage is the same universally throughout evolution may therefore place ctenophores incorrectly. In contrast, so far the only genome from a placozoan shows that they are equally closely related to jellyfish and corals (cnidarians) and bilaterians, which includes worms, insects and vertebrates.

To test whether this view of the first branches of the animal tree of life is correct, Laumer et al. included the genomes from several undescribed species of placozoans in a phylogenetic analysis. These analyses showed a relationship that had not previously been seen. The placozoans were the closest living relative to cnidarians. However, when looking at the level of genes rather than whole genomes, the more usual relationship of placozoans being equally related to cnidarians and bilaterians re-emerged. To resolve this conflict, Laumer et al. focused on the genes that had the least compositional heterogeneity. When doing this, the relationship appeared to be the newly identified one of placozoans being most closely related to cnidarians.

Researchers studying cnidarians often hope to find some clues as to how the complex features they seem to share with bilaterians originated. The findings of Laumer et al. may suggest that the ancestors of the placozoans did in fact have muscles, nerves and guts, but they lost these traits in favor of a simpler lifestyle. An alternative, but controversial possibility is that the ancestor of cnidarians and bilaterians was a simple organism like a placozoan, and the two evolved their complex traits independently. The findings show a complex picture of early animal evolution. Further study of placozoans may well clarify this picture.

The discovery (Schulze, 1883) and mid-20^th century rediscovery (Grell and Benwitz, 1971) of the enigmatic, amoeba-like placozoan Trichoplax adhaerens did much to ignite the imagination of zoologists interested in early animal evolution (Bütschli, 1884). As microscopic animals adapted to extracellular grazing on the biofilms over which they creep (Wenderoth, 1986), placozoans have a simple anatomy suited to exploit passive diffusion for many physiological needs, with only six morphological cell types discernible even to intensive microscopical scrutiny (Grell and Ruthmann, 1991; Smith et al., 2014), albeit a greater diversity of cell types is apparent through single-cell RNA-seq (Sebé-Pedrós, 2018a). They have no conventional muscular, digestive, or nervous systems, yet show tightly-coordinated behaviour regulated by peptidergic signaling (Smith et al., 2015; Senatore et al., 2017; Varoqueaux, 2018; Armon et al., 2018). In laboratory conditions, they proliferate through fission and somatic growth. Evidence for sexual reproduction remains elusive, despite genetic evidence of recombination (Srivastava et al., 2008) and descriptions of early abortive embryogenesis (Eitel et al., 2011; Grell, 1972), with the possibility that sexual phases of the life cycle may occur only under poorly understood field conditions (Pearse and Voigt, 2007; McFall-Ngai et al., 2013)

Given their simple, puzzling morphology and dearth of embryological clues, molecular data are crucial in placing placozoans phylogenetically. The position of Placozoa in the animal tree proved recalcitrant to early standard-marker analyses (Kim et al., 1999; Silva et al., 2007; Wallberg et al., 2004), although this paradigm did reveal a large degree of molecular diversity in placozoan isolates from around the globe, clearly indicating the existence of many cryptic species (Pearse and Voigt, 2007; Eitel et al., 2013; Signorovitch et al., 2007) with up to 27% genetic distance in 16S rRNA alignments (Eitel and Schierwater, 2010). An apparent answer to the question of placozoan affinities was provided by analysis of a nuclear genome assembly (Srivastava et al., 2008), which strongly supported a position as the sister group of a clade of Cnidaria + Bilateria (sometimes called Planulozoa). However, this effort also revealed a surprisingly bilaterian-like (Dunn et al., 2015) developmental gene toolkit in placozoans, a paradox for such a simple animal.

As metazoan phylogenetics has pressed onward into the genomic era, perhaps the largest controversy has been the debate over the identity of the sister group to the remaining metazoans, traditionally thought to be Porifera, but considered to be Ctenophora by Dunn et al (Dunn et al., 2008). and subsequently by additional studies (Hejnol et al., 2009; Moroz et al., 2014; NISC Comparative Sequencing Program et al., 2013; Whelan et al., 2015; Whelan et al., 2017). Others have suggested that this result arises from artifacts with potentially additive effects, such as inadequate taxon sampling, flawed matrix husbandry (undetected paralogy or contamination), and use of poorly fitting substitution models (Philippe et al., 2009; Pick et al., 2010; Pisani et al., 2015; Simion et al., 2017; Feuda et al., 2017). A third view has emphasized that using different sets of genes can lead to different conclusions, with only a small number sometimes sufficient to drive one result or another (Nosenko et al., 2013; Shen et al., 2017). This controversy, regardless of its eventual resolution, has spurred serious contemplation of possibly independent origins of several hallmark traits such as striated muscles, digestive systems, and nervous systems (Moroz et al., 2014; Dayraud et al., 2012; Hejnol and Martín-Durán, 2015; Liebeskind et al., 2017; Moroz and Kohn, 2016; Presnell et al., 2016; Steinmetz et al., 2012).

Driven by this controversy, new genomic and transcriptomic data from sponges, ctenophores, and metazoan outgroups have accrued, while new sequences and analyses focusing on the position of Placozoa have been slow to emerge. Here, we provide a novel test of the phylogenetic position of placozoans, adding draft genomes from three putative species that span the root of this clade’s known diversity (Eitel et al., 2013) (Table 1), and critically assessing the role of systematic error in placing of these enigmatic organisms (Laumer, 2018).

Orthology assignment on sets of predicted proteomes derived from 59 genome and transcriptome assemblies yielded 4294 gene trees with at least 20 sequences each, sampling all five major metazoan clades and outgroups, from which we obtained 1388 well-aligned orthologues. Within this set, individual maximum-likelihood (ML) gene trees were constructed, and a set of 430 most-informative orthologues were selected on the basis of tree-likeness scores (Misof et al., 2013). This yielded an amino-acid matrix of 73,547 residues with 37.55% gaps or missing data, with an average of 371.92 and 332.75 orthologues represented for Cnidaria and Placozoa, respectively (with a maximum of 383 orthologues present for the newly sequenced placozoan H4 clade representative; Figure 1).

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Our Bayesian analyses of this matrix place Cnidaria and Placozoa as sister groups with full posterior probability under the general site-heterogeneous CAT + GTR + Г4 model (Figure 1). Under ML inference with the C60 +LG + FO + R4 profile mixture model (Wang et al., 2018) (Figure 1—figure supplement 1), we again recover Cnidaria + Placozoa, albeit with more marginal resampling support. Both Bayesian and ML analyses show little internal branch diversity within Placozoa. Accordingly, deleting all newly-added placozoan genomes from our analysis has no effect on topology and only a marginal effect on support in ML analysis (Figure 1—figure supplement 2). Quartet-based concordance analyses (Zhou, 2017) show no evidence of strong phylogenetic conflicts among ML gene trees in this 430-gene set (Figure 1), although internode certainty metrics are close to 0 for many key clades including Cnidaria + Placozoa, indicating that support for some ancient relationships may be masked by gene-tree estimation errors, emerging only in combined analysis (Gatesy and Baker, 2005).

