DOI: 10.5061/dryad.nvx0k6f0j
title: Data from: A phylogenomic backbone for Acoelomorpha inferred from transcriptomic data

description: Xenacoelomorpha are mostly microscopic, morphologically simple worms,
 lacking many structures typical of other bilaterians. Xenacoelomorphs
 –which include three main groups: Acoela, Nemertodermatida, and
 Xenoturbella– have been proposed to be an early diverging Bilateria,
 sister to protostomes and deuterostomes, but other phylogenomic analyses
 have recovered this clade nested within the deuterostomes, as sister to
 Ambulacraria. The position of Xenacoelomorpha within the metazoan tree has
 understandably attracted a lot of attention, overshadowing the study of
 phylogenetic relationships within this group. Given that Xenoturbella
 includes only six species whose relationships are well understood, we
 decided to focus on the most specious Acoelomorpha (Acoela +
 Nemertodermatida). Here, we have sequenced 29 transcriptomes, doubling the
 number of sequenced species, to infer a backbone tree for Acoelomorpha
 based on genomic data. The recovered topology is mostly congruent with
 previous studies. The most important difference is the recovery of
 Paratomella as the first off-shoot within Acoela, dramatically changing
 the reconstruction of the ancestral acoel. Besides, we have detected
 incongruence between the gene tres and the species tree, likely linked to
 incomplete lineage sorting, and some signal of introgression between the
 families Dakuidae and Mecynostomidae, which hampers inferring the correct
 placement of this family and, particularly, of the genus Notocelis. We
 have also used this dataset to infer for the first time diversification
 times within Acoelomorpha, which coincide with known bilaterian
 diversification and extinction events. Given the importance of
 morphological data in acoelomorph phylogenetics, we tested several
 partitions and models. Although morphological data failed to recover a
 robust phylogeny, phylogenetic placement has proven to be a suitable
 alternative when a reference phylogeny is available.
description: <strong>Molecular phylogenomics</strong>
 A total of 29 transcriptomes were generated from individuals
 collected between 2007 and 2020, preserved in either RNAlater or RNA
 Shield and long-term stored at -20ºC. Total RNA was extracted using the
 Zymo Microprep Quick-RNA kit (Zymo Research) and amplified with the
 SMARTer Universal Low Input RNA Kit (Takara Bio). The quality of the
 extractions was ensured with the Bioanalyzer High Sensitivity DNA Analysis
 and sent to either SciLifeLab or Macrogen for sequencing in an Illumina
 HiSeq X platform. Three cleaning and assembly strategies were devised to
 maximise assembly completeness. First, following standard practice, raw
 reads were cleaned with Trimmomatic and assembled with Trinity 2.9.1 with
 default parameters. Second, using the TransPi pipeline (version 1.1.0⁠)
 with three kmer lengths (21, 31, and 41). Third, raw reads were
 quality-filtered before the Trinity assembly in a three-step process:
 sequencing errors were corrected with Rcorrector 1.0.4, sequencing
 adapters were removed with Trimmomatic (as implemented in Trinity 2.9.1),
 and the reads were quality-filtered with Prinseq 0.20.4⁠, trimming
 nucleotides under 30 PHRED from both ends and filtering out reads with a
 mean quality under 20, entropy under 50, and shorter than 40 base pairs.
 Redundant contigs were removed with EvidentialGene v2019.05.14 and
 cross-contaminants were filtered with CroCo 1.1 measuring the contig
 expression with Kallisto 0.46.2⁠. All transcriptomes were assembled
 following the three pipelines and the best assembly was selected based on
 its completeness score, measured with BUSCO 3.0.2 and the Metazoa_odb9
 database⁠. Finally, coding regions with a minimum length of 300 amino
 acids were extracted with TransDecoder 5.3.0 and duplicates collapsed
 (minimum identity 95%, minimum overlap 40 amino acids) with the Dedupe
 program from BBMap 38.92⁠. The extracted proteins were
 assigned to orthogroups with OrthoFinder 2.4.1 and screened for paralogs
 with PhyloPyPruner 1.2.3⁠ with the following settings: pruning algorithm
 “Largest Subtree”, keep orthogroups with at least five taxa, trim branches
 longer than five times the standard deviation of all branch lengths,
 collapse nodes with nodal support under 60, and, in species-specific
 duplications, keep the sequences with the shortest pairwise distance to
 its sister taxa. For the seven species represented by two transcriptomes,
 only the specimen with the highest number of orthologs was kept.
