Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
• Data Files
• Related Publications
• Related Datasets
• Parameters
• Instruments
• Deployments
• Project Information
• Funding

From Cannon et al 2014):
Taxon Sampling
We sampled transcriptomic data from representatives of all recognized hemichordate families and all echinoderm classes except Echinoidea, for which data were already available (Tables S1 and S2). At least two species of each taxonomic group were sampled, except for Spengelidae. Samples from which novel transcriptomic data were obtained were collected in various locations, transported live to the laboratory, transferred to RNAlater, frozen at -80C, or kept in ethanol at -20C.

Sequencing and Assembly
Total RNA was extracted from fresh or preserved samples, and cDNA libraries were prepared using the SMART cDNA Library Construction Kit (Clontech; see Supplemental Experimental Procedures for further details). Samples were sequenced with 454 FLX, 454 Titanium, or Illumina HiSeq 2000. Generated sequence data were augmented with publicly available data. Taxa sequenced by 454 or Sanger methods were assembled and processed using the EST2uni pipeline [Forment et al, 2008], while those sequenced by Illumina were digitally normalized using khmer [Brown et al, 2012] and assembled using Trinity [Ebersberger, et al, 2009]. Contigs (for Illumina libraries) or contigs + high-quality singletons (for 454 and Sanger libraries) were translated using TransDecoder (http:// sourceforge.net/projects/transdecoder/). Table S2 provides the number of unigenes (contigs for Illumina libraries and contigs + singletons for 454 and Sanger libraries) obtained for each taxon.

Identification of putative ortholog groups (OGs) was conducted with HaMStR [Ebersberger et al, 2009] using the ‘‘model organisms’’ reference taxon set. After orthology determination, sequences from two 454 libraries from Ophionotusvictoriae and four libraries from Rhabdopleura species were combined into chimeric OTUs in order to reduce the amount of missing data per taxon.

Orthology groups were filtered following the approach of Kocot et al. [2011] First, only sequences greater than 100 aa in length, and OGs with at least 15ambulacrarian taxa and including at least one pterobranch sequence, were retained for further analyses. Sequences less than 100 aa in length were deleted because they are likely to be incorrectly aligned. To remove mistranslated sequence ends, we trimmed amino acid sequences when stop codons (marked by X) were present in either the first or last 20 characters. Each OG was aligned with MAFFT [Katoh et al, 2005] and then trimmed with Aliscore and Alicut [Misof and Misof, 2009] to remove columns with ambiguous alignment.

Next, individual alignments were manually evaluated for partially mistranslated sequences, which were deleted or trimmed as appropriate. Single-OG trees were then constructed for each OG using RAxML v7.3.8 [Stamatakis, 2006] with the PROTGAMMALGF model. Individual gene trees were manually evaluated for sequence contamination as described in Supplemental Experimental Procedures.After manual screening of alignments and individual OG trees, 299 alignments remained, constituting the basis for the 299/33 data set. To screen for potential paralogs, we used PhyloTreePruner [Kocot et al, 2013] (details in Supplemental Experimental Procedures). Alignments passing screening via PhyloTreePruner constituted the 185/33 and 185/31 data sets. To test the effect of low-coverage taxa and missing data on our phylogenetic reconstruction, we employed MARE [Meyer et al, 2010] to generate the 165/20 data set (weighting of information content parameter alpha = 3).

Phylogenetic Analyses
Maximum-likelihood phylogenetic analyses of all data sets were conducted using RAxML v7.7.6 [Stamatakis, 2006] using a PROTGAMMALGF model for each individual OG partition. This model was the most appropriate for the majority of genes and was among the best-fitting models for nearly all OGs as assessed by ProtTest [Darriba et al, 2011]. Previous work [Kocot et al, 2011] and preliminary analyses on these data sets indicated that employing a single model across all partitions recovers highly similar trees while proving considerably less computationally expensive than specifying individual models for all partitions. Nodal support was assessed with 1,000 replicates of nonparametric bootstrapping. Bootstrapped trees from the 185/33 data set were used to calculate leaf stability and taxonomic instability indices of each OTU using the RogueNaRok server (http://rnr.h-its.org). Competing hypotheses ofambulacrarian phylogeny were evaluated using the SH test [Shimodaira, 2002] as implemented in RAxML with the PROTGAMMALGF model for each OG partition. Bayesian inference analyses were conducted using PhyloBayes 2.3 [Lartillot et al, 2013] with the CAT model, which accounts for site specific rate heterogeneity, with two independent chains run for 11,000 cycles each and 1,100 cycles discarded as burn-in. Topology and posterior consensus support was generated using the bpcomp program within PhyloBayes; convergence of the two chains was indicated by ‘‘maxdiff’’ values below 0.2. All phylogenetic analyses were conducted on the Auburn University CASIC HPC supercomputer.

Accession Numbers
Sequence Read Archive (SRA) accession numbers for all data are given in Table S2. The four data matrices are available from the Dryad Digital Repository (http://datadryad.org) with the DOI http://dx.doi.org/10.5061/dryad.20s7c.

BCO-DMO Processing notes:
- added conventional header with dataset name, PI name, version date
- modified parameter names to conform with BCO-DMO naming conventions
- added links to NCBI accession pages

Extracted from the NSF award abstract:

The research will explore the genetics, diversity, and biogeography of Antarctic marine benthic invertebrates, seeking to overturn the widely accepted suggestion that benthic fauna do not constitute a large, panmictic population. The investigators will sample adults and larvae from undersampled regions of West Antarctica that, combined with existing samples, will provide significant coverage of the western hemisphere of the Southern Ocean. The objectives are: 1) To assess the degree of genetic connectivity (or isolation) of benthic invertebrate species in the Western Antarctic using high-resolution genetic markers. 2) To begin exploring planktonic larvae spatial and bathymetric distributions for benthic shelf invertebrates in the Bellinghausen, Amundsen and Ross Seas. 3) To continue to develop a Marine Antarctic Genetic Inventory (MAGI) that relates larval and adult forms via DNA barcoding.