Compositional heterogeneity of amino-acid frequencies along the tree is a source of phylogenetic error not modelled by even complex site-heterogeneous substitution models such as CAT+GTR (Blanquart and Lartillot, 2008; Foster, 2004; Lartillot and Philippe, 2004; Lartillot et al., 2013). Furthermore, previous analyses (Nosenko et al., 2013) have shown that placozoans and choanoflagellates in particular, both of which taxa our matrix samples intensively, deviate strongly from the mean amino-acid composition of Metazoa, perhaps as a result of genomic GC content discrepancies. As a measure to at least partially ameliorate such nonstationary substitution, we recoded the amino-acid matrix into the 6 ‘Dayhoff’ categories, a common strategy previously shown to reduce the effect of compositional variation among taxa, albeit the Dayhoff-6 groups represent only one of many plausible recoding strategies, all of which sacrifice information (Feuda et al., 2017; Nesnidal et al., 2010; Rota-Stabelli et al., 2013; Susko and Roger, 2007). Analysis of this recoded matrix under the CAT + GTR model again recovered full support (pp = 1) for Cnidaria + Placozoa (Figure 2). Indeed, under Dayhoff-6 recoding, the only major change is in the relative positions of Ctenophora and Porifera, with the latter here constituting the sister group to all other animals with full support. Similar recoding-driven effects on relative positions of Porifera and Ctenophora have also been seen in other recent work (Feuda et al., 2017), and have been interpreted to indicate a role for compositional bias in misplacing Ctenophora as sister group to all other animals

Figure 2

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Many research groups, using good taxon sampling and genome-scale datasets, and even recently including data from a new divergent placozoan species (Whelan et al., 2017; Feuda et al., 2017; Eitel, 2017), have consistently reported strong support for Planulozoa under the CAT + GTR model. Indeed, when we construct a supermatrix from our predicted peptide catalogues using a different strategy, relying on complete sequences of 303 pan-eukaryote ‘Benchmarking Universal Single-Copy Orthologs’ (BUSCOs) (Simão et al., 2015), we also see full support in a CAT + GTR + Г analysis for Planulozoa, in both amino-acid (Figure 3a) and Dayhoff-6 recoded alphabets (Figure 3b). Which phylogeny is correct, and what process drives support for the incorrect topology? Posterior predictive tests, which compare the observed among-taxon usage of amino-acid frequencies to expected distributions simulated using the sampled posterior distribution and a single composition vector, may provide insight (Feuda et al., 2017; Lartillot and Philippe, 2004). Both the initial 430-gene matrix and the 303-gene BUSCO matrix fail these tests, but the BUSCO matrix fails it more profoundly, with z-scores (measuring mean-squared across-taxon heterogeneity) scoring in the range of 330–340, in contrast to the range of 176–187 seen in the 430-gene matrix (Table 2). Furthermore, inspecting z-scores for individual taxa in representative chains from both matrices shows that a large amount of this global difference in z-scores can be attributed to placozoans, with additional contributions from choanoflagellates and select isolated representatives of other clades (Figure 3C).

Figure 3

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As a final measure to describe the influence of compositional heterogeneity in this dataset, we applied a null-simulation test for compositional bias to each alignment in our set of 1388 orthologues. This test, which compares the real data to a null distribution of amino-acid frequencies simulated along assumed gene trees with a substitution model using a single composition vector, is less prone to Type II errors than the more conventional X (Grell and Benwitz, 1971) test (Foster, 2004). Remarkably, at a conservative significance threshold of α = 0.10, the majority (764 genes or ~55%) of this gene set is identified as compositionally biased by this test, highlighting the importance of using appropriate statistical tests to control this source of systematic error, rather than applying arbitrary heuristic cutoffs (Kück and Struck, 2014). Building informative matrices from gene sets on either side of this significance threshold, and again applying both CAT + GTR mixture models and ML profile mixtures, we see strong support for Cnidaria + Placozoa in the test-passing supermatrix, and conversely, strong support for Cnidaria + Bilateria in the test-failing supermatrix (Figure 4, Figure 4—figure supplement 1, Figure 4—figure supplement 2). Interestingly, in trees built through CAT + GTR + Г4 analysis of the test-failing supermatrix (Figure 4A,C), in both amino-acid and Dayhoff-6 alphabets, we also observe full support for Porifera as sister to all other animals. In contrast, analysis of this amino acid matrix under a profile mixture model recovers support for Ctenophora in this position (Figure 4—figure supplement 1), indicating that, at least for this alignment, compositional heterogeneity need not be invoked to explain why outcomes differ among analyses, as some have argued (Feuda et al., 2017): both CAT + GTR and the C60 +LG + FO + R4 profile mixture model assume a single composition vector over time, but the CAT + GTR model is better able to accommodate site-heterogeneous substitution patterns (Lartillot et al., 2013; Quang et al., 2008). In the context of this experiment, Dayhoff-6 recoding appears impactful only for the test-passing supermatrix (Figure 4B,D), where it obviates support for Ctenophora-sister (Figure 4B, Figure 4—figure supplement 2) in favour of (albeit, with marginal support) Porifera-sister (Figure 4D), and also diminishes support for Placozoa + Cnidaria (in contrast to the 430-gene matrix; Figure 2), perhaps reflecting the inherent information loss of using a reduced amino-acid alphabet for this relatively shorter matrix.

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A possible hidden variable related to the phylogenetic discordance we describe, the precise significance of which remains unclear, is mean trimmed alignment length: both the test-passing and the original 430-gene matrix are composed of considerably shorter alignments than the test-failing and the 303-gene BUSCO matrix (see Materials and methods). Indeed, alignment length has been previously shown to be predictive of a number of other metrics of phylogenetic relevance (Shen et al., 2016); the generality and directionality of such relationships in empirical datasets at varying scales of divergence is clearly worthy of further investigation.