 Non-homologous stretches within the sequences were identified and masked
 with Prequal 1.02⁠, and all sequences shorter than 250 unmasked amino
 acids were removed. All remaining orthogroups with more than five species
 were aligned with MAFFT 7.475⁠ using the L-INS-i algorithm. Ambiguously
 aligned positions, sequences shorter than 66% of the total alignment
 length, and sites with more than 80% missing data were filtered with BMGE
 1.12. The alignments that did not meet the assumptions of stationarity and
 homogeneity were identified with IQ-TREE 2.1.3 and removed. The resulting
 dataset included 2774 genes. This dataset was filtered by occupancy,
 substitution rate, level of saturation, compositional heterogeneity, and
 average patristic distances to reduce systematic errors. ASTRAL, IQ-TREE
 (partitioned and site-specific C20 and C60 models), and PhyloBayes were
 used in phylogenomic inference. MCMCtree was used to infer divergence
 times. <strong>Morphological
 phylogenetics</strong> A morphological matrix
 including all species and up to 44 characters was prepared based on
 descriptions from the literature and photographs of the specimens
 analysed. Several partition schemes were tested to maximise the
 phylogenetic signal of the data. The stepping-stone algorithm implemented
 in MrBayes 3.2.7 was used to calculate the likelihood of each scheme under
 the standard discrete model, but applying the ascertainment bias
 correction and with different model parameters: fixed or variable rates
 among partitions (APRV), fixed or variable rates among characters (ACRV),
 and linking or unlinking branch lengths, testing nine models per partition
 scheme. Finally, the best overall partition scheme and best model
 configuration per scheme were identified with BayesFactors. MrBayes was
 used to infer a phylogenetic tree for each partition scheme, applying the
 best-fit model configuration. For each analysis we ran two independent
 runs with four Markov chains each for 50 million generations, sampling
 every 10,000 generations and discarding the first 25% as burn-in. Chain
 convergence was assessed by ensuring a correct mixing in the
 log-likelihood plot, that all ESS values were above 200, and that the
 Potential Scale Reduction Factor was at least one.
 Additionally, the ability of these characters to place a set of
 species in a given tree was tested using the phylogenetic placement
 algorithm implemented in RAxML 8.2.12. First, morphological characters
 were weighted in RAxML using the IQ-TREE topology as a guide tree with
 four gamma categories and applying the Lewis ascertainment bias
 correction. Then, a morphological matrix with 84 acoel species was
 downloaded from Jondelius et al. (2011, Systematic Biology)⁠ and used to
 place the species in the reference tree applying the inferred character
 weights.
description: # A Phylogenomic Backbone for Acoelomorpha Inferred from Transcriptomic
 Data
 [https://doi.org/10.5061/dryad.nvx0k6f0j](https://doi.org/10.5061/dryad.nvx0k6f0j) Here, we have sequenced 29 transcriptomes, doubling the number of sequenced species, to infer a backbone tree for Acoelomorpha based on genomic data. In doing so, we not only generated a robust phylogeny but also explored the data to identify potential sources of incongruence that generate topological instability. We then used this new phylogenomic hypothesis to (1) date major cladogenetic events within Acoelomorpha, (2) evaluate the performance of available morphological characters when reconstructing acoelomorph phylogeny and in phylogenetic placement of new species, and (3) study morphological evolution in this group. ## Description of the data and file structure Here, we provide access to the files necessary to replicate our findings, separated into six directories, four related to molecular phylogenomics and two to morphological phylogenetics. * Molecular phylogenomics: * "00-Assemblies": the best assembly per specimen, after removing cross-contaminant contigs with CroCo. * "01-TransDecoder": Two fasta files per specimen, including the coding regions coded as amino acids (.pep) or nucleotides (.cds). * "02-Alignments": Multiple sequence alignments of the 2774 genes included in the full dataset. * "03-Genetrees": Genetrees corresponding to these alignments. Please, note that the number of genetrees does not match the number of alignments. IQ-TREE cannot infer trees with nodal support from alignments with fewer than four sequences. The minimum number of sequences per alignments is five, but some might be identical and hence IQ-TREE does not consider them during tree inference. * "04-FullDataset_noNotocelis": Full dataset after excluding the species *Notocelis gullmarensis*. This is the supermatrix created to run phylogenomic analyses and from which all data filtering was performed. * "05-Loci_subsampling": Gene trees, supermatrices, and partition files for all submatrices. There are 10 main datasets, created after selecting either the best 300 and 567 genes according to their: occupation, substitution rate, saturation, compositional heterogeneity, and average patristic distances. * "06-Phylogenetically_informative_genes": Dataset created to infer the position of *Notocelis* and for the topology tests. These genes were selected based on the Likelihood Mapping algorithm implemented in IQ-TREE. Only the genes for which &gt;70% of the quartets fall in one of the corners were selected. * "07-MCMCtree_inputdata": Supermatrix, reference tree, and control files for the MCMCtree analysis. * Morphological phylogenetics: * "08-Morphological_partitions_to_test-Nexus": list of all nexus files used to infer the best model configuration for each partition. * "09-Morphological_phylogenetics-Nexus": list of nexus files used during phylogenetic inference, i.e. the best model configuration per partition scheme. Finally, an additional file called "genesortR-Gene_properties.csv" is included. This is the output of the *genesortR* script, which includes a summary of several gene properties per alignments. this is the file that was used to create all submatrices analysed.