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      Relationships within Cladobranchia (Gastropoda: Nudibranchia) based on RNA-Seq data: an initial investigation

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          Abstract

          Cladobranchia (Gastropoda: Nudibranchia) is a diverse (approx. 1000 species) but understudied group of sea slug molluscs. In order to fully comprehend the diversity of nudibranchs and the evolution of character traits within Cladobranchia, a solid understanding of evolutionary relationships is necessary. To date, only two direct attempts have been made to understand the evolutionary relationships within Cladobranchia, neither of which resulted in well-supported phylogenetic hypotheses. In addition to these studies, several others have addressed some of the relationships within this clade while investigating the evolutionary history of more inclusive groups (Nudibranchia and Euthyneura). However, all of the resulting phylogenetic hypotheses contain conflicting topologies within Cladobranchia. In this study, we address some of these long-standing issues regarding the evolutionary history of Cladobranchia using RNA-Seq data (transcriptomes). We sequenced 16 transcriptomes and combined these with four transcriptomes from the NCBI Sequence Read Archive. Transcript assembly using Trinity and orthology determination using H aMS tR yielded 839 orthologous groups for analysis. These data provide a well-supported and almost fully resolved phylogenetic hypothesis for Cladobranchia. Our results support the monophyly of Cladobranchia and the sub-clade Aeolidida, but reject the monophyly of Dendronotida.

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          Most cited references26

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          Improved tools for biological sequence comparison.

          We have developed three computer programs for comparisons of protein and DNA sequences. They can be used to search sequence data bases, evaluate similarity scores, and identify periodic structures based on local sequence similarity. The FASTA program is a more sensitive derivative of the FASTP program, which can be used to search protein or DNA sequence data bases and can compare a protein sequence to a DNA sequence data base by translating the DNA data base as it is searched. FASTA includes an additional step in the calculation of the initial pairwise similarity score that allows multiple regions of similarity to be joined to increase the score of related sequences. The RDF2 program can be used to evaluate the significance of similarity scores using a shuffling method that preserves local sequence composition. The LFASTA program can display all the regions of local similarity between two sequences with scores greater than a threshold, using the same scoring parameters and a similar alignment algorithm; these local similarities can be displayed as a "graphic matrix" plot or as individual alignments. In addition, these programs have been generalized to allow comparison of DNA or protein sequences based on a variety of alternative scoring matrices.
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            Inferring ancient divergences requires genes with strong phylogenetic signals.

            To tackle incongruence, the topological conflict between different gene trees, phylogenomic studies couple concatenation with practices such as rogue taxon removal or the use of slowly evolving genes. Phylogenomic analysis of 1,070 orthologues from 23 yeast genomes identified 1,070 distinct gene trees, which were all incongruent with the phylogeny inferred from concatenation. Incongruence severity increased for shorter internodes located deeper in the phylogeny. Notably, whereas most practices had little or negative impact on the yeast phylogeny, the use of genes or internodes with high average internode support significantly improved the robustness of inference. We obtained similar results in analyses of vertebrate and metazoan phylogenomic data sets. These results question the exclusive reliance on concatenation and associated practices, and argue that selecting genes with strong phylogenetic signals and demonstrating the absence of significant incongruence are essential for accurately reconstructing ancient divergences.
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              HaMStR: Profile hidden markov model based search for orthologs in ESTs

              Background EST sequencing is a versatile approach for rapidly gathering protein coding sequences. They provide direct access to an organism's gene repertoire bypassing the still error-prone procedure of gene prediction from genomic data. Therefore, ESTs are often the only source for biological sequence data from taxa outside mainstream interest. The widespread use of ESTs in evolutionary studies and particularly in molecular systematics studies is still hindered by the lack of efficient and reliable approaches for automated ortholog predictions in ESTs. Existing methods either depend on a known species tree or cannot cope with redundancy in EST data. Results We present a novel approach (HaMStR) to mine EST data for the presence of orthologs to a curated set of genes. HaMStR combines a profile Hidden Markov Model search and a subsequent BLAST search to extend existing ortholog cluster with sequences from further taxa. We show that the HaMStR results are consistent with those obtained with existing orthology prediction methods that require completely sequenced genomes. A case study on the phylogeny of 35 fungal taxa illustrates that HaMStR is well suited to compile informative data sets for phylogenomic studies from ESTs and protein sequence data. Conclusion HaMStR extends in a standardized manner a pre-defined set of orthologs with ESTs from further taxa. In the same fashion HaMStR can be applied to protein sequence data, and thus provides a comprehensive approach to compile ortholog cluster from any protein coding data. The resulting orthology predictions serve as the data basis for a variety of evolutionary studies. Here, we have demonstrated the application of HaMStR in a molecular systematics study. However, we envision that studies tracing the evolutionary fate of individual genes or functional complexes of genes will greatly benefit from HaMStR orthology predictions as well.
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                Author and article information

                Journal
                R Soc Open Sci
                R Soc Open Sci
                RSOS
                royopensci
                Royal Society Open Science
                The Royal Society Publishing
                2054-5703
                September 2015
                23 September 2015
                23 September 2015
                : 2
                : 9
                : 150196
                Affiliations
                [1 ]Laboratory of Molecular Evolution, Center for Bioinformatics and Computational Biology, University of Maryland, College Park , MD 20742, USA
                [2 ]NMFS, National Systematics Laboratory, National Museum of Natural History, Smithsonian Institution , MRC-153, PO Box 37012, Washington, DC 20013, USA
                Author notes
                Author for correspondence: Jessica A. Goodheart e-mail: jagood@ 123456umd.edu
                Article
                rsos150196
                10.1098/rsos.150196
                4593679
                26473045
                546fadcc-db0b-4fc0-aaf1-18f169c2db17

                © 2015 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

                History
                : 7 May 2015
                : 26 August 2015
                Funding
                Funded by: Conchologists of America
                Award ID: Research Grant
                Funded by: National Museum of Natural History, Smithsonian Institution http://dx.doi.org/10.13039/100006271
                Award ID: Small Grant
                Funded by: Society of Systematic Biologists http://dx.doi.org/10.13039/100006069
                Award ID: Graduate Student Research Award
                Funded by: University of Maryland, College Park
                Funded by: Directorate for Biological Sciences http://dx.doi.org/10.13039/100000076
                Award ID: PIRE-1243541
                Funded by: Graduate School, University of Maryland http://dx.doi.org/10.13039/100008029
                Award ID: Dean's Fellowship
                Categories
                1001
                183
                198
                70
                Biology (Whole Organism)
                Research Article
                Custom metadata
                September, 2015

                mollusca,phylogenomics,nudibranchs,sea slugs,rna-seq,phylotranscriptomics

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