The previously cryptic phylogenetic link between cnidarians and placozoans seen in gene sets less influenced by compositional bias will require further testing with other analyses and data modalities, such as rare genomic changes, which should be ever more visible as highly contiguous assemblies continue to be reported from non-bilaterian animals (Eitel et al., 2018; Kamm et al., 2018; Jiang, 2018; Leclère, 2018). However, if validated, this relationship must continue to raise questions on the homology of certain traits across non-bilaterians. Many workers, citing the incompletely known development (Eitel et al., 2011; Pearse and Voigt, 2007) and relatively bilaterian-like gene content of placozoans (Srivastava et al., 2008; Eitel, 2017), presume that these organisms must have a still-unobserved, more typical development and life cycle (DuBuc et al., 2018), or else are merely oddities that have experienced wholesale secondary simplification, having scant significance to any evolutionary path outside their own. Indeed, it is tempting to interpret this new phylogenetic position as further bolstering such hypotheses, as much work on cnidarian models in the evo-devo paradigm is predicated on the notion that cnidarians and bilaterians share, more or less, many homologous morphological features, viz. axial organization (Genikhovich and Technau, 2017; DuBuc et al., 2018), nervous systems (Liebeskind et al., 2017; Moroz and Kohn, 2016; Kelava et al., 2015; Kristan, 2016; Arendt et al., 2016), basement-membrane lined epithelia (Fidler et al., 2017; Leys and Riesgo, 2012), musculature (Steinmetz et al., 2012), embryonic germ-layer organisation (Steinmetz et al., 2017), and internal digestion (Presnell et al., 2016; Putnam et al., 2007; Hejnol and Martindale, 2008; Martindale and Hejnol, 2009). While we do not argue, as some have done (Schierwater, 2005; Syed and Schierwater, 2002), that placozoans resemble hypothetical metazoan ancestors, we hesitate to dismiss them a priori as irrelevant to understanding early bilaterian evolution in particular: although apparently simpler and less diverse, placozoans nonetheless have equal status to cnidarians as an immediate extant outgroup. Rather, we see value in testing assumed hypotheses of homology, character by character, by extending pairwise comparisons between bilaterians and cnidarians to include placozoans, an agenda which demands reducing the large disparity in embryological, physiological, and molecular genetic knowledge between these taxa, towards which recent progress has been made using both established methods such as in situ hybridization (DuBuc et al., 2018) and image analysis (Varoqueaux, 2018), as well as new technologies such as single-cell RNA-seq (Sebé-Pedrós, 2018a; Sebé-Pedrós et al., 2018b). Conversely, we emphasize another implication of this phylogeny: characters that can be validated as homologous at any level between Bilateria and Cnidaria must have originated earlier in animal evolution than previously appreciated, and should either cryptically occur in modern placozoans or else have been lost at some point in their ancestry. In this light, paleobiological scenarios of early animal evolution founded on inherently phylogenetically-informed interpretations of Ediacaran fossil forms (Cavalier-Smith, 2018; Cavalier-Smith, 2017; Dufour and McIlroy, 2018; Sperling and Vinther, 2010; Evans et al., 2017) and molecular clock estimates (Cunningham et al., 2017; dos Reis et al., 2015; Dohrmann and Wörheide, 2017; Erwin et al., 2011) may require re-examination.

SRA accession codes, where used, and all alternative sources for sequence data (e.g. individually hosted websites, personal communications), are listed above in the Materials and Methods section. A DataDryad accession is available at https://doi.org/10.5061/dryad.6cm1166, which makes available all helper scripts, orthogroups, multiple sequence alignments, phylogenetic program output, and raw host proteomes inputted to OrthoFinder. Metagenomic bins containing placozoan host contigs and raw RNA reads used to derive gene annotations from H4, H6 and H11 isolates are also provided in this accession. PhyloBayes. chain files, due to their large size, are separately accessioned at in Zenodo at https://doi.org/10.5281/zenodo.1197272. Raw RNA-seq data from three separate isolates of Placozoan haplotype H4 have been deposited in NCBI BioProject under accession code PRJNA505163.

1. 1. Laumer C
   2. Gruber-Vodicka H
   3. Hadfield MG
   4. Pearse VB
   5. Riesgo A
   6. Marioni JC
   7. Giribet G

   (2018) Dryad Digital Repository

   Data from: Placozoa and Cnidaria are sister taxa.

   https://doi.org/10.5061/dryad.6cm1166
2. 1. Laumer C
   2. Gruber-Vodicka H
   3. Hadfield MG
   4. Pearse VB
   5. Riesgo A
   6. Marioni JC
   7. Giribet G

   (2018) Zenodo

   PhyloBayes chain data from: Placozoa and Cnidaria are sister taxa.

   https://doi.org/10.5281/zenodo.1197272
3. 1. Christopher E Laumer
   2. Harald Gruber-Vodicka
   3. Michael G Hadfield
   4. Vicki B Pearse
   5. Ana Riesgo
   6. John C Marioni
   7. Gonzalo Giribet

   (2018) NCBI BioProject

   ID PRJNA505163. Amplified total RNA from Placozoan H4 isolates.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJNA505163

1. 1. Arendt D
   2. Tosches MA
   3. Marlow H

   (2016) From nerve net to nerve ring, nerve cord and brain--evolution of the nervous system

   Nature Reviews Neuroscience 17:61–72.

   https://doi.org/10.1038/nrn.2015.15

   • PubMed
   • Google Scholar
2. Preprint

   1. Armon S
   2. Bull MS
   3. Aranda-Diaz AJ
   4. Prakash M

   (2018) Ultra-fast cellular contractions in the epithelium of T. adhaerens and the ‘active cohesion’ hypothesis

   bioRxiv.

   https://doi.org/10.1101/258103

   • Google Scholar
3. 1. Ballesteros JA
   2. Hormiga G

   (2016) A new orthology assessment method for phylogenomic data: unrooted phylogenetic orthology

   Molecular Biology and Evolution 33:2117–2134.

   https://doi.org/10.1093/molbev/msw069

   • PubMed
   • Google Scholar
4. 1. Bankevich A
   2. Nurk S
   3. Antipov D
   4. Gurevich AA
   5. Dvorkin M
   6. Kulikov AS
   7. Lesin VM
   8. Nikolenko SI
   9. Pham S
   10. Prjibelski AD
   11. Pyshkin AV
   12. Sirotkin AV
   13. Vyahhi N
   14. Tesler G
   15. Alekseyev MA
   16. Pevzner PA

   (2012) SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing

   Journal of Computational Biology 19:455–477.

   https://doi.org/10.1089/cmb.2012.0021

   • PubMed
   • Google Scholar
5. 1. Blanquart S
   2. Lartillot N

   (2008) A site- and time-heterogeneous model of amino acid replacement

   Molecular Biology and Evolution 25:842–858.

   https://doi.org/10.1093/molbev/msn018

   • PubMed
   • Google Scholar
6. 1. Bütschli O

   (1884)

   Bemerkungen zur Gastraea-Theorie

   Morphologisches Jahrbuch 9:415–427.

   • Google Scholar
7. 1. Cannon JT
   2. Vellutini BC
   3. Smith J
   4. Ronquist F
   5. Jondelius U
   6. Hejnol A

   (2016) Xenacoelomorpha is the sister group to nephrozoa

   Nature 530:89–93.

   https://doi.org/10.1038/nature16520

   • PubMed
   • Google Scholar
8. 1. Cavalier-Smith T

   (2017)

   Origin of animal multicellularity: precursors, causes, consequences—the choanoflagellate/sponge transition, neurogenesis and the Cambrian explosion

   Philosophical Transactions of the Royal Society B: Biological Sciences 372:0476.

   • Google Scholar
9. 1. Cavalier-Smith T

   (2018) Vendozoa and selective forces on animal origin and early diversification: reply to Dufour and McIlroy (2017)

   Philosophical Transactions of the Royal Society B: Biological Sciences 373:0336.

   https://doi.org/10.1098/rstb.2017.0336

   • Google Scholar
10. 1. Celis JS
    2. Wibberg D
    3. Ramírez-Portilla C
    4. Rupp O
    5. Sczyrba A
    6. Winkler A
    7. Kalinowski J
    8. Wilke T

    (2018) Binning enables efficient host genome reconstruction in cnidarian holobionts

    GigaScience 7:.

    https://doi.org/10.1093/gigascience/giy075

    • PubMed
    • Google Scholar
11. 1. Cunningham JA
    2. Liu AG
    3. Bengtson S
    4. Donoghue PCJ

    (2017) The origin of animals: can molecular clocks and the fossil record be reconciled?

    BioEssays 39:e201600120.

    https://doi.org/10.1002/bies.201600120

    • Google Scholar
12. 1. Dayraud C
    2. Alié A
    3. Jager M
    4. Chang P
    5. Le Guyader H
    6. Manuel M
    7. Quéinnec E

    (2012) Independent specialisation of myosin II paralogues in muscle vs. non-muscle functions during early animal evolution: a ctenophore perspective

    BMC Evolutionary Biology 12:107.

    https://doi.org/10.1186/1471-2148-12-107

    • PubMed
    • Google Scholar
13. 1. Dobin A
    2. Davis CA
    3. Schlesinger F
    4. Drenkow J
    5. Zaleski C
    6. Jha S
    7. Batut P
    8. Chaisson M
    9. Gingeras TR

    (2013) STAR: ultrafast universal RNA-seq aligner

    Bioinformatics 29:15–21.

    https://doi.org/10.1093/bioinformatics/bts635

    • PubMed
    • Google Scholar
14. 1. Dohrmann M
    2. Wörheide G

    (2017) Dating early animal evolution using phylogenomic data

    Scientific Reports 7:.

    https://doi.org/10.1038/s41598-017-03791-w

    • PubMed
    • Google Scholar
15. 1. dos Reis M
    2. Thawornwattana Y
    3. Angelis K
    4. Telford MJ
    5. Donoghue PC
    6. Yang Z

    (2015) Uncertainty in the timing of origin of animals and the limits of precision in molecular timescales

    Current Biology 25:2939–2950.

    https://doi.org/10.1016/j.cub.2015.09.066

    • PubMed
    • Google Scholar
16. Preprint

    1. DuBuc T
    2. Bobkov Y
    3. Ryan JF
    4. Martindale M

    (2018) The radial expression of dorsal-ventral patterning genes in Placozoans, Trichoplax adhaerens. argues for an oral-aboral Axis.

    bioRxiv.

    https://doi.org/10.1101/345777

    • Google Scholar
17. 1. Dufour SC
    2. McIlroy D

    (2018) An ediacaran pre-placozoan alternative to the pre-sponge route towards the cambrian explosion of animal life: a comment on Cavalier-Smith 2017

    Philosophical Transactions of the Royal Society B: Biological Sciences 373:0148.

    https://doi.org/10.1098/rstb.2017.0148

    • Google Scholar
18. 1. Dunn CW
    2. Hejnol A
    3. Matus DQ
    4. Pang K
    5. Browne WE
    6. Smith SA
    7. Seaver E
    8. Rouse GW
    9. Obst M
    10. Edgecombe GD
    11. Sørensen MV
    12. Haddock SH
    13. Schmidt-Rhaesa A
    14. Okusu A
    15. Kristensen RM
    16. Wheeler WC
    17. Martindale MQ
    18. Giribet G

    (2008) Broad phylogenomic sampling improves resolution of the animal tree of life

    Nature 452:745–749.

    https://doi.org/10.1038/nature06614

    • PubMed
    • Google Scholar
19. 1. Dunn CW
    2. Leys SP
    3. Haddock SH

    (2015) The hidden biology of sponges and ctenophores

    Trends in Ecology & Evolution 30:282–291.

    https://doi.org/10.1016/j.tree.2015.03.003

    • PubMed
    • Google Scholar
20. 1. Eitel M
    2. Schierwater B

    (2010) The phylogeography of the placozoa suggests a taxon-rich phylum in tropical and subtropical waters

    Molecular Ecology 19:2315–2327.

    https://doi.org/10.1111/j.1365-294X.2010.04617.x

    • PubMed
    • Google Scholar
21. 1. Eitel M
    2. Guidi L
    3. Hadrys H
    4. Balsamo M
    5. Schierwater B

    (2011) New insights into placozoan sexual reproduction and development

    PLoS ONE 6:e19639.

    https://doi.org/10.1371/journal.pone.0019639

    • PubMed
    • Google Scholar
22. 1. Eitel M
    2. Osigus HJ
    3. DeSalle R
    4. Schierwater B

    (2013) Global diversity of the placozoa

    PLoS ONE 8:e57131.

    https://doi.org/10.1371/journal.pone.0057131

    • PubMed
    • Google Scholar
23. Preprint

    1. Eitel M

    (2017) A taxogenomics approach uncovers a new genus in the phylum placozoa

    bioRxiv.

    https://doi.org/10.1101/202119

    • Google Scholar
24. 1. Eitel M
    2. Francis WR
    3. Varoqueaux F
    4. Daraspe J
    5. Osigus HJ
    6. Krebs S
    7. Vargas S
    8. Blum H
    9. Williams GA
    10. Schierwater B
    11. Wörheide G

    (2018) Comparative genomics and the nature of placozoan species

    PLOS Biology 16:e2005359.

    https://doi.org/10.1371/journal.pbio.2005359

    • PubMed
    • Google Scholar
25. 1. Emms DM
    2. Kelly S

    (2015) OrthoFinder: solving fundamental biases in whole genome comparisons dramatically improves orthogroup inference accuracy

    Genome Biology 16:157.

    https://doi.org/10.1186/s13059-015-0721-2

    • PubMed
    • Google Scholar
26. 1. Erwin DH
    2. Laflamme M
    3. Tweedt SM
    4. Sperling EA
    5. Pisani D
    6. Peterson KJ

    (2011) The cambrian conundrum: early divergence and later ecological success in the early history of animals

    Science 334:1091–1097.

    https://doi.org/10.1126/science.1206375

    • PubMed
    • Google Scholar
27. 1. Evans SD
    2. Droser ML
    3. Gehling JG

    (2017) Highly regulated growth and development of the Ediacara macrofossil Dickinsonia costata

    PLOS ONE 12:e0176874.

    https://doi.org/10.1371/journal.pone.0176874

    • PubMed
    • Google Scholar
28. 1. Feuda R
    2. Dohrmann M
    3. Pett W
    4. Philippe H
    5. Rota-Stabelli O
    6. Lartillot N
    7. Wörheide G
    8. Pisani D

    (2017) Improved modeling of compositional heterogeneity supports sponges as sister to all other animals

    Current Biology 27:3864–3870.

    https://doi.org/10.1016/j.cub.2017.11.008

    • PubMed
    • Google Scholar
29. 1. Fidler AL
    2. Darris CE
    3. Chetyrkin SV
    4. Pedchenko VK
    5. Boudko SP
    6. Brown KL
    7. Gray Jerome W
    8. Hudson JK
    9. Rokas A
    10. Hudson BG

    (2017) Collagen IV and basement membrane at the evolutionary dawn of metazoan tissues

    eLife 6:e24176.

    https://doi.org/10.7554/eLife.24176

    • PubMed
    • Google Scholar
30. 1. Foster PG

    (2004) Modeling compositional heterogeneity

    Systematic Biology 53:485–495.

    https://doi.org/10.1080/10635150490445779

    • PubMed
    • Google Scholar
31. 1. Gatesy J
    2. Baker RH

    (2005) Hidden likelihood support in genomic data: can forty-five wrongs make a right?

    Systematic Biology 54:483–492.

    https://doi.org/10.1080/10635150590945368

    • PubMed
    • Google Scholar
32. 1. Genikhovich G
    2. Technau U

    (2017) On the evolution of bilaterality

    Development 144:3392–3404.

    https://doi.org/10.1242/dev.141507

    • PubMed
    • Google Scholar
33. 1. Grell KG
    2. Benwitz B

    (1971)

    Die Ultrastruktur Von Trichoplax adhaerens F. E. Schultze

    Cytobiologie 4:216–240.

    • Google Scholar
34. 1. Grell KG

    (1972) Eibildung und Furchung Von Trichoplax adhaerens F. E. Schulze (Placozoa)

    Zeitschrift F R Morphologie Der Tiere 73:297–314.

    https://doi.org/10.1007/BF00391925

    • Google Scholar
35. Book

    1. Grell KG
    2. Ruthmann A

    (1991)

    Placozoa. in Microscopic Anatomy of Invertebrates

    Book Depository.

    • Google Scholar
36. 1. Gurevich A
    2. Saveliev V
    3. Vyahhi N
    4. Tesler G

    (2013) QUAST: quality assessment tool for genome assemblies

    Bioinformatics 29:1072–1075.

    https://doi.org/10.1093/bioinformatics/btt086

    • PubMed
    • Google Scholar
37. 1. Haas BJ
    2. Papanicolaou A
    3. Yassour M
    4. Grabherr M
    5. Blood PD
    6. Bowden J
    7. Couger MB
    8. Eccles D
    9. Li B
    10. Lieber M
    11. MacManes MD
    12. Ott M
    13. Orvis J
    14. Pochet N
    15. Strozzi F
    16. Weeks N
    17. Westerman R
    18. William T
    19. Dewey CN
    20. Henschel R
    21. LeDuc RD
    22. Friedman N
    23. Regev A

    (2013) De novo transcript sequence reconstruction from RNA-seq using the trinity platform for reference generation and analysis

    Nature Protocols 8:1494–1512.

    https://doi.org/10.1038/nprot.2013.084

    • PubMed
    • Google Scholar
38. 1. Hejnol A
    2. Martín-Durán JM

    (2015) Getting to the bottom of anal evolution

    Zoologischer Anzeiger - a Journal of Comparative Zoology 256:61–74.

    https://doi.org/10.1016/j.jcz.2015.02.006

    • Google Scholar
39. 1. Hejnol A
    2. Martindale MQ

    (2008) Acoel development indicates the independent evolution of the bilaterian mouth and anus

    Nature 456:382–386.

    https://doi.org/10.1038/nature07309

    • PubMed
    • Google Scholar
40. 1. Hejnol A
    2. Obst M
    3. Stamatakis A
    4. Ott M
    5. Rouse GW
    6. Edgecombe GD
    7. Martinez P
    8. Baguna J
    9. Bailly X
    10. Jondelius U
    11. Wiens M
    12. Muller WEG
    13. Seaver E
    14. Wheeler WC
    15. Martindale MQ
    16. Giribet G
    17. Dunn CW

    (2009) Assessing the root of bilaterian animals with scalable phylogenomic methods

    Proceedings of the Royal Society B: Biological Sciences 276:4261–4270.

    https://doi.org/10.1098/rspb.2009.0896

    • Google Scholar
41. 1. Hoang DT
    2. Chernomor O
    3. von Haeseler A
    4. Minh BQ
    5. Vinh LS

    (2018) UFBoot2: improving the ultrafast bootstrap approximation

    Molecular Biology and Evolution 35:518–522.

    https://doi.org/10.1093/molbev/msx281

    • PubMed
    • Google Scholar
42. 1. Hoff KJ
    2. Lange S
    3. Lomsadze A
    4. Borodovsky M
    5. Stanke M

    (2016) BRAKER1: unsupervised RNA-Seq-Based genome annotation with GeneMark-ET and AUGUSTUS

    Bioinformatics 32:767–769.

    https://doi.org/10.1093/bioinformatics/btv661

    • PubMed
    • Google Scholar
43. Preprint

    1. Jiang J

    (2018) A hybrid de novo assembly of the sea pansy (Renilla muelleri) Genome

    bioRxiv.

    https://doi.org/10.1101/424614

    • Google Scholar
44. 1. Kamm K
    2. Osigus HJ
    3. Stadler PF
    4. DeSalle R
    5. Schierwater B

    (2018) Trichoplax genomes reveal profound admixture and suggest stable wild populations without bisexual reproduction

    Scientific Reports 8:11168.

    https://doi.org/10.1038/s41598-018-29400-y

    • PubMed
    • Google Scholar
45. 1. Kang DD
    2. Froula J
    3. Egan R
    4. Wang Z

    (2015) MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities

    PeerJ 3:e1165.

    https://doi.org/10.7717/peerj.1165

    • PubMed
    • Google Scholar
46. 1. Kelava I
    2. Rentzsch F
    3. Technau U

    (2015) Evolution of eumetazoan nervous systems: insights from cnidarians

    Philosophical Transactions of the Royal Society B: Biological Sciences 370:20150065.

    https://doi.org/10.1098/rstb.2015.0065

    • Google Scholar
47. 1. Kim J
    2. Kim W
    3. Cunningham CW

    (1999) A new perspective on lower metazoan relationships from 18S rDNA sequences

    Molecular Biology and Evolution 16:423–427.

    https://doi.org/10.1093/oxfordjournals.molbev.a026124

    • PubMed
    • Google Scholar
48. 1. Kristan WB

    (2016) Early evolution of neurons

    Current Biology 26:R949–R954.

    https://doi.org/10.1016/j.cub.2016.05.030

    • PubMed
    • Google Scholar
49. 1. Kück P
    2. Struck TH

    (2014) BaCoCa--a heuristic software tool for the parallel assessment of sequence biases in hundreds of gene and taxon partitions

    Molecular Phylogenetics and Evolution 70:94–98.

    https://doi.org/10.1016/j.ympev.2013.09.011

    • PubMed
    • Google Scholar
50. 1. Lartillot N
    2. Philippe H

    (2004) A bayesian mixture model for across-site heterogeneities in the amino-acid replacement process

    Molecular Biology and Evolution 21:1095–1109.

    https://doi.org/10.1093/molbev/msh112

    • PubMed
    • Google Scholar
51. 1. Lartillot N
    2. Rodrigue N
    3. Stubbs D
    4. Richer J

    (2013) PhyloBayes MPI: phylogenetic reconstruction with infinite mixtures of profiles in a parallel environment

    Systematic Biology 62:611–615.

    https://doi.org/10.1093/sysbio/syt022

    • PubMed
    • Google Scholar
52. 1. Laumer CE
    2. Hejnol A
    3. Giribet G

    (2015) Nuclear genomic signals of the ‘microturbellarian’ roots of platyhelminth evolutionary innovation

    eLife 4:e05503.

    https://doi.org/10.7554/eLife.05503

    • Google Scholar
53. 1. Laumer CE

    (2018) Inferring ancient relationships with genomic data: a commentary on current practices

    Integrative and Comparative Biology 58:623–639.

    https://doi.org/10.1093/icb/icy075

    • PubMed
    • Google Scholar
54. Preprint

    1. Leclère L

    (2018) The genome of the jellyfish Clytia hemisphaerica and the evolution of the cnidarian life-cycle

    bioRxiv.

    https://doi.org/10.1101/369959

    • Google Scholar
55. 1. Leys SP
    2. Riesgo A

    (2012) Epithelia, an evolutionary novelty of metazoans

    Journal of Experimental Zoology Part B: Molecular and Developmental Evolution 318:438–447.

    https://doi.org/10.1002/jez.b.21442

    • Google Scholar
56. 1. Liebeskind BJ
    2. Hofmann HA
    3. Hillis DM
    4. Zakon HH

    (2017) Evolution of animal neural systems

    Annual Review of Ecology, Evolution, and Systematics 48:377–398.

    https://doi.org/10.1146/annurev-ecolsys-110316-023048

    • Google Scholar
57. 1. Martindale MQ
    2. Hejnol A

    (2009) A developmental perspective: changes in the position of the blastopore during bilaterian evolution

    Developmental Cell 17:162–174.

    https://doi.org/10.1016/j.devcel.2009.07.024

    • PubMed
    • Google Scholar
58. 1. McFall-Ngai M
    2. Hadfield MG
    3. Bosch TCG
    4. Carey HV
    5. Domazet-Lošo T
    6. Douglas AE
    7. Dubilier N
    8. Eberl G
    9. Fukami T
    10. Gilbert SF
    11. Hentschel U
    12. King N
    13. Kjelleberg S
    14. Knoll AH
    15. Kremer N
    16. Mazmanian SK
    17. Metcalf JL
    18. Nealson K
    19. Pierce NE
    20. Rawls JF
    21. Reid A
    22. Ruby EG
    23. Rumpho M
    24. Sanders JG
    25. Tautz D
    26. Wernegreen JJ

    (2013) Animals in a bacterial world, a new imperative for the life sciences

    Proceedings of the National Academy of Sciences 110:3229–3236.

    https://doi.org/10.1073/pnas.1218525110

    • Google Scholar
59. 1. Misof B
    2. Meyer B
    3. von Reumont BM
    4. Kück P
    5. Misof K
    6. Meusemann K

    (2013) Selecting informative subsets of sparse supermatrices increases the chance to find correct trees

    BMC Bioinformatics 14:348.

    https://doi.org/10.1186/1471-2105-14-348

    • PubMed
    • Google Scholar
60. 1. Moroz LL
    2. Kocot KM
    3. Citarella MR
    4. Dosung S
    5. Norekian TP
    6. Povolotskaya IS
    7. Grigorenko AP
    8. Dailey C
    9. Berezikov E
    10. Buckley KM
    11. Ptitsyn A
    12. Reshetov D
    13. Mukherjee K
    14. Moroz TP
    15. Bobkova Y
    16. Yu F
    17. Kapitonov VV
    18. Jurka J
    19. Bobkov YV
    20. Swore JJ
    21. Girardo DO
    22. Fodor A
    23. Gusev F
    24. Sanford R
    25. Bruders R
    26. Kittler E
    27. Mills CE
    28. Rast JP
    29. Derelle R
    30. Solovyev VV
    31. Kondrashov FA
    32. Swalla BJ
    33. Sweedler JV
    34. Rogaev EI
    35. Halanych KM
    36. Kohn AB

    (2014) The ctenophore genome and the evolutionary origins of neural systems

    Nature 510:109–114.

    https://doi.org/10.1038/nature13400

    • PubMed
    • Google Scholar
61. 1. Moroz LL
    2. Kohn AB

    (2016) Independent origins of neurons and synapses: insights from ctenophores

    Philosophical Transactions of the Royal Society B: Biological Sciences 371:20150041.

    https://doi.org/10.1098/rstb.2015.0041

    • PubMed
    • Google Scholar
62. 1. Nesnidal MP
    2. Helmkampf M
    3. Bruchhaus I
    4. Hausdorf B

    (2010) Compositional heterogeneity and phylogenomic inference of metazoan relationships

    Molecular Biology and Evolution 27:2095–2104.

    https://doi.org/10.1093/molbev/msq097

    • PubMed
    • Google Scholar
63. 1. Nikolenko SI
    2. Korobeynikov AI
    3. Alekseyev MA

    (2013) BayesHammer: bayesian clustering for error correction in single-cell sequencing

    BMC Genomics 14:S7.

    https://doi.org/10.1186/1471-2164-14-S1-S7

    • PubMed
    • Google Scholar
64. 1. NISC Comparative Sequencing Program
    2. Ryan JF
    3. Pang K
    4. Schnitzler CE
    5. Nguyen AD
    6. Moreland RT
    7. Simmons DK
    8. Koch BJ
    9. Francis WR
    10. Havlak P
    11. Smith SA
    12. Putnam NH
    13. Haddock SH
    14. Dunn CW
    15. Wolfsberg TG
    16. Mullikin JC
    17. Martindale MQ
    18. Baxevanis AD

    (2013) The genome of the ctenophore Mnemiopsis leidyi and its implications for cell type evolution

    Science 342:1242592.

    https://doi.org/10.1126/science.1242592

    • PubMed
    • Google Scholar
65. 1. Nosenko T
    2. Schreiber F
    3. Adamska M
    4. Adamski M
    5. Eitel M
    6. Hammel J
    7. Maldonado M
    8. Müller WE
    9. Nickel M
    10. Schierwater B
    11. Vacelet J
    12. Wiens M
    13. Wörheide G

    (2013) Deep metazoan phylogeny: when different genes tell different stories

    Molecular Phylogenetics and Evolution 67:223–233.

    https://doi.org/10.1016/j.ympev.2013.01.010

    • PubMed
    • Google Scholar
66. 1. Pearse VB
    2. Voigt O

    (2007) Field biology of placozoans (Trichoplax): distribution, diversity, biotic interactions

    Integrative and Comparative Biology 47:677–692.

    https://doi.org/10.1093/icb/icm015

    • PubMed
    • Google Scholar
67. 1. Philippe H
    2. Derelle R
    3. Lopez P
    4. Pick K
    5. Borchiellini C
    6. Boury-Esnault N
    7. Vacelet J
    8. Renard E
    9. Houliston E
    10. Quéinnec E
    11. Da Silva C
    12. Wincker P
    13. Le Guyader H
    14. Leys S
    15. Jackson DJ
    16. Schreiber F
    17. Erpenbeck D
    18. Morgenstern B
    19. Wörheide G
    20. Manuel M

    (2009) Phylogenomics revives traditional views on deep animal relationships

    Current Biology 19:706–712.

    https://doi.org/10.1016/j.cub.2009.02.052

    • PubMed
    • Google Scholar
68. 1. Pick KS
    2. Philippe H
    3. Schreiber F
    4. Erpenbeck D
    5. Jackson DJ
    6. Wrede P
    7. Wiens M
    8. Alié A
    9. Morgenstern B
    10. Manuel M
    11. Wörheide G

    (2010) Improved phylogenomic taxon sampling noticeably affects nonbilaterian relationships

    Molecular Biology and Evolution 27:1983–1987.

    https://doi.org/10.1093/molbev/msq089

    • PubMed
    • Google Scholar
69. 1. Pisani D
    2. Pett W
    3. Dohrmann M
    4. Feuda R
    5. Rota-Stabelli O
    6. Philippe H
    7. Lartillot N
    8. Wörheide G

    (2015) Genomic data do not support comb jellies as the sister group to all other animals

    Proceedings of the National Academy of Sciences 112:15402–15407.

    https://doi.org/10.1073/pnas.1518127112

    • Google Scholar
70. 1. Presnell JS
    2. Vandepas LE
    3. Warren KJ
    4. Swalla BJ
    5. Amemiya CT
    6. Browne WE

    (2016) The presence of a functionally tripartite Through-Gut in ctenophora has implications for metazoan character trait evolution

    Current Biology 26:2814–2820.

    https://doi.org/10.1016/j.cub.2016.08.019

    • PubMed
    • Google Scholar
71. 1. Price MN
    2. Dehal PS
    3. Arkin AP

    (2010) FastTree 2--approximately maximum-likelihood trees for large alignments

    PLoS ONE 5:e9490.

    https://doi.org/10.1371/journal.pone.0009490

    • PubMed
    • Google Scholar
72. 1. Putnam NH
    2. Srivastava M
    3. Hellsten U
    4. Dirks B
    5. Chapman J
    6. Salamov A
    7. Terry A
    8. Shapiro H
    9. Lindquist E
    10. Kapitonov VV
    11. Jurka J
    12. Genikhovich G
    13. Grigoriev IV
    14. Lucas SM
    15. Steele RE
    16. Finnerty JR
    17. Technau U
    18. Martindale MQ
    19. Rokhsar DS

    (2007) Sea Anemone genome reveals ancestral eumetazoan gene repertoire and genomic organization

    Science 317:86–94.

    https://doi.org/10.1126/science.1139158

    • PubMed
    • Google Scholar
73. 1. Quang leS
    2. Gascuel O
    3. Lartillot N

    (2008) Empirical profile mixture models for phylogenetic reconstruction

    Bioinformatics 24:2317–2323.

    https://doi.org/10.1093/bioinformatics/btn445

    • PubMed
    • Google Scholar
74. 1. Rota-Stabelli O
    2. Lartillot N
    3. Philippe H
    4. Pisani D

    (2013) Serine codon-usage Bias in deep phylogenomics: pancrustacean relationships as a case study

    Systematic Biology 62:121–133.

    https://doi.org/10.1093/sysbio/sys077

    • PubMed
    • Google Scholar
75. 1. Schierwater B

    (2005) My favorite animal, Trichoplax adhaerens

    BioEssays 27:1294–1302.

    https://doi.org/10.1002/bies.20320

    • PubMed
    • Google Scholar
76. 1. Schulze FE

    (1883)

    Trichoplax adhaerens, nov. gen., nov. spec

    Zoologischer Anzeiger 6:92–97.

    • Google Scholar
77. 1. Sebé-Pedrós A

    (2018a)

    Early metazoan cell type diversity and the evolution of multicellular gene regulation. nat

    Ecology and Evolution 2:1176–1188.

    • Google Scholar
78. 1. Sebé-Pedrós A
    2. Saudemont B
    3. Chomsky E
    4. Plessier F
    5. Mailhé MP
    6. Renno J
    7. Loe-Mie Y
    8. Lifshitz A
    9. Mukamel Z
    10. Schmutz S
    11. Novault S
    12. Steinmetz PRH
    13. Spitz F
    14. Tanay A
    15. Marlow H

    (2018b) Cnidarian cell type diversity and regulation revealed by Whole-Organism Single-Cell RNA-Seq

    Cell 173:1520–1534.

    https://doi.org/10.1016/j.cell.2018.05.019

    • PubMed
    • Google Scholar
79. 1. Senatore A
    2. Reese TS
    3. Smith CL

    (2017) Neuropeptidergic integration of behavior in Trichoplax adhaerens, an animal without synapses

    The Journal of Experimental Biology 220:3381–3390.

    https://doi.org/10.1242/jeb.162396

    • PubMed
    • Google Scholar
80. 1. Shen XX
    2. Salichos L
    3. Rokas A

    (2016) A Genome-Scale investigation of how sequence, function, and Tree-Based gene properties influence phylogenetic inference

    Genome Biology and Evolution 8:2565–2580.

    https://doi.org/10.1093/gbe/evw179

    • PubMed
    • Google Scholar
81. 1. Shen X-X
    2. Hittinger CT
    3. Rokas A

    (2017) Contentious relationships in phylogenomic studies can be driven by a handful of genes

    Nature Ecology & Evolution 1:0126.

    https://doi.org/10.1038/s41559-017-0126

    • Google Scholar
82. 1. Signorovitch AY
    2. Buss LW
    3. Dellaporta SL

    (2007) Comparative genomics of large mitochondria in placozoans

    PLoS Genetics 3:e13.

    https://doi.org/10.1371/journal.pgen.0030013

    • PubMed
    • Google Scholar
83. 1. Silva FBda
    2. Muschner VC
    3. Bonatto SL

    (2007) Phylogenetic position of placozoa based on large subunit (LSU) and small subunit (SSU) rRNA genes

    Genetics and Molecular Biology 30:127–132.

    https://doi.org/10.1590/S1415-47572007000100022

    • Google Scholar
84. 1. Simão FA
    2. Waterhouse RM
    3. Ioannidis P
    4. Kriventseva EV
    5. Zdobnov EM

    (2015) BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs

    Bioinformatics 31:3210–3212.

    https://doi.org/10.1093/bioinformatics/btv351

    • Google Scholar
85. 1. Simion P
    2. Philippe H
    3. Baurain D
    4. Jager M
    5. Richter DJ
    6. Di Franco A
    7. Roure B
    8. Satoh N
    9. Quéinnec É
    10. Ereskovsky A
    11. Lapébie P
    12. Corre E
    13. Delsuc F
    14. King N
    15. Wörheide G
    16. Manuel M

    (2017) A large and consistent phylogenomic dataset supports sponges as the sister group to all other animals

    Current Biology 27:958–967.

    https://doi.org/10.1016/j.cub.2017.02.031

    • PubMed
    • Google Scholar
86. 1. Smith CL
    2. Varoqueaux F
    3. Kittelmann M
    4. Azzam RN
    5. Cooper B
    6. Winters CA
    7. Eitel M
    8. Fasshauer D
    9. Reese TS

    (2014) Novel cell types, neurosecretory cells, and body plan of the early-diverging metazoan Trichoplax adhaerens

    Current Biology 24:1565–1572.

    https://doi.org/10.1016/j.cub.2014.05.046

    • PubMed
    • Google Scholar
87. 1. Smith CL
    2. Pivovarova N
    3. Reese TS

    (2015) Coordinated feeding behavior in Trichoplax, an animal without synapses

    Plos One 10:e0136098.

    https://doi.org/10.1371/journal.pone.0136098

    • PubMed
    • Google Scholar
88. 1. Sperling EA
    2. Vinther J

    (2010) A placozoan affinity for dickinsonia and the evolution of late Proterozoic metazoan feeding modes

    Evolution & Development 12:201–209.

    https://doi.org/10.1111/j.1525-142X.2010.00404.x

    • PubMed
    • Google Scholar
89. 1. Srivastava M
    2. Begovic E
    3. Chapman J
    4. Putnam NH
    5. Hellsten U
    6. Kawashima T
    7. Kuo A
    8. Mitros T
    9. Salamov A
    10. Carpenter ML
    11. Signorovitch AY
    12. Moreno MA
    13. Kamm K
    14. Grimwood J
    15. Schmutz J
    16. Shapiro H
    17. Grigoriev IV
    18. Buss LW
    19. Schierwater B
    20. Dellaporta SL
    21. Rokhsar DS

    (2008) The Trichoplax genome and the nature of placozoans

    Nature 454:955–960.

    https://doi.org/10.1038/nature07191

    • PubMed
    • Google Scholar
90. 1. Steinmetz PR
    2. Kraus JE
    3. Larroux C
    4. Hammel JU
    5. Amon-Hassenzahl A
    6. Houliston E
    7. Wörheide G
    8. Nickel M
    9. Degnan BM
    10. Technau U

    (2012) Independent evolution of striated muscles in cnidarians and bilaterians

    Nature 487:231–234.

    https://doi.org/10.1038/nature11180

    • PubMed
    • Google Scholar
91. 1. Steinmetz PRH
    2. Aman A
    3. Kraus JEM
    4. Technau U

    (2017) Gut-like ectodermal tissue in a sea Anemone challenges germ layer homology

    Nature Ecology & Evolution 1:1535–1542.

    https://doi.org/10.1038/s41559-017-0285-5

    • PubMed
    • Google Scholar
92. 1. Strous M
    2. Kraft B
    3. Bisdorf R
    4. Tegetmeyer HE

    (2012) The binning of metagenomic contigs for microbial physiology of mixed cultures

    Frontiers in Microbiology 3:410.

    https://doi.org/10.3389/fmicb.2012.00410

    • PubMed
    • Google Scholar
93. 1. Susko E
    2. Roger AJ

    (2007) On reduced amino acid alphabets for phylogenetic inference

    Molecular Biology and Evolution 24:2139–2150.

    https://doi.org/10.1093/molbev/msm144

    • PubMed
    • Google Scholar
94. 1. Syed T
    2. Schierwater B

    (2002)

    Trichoplax adhaerens: discovered as a missing link, forgotten as a Hydrozoan, re-discovered as a key to metazoan evolution

    Vie Et Milieu 52:177–187.

    • Google Scholar
95. 1. Ter-Hovhannisyan V
    2. Lomsadze A
    3. Chernoff YO
    4. Borodovsky M

    (2008) Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training

    Genome Research 18:1979–1990.

    https://doi.org/10.1101/gr.081612.108

    • PubMed
    • Google Scholar
96. Preprint

    1. Varoqueaux F

    (2018) High cell diversity and complex peptidergic signalling underlie placozoan behaviour

    bioRxiv.

    https://doi.org/10.1101/360925

    • Google Scholar
97. 1. Wallberg A
    2. Thollesson M
    3. Farris JS
    4. Jondelius U

    (2004) The phylogenetic position of the comb jellies (Ctenophora) and the importance of taxonomic sampling

    Cladistics 20:558–578.

    https://doi.org/10.1111/j.1096-0031.2004.00041.x

    • Google Scholar
98. 1. Wang HC
    2. Minh BQ
    3. Susko E
    4. Roger AJ

    (2018) Modeling site heterogeneity with posterior mean site frequency profiles accelerates accurate phylogenomic estimation

    Systematic Biology 67:216–235.

    https://doi.org/10.1093/sysbio/syx068

    • PubMed
    • Google Scholar
99. 1. Wenderoth H

    (1986) Transepithelial cytophagy by Trichoplax adhaerens FE. Schulze (Placozoa) Feeding on Yeast.

     Z. Für Naturforschung 41:343–347.

    https://doi.org/10.1515/znc-1986-0316

    • Google Scholar
100. 1. Whelan NV
     2. Kocot KM
     3. Moroz LL
     4. Halanych KM

     (2015) Error, signal, and the placement of ctenophora sister to all other animals

     PNAS 112:5773–5778.

     https://doi.org/10.1073/pnas.1503453112

     • PubMed
     • Google Scholar
101. 1. Whelan NV
     2. Kocot KM
     3. Moroz TP
     4. Mukherjee K
     5. Williams P
     6. Paulay G
     7. Moroz LL
     8. Halanych KM

     (2017) Ctenophore relationships and their placement as the sister group to all other animals

     Nature Ecology & Evolution 1:1737–1746.

     https://doi.org/10.1038/s41559-017-0331-3

     • PubMed
     • Google Scholar
102. 1. Wu M
     2. Chatterji S
     3. Eisen JA

     (2012) Accounting for alignment uncertainty in phylogenomics

     PLoS One 7:e30288.

     https://doi.org/10.1371/journal.pone.0030288

     • PubMed
     • Google Scholar
103. 1. Yang Y
     2. Smith SA

     (2014) Orthology inference in nonmodel organisms using transcriptomes and low-coverage genomes: improving accuracy and matrix occupancy for phylogenomics

     Molecular Biology and Evolution 31:3081–3092.

     https://doi.org/10.1093/molbev/msu245

     • PubMed
     • Google Scholar
104. Preprint

     1. Zhou X

     (2017) Quartet-based computations of internode certainty provide accurate and robust measures of phylogenetic incongruence

     bioRxiv.

     https://doi.org/10.1101/168526

     • Google Scholar

Author details

1. Christopher E Laumer

   1. Wellcome Trust Sanger Institute, Hinxton, United Kingdom
   2. European Molecular Biology Laboratories-European Bioinformatics Institute, Hinxton, United Kingdom

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   claumer@ebi.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8097-8516

2. Harald Gruber-Vodicka

   Max Planck Institute for Marine Microbiology, Bremen, Germany

   Contribution

   Resources, Formal analysis, Supervision, Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

3. Michael G Hadfield

   Kewalo Marine Laboratory, Pacific Biosciences Research Center and the University of Hawaii-Manoa, Honolulu, United States

   Contribution

   Resources, Writing—review and editing

   Competing interests

   No competing interests declared

4. Vicki B Pearse

   Institute of Marine Sciences, University of California, Santa Cruz, United States

   Contribution

   Conceptualization, Resources, Writing—review and editing

   Competing interests

   No competing interests declared

5. Ana Riesgo

   Invertebrate Division, Life Sciences Department, The Natural History Museum, London, United Kingdom

   Contribution

   Resources, Data curation, Writing—review and editing

   Competing interests

   No competing interests declared

6. John C Marioni

   1. Wellcome Trust Sanger Institute, Hinxton, United Kingdom
   2. European Molecular Biology Laboratories-European Bioinformatics Institute, Hinxton, United Kingdom
   3. Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Resources, Supervision, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9092-0852

7. Gonzalo Giribet

   Museum of Comparative Zoology, Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States

   Contribution

   Conceptualization, Resources, Funding acquisition, Writing—review and editing

   Competing interests

   No competing interests declared

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