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      Comparative transcriptomics enlarges the toolkit of known developmental genes in mollusks

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          Abstract

          Background

          Mollusks display a striking morphological disparity, including, among others, worm-like animals (the aplacophorans), snails and slugs, bivalves, and cephalopods. This phenotypic diversity renders them ideal for studies into animal evolution. Despite being one of the most species-rich phyla, molecular and in silico studies concerning specific key developmental gene families are still scarce, thus hampering deeper insights into the molecular machinery that governs the development and evolution of the various molluscan class-level taxa.

          Results

          Next-generation sequencing was used to retrieve transcriptomes of representatives of seven out of the eight recent class-level taxa of mollusks. Similarity searches, phylogenetic inferences, and a detailed manual curation were used to identify and confirm the orthology of numerous molluscan Hox and ParaHox genes, which resulted in a comprehensive catalog that highlights the evolution of these genes in Mollusca and other metazoans. The identification of a specific molluscan motif in the Hox paralog group 5 and a lophotrochozoan ParaHox motif in the Gsx gene is described. Functional analyses using KEGG and GO tools enabled a detailed description of key developmental genes expressed in important pathways such as Hedgehog, Wnt, and Notch during development of the respective species. The KEGG analysis revealed Wnt8, Wnt11, and Wnt16 as Wnt genes hitherto not reported for mollusks, thereby enlarging the known Wnt complement of the phylum. In addition, novel Hedgehog ( Hh) -related genes were identified in the gastropod Lottia cf. kogamogai, demonstrating a more complex gene content in this species than in other mollusks.

          Conclusions

          The use of de novo transcriptome assembly and well-designed in silico protocols proved to be a robust approach for surveying and mining large sequence data in a wide range of non-model mollusks. The data presented herein constitute only a small fraction of the information retrieved from the analysed molluscan transcriptomes, which can be promptly employed in the identification of novel genes and gene families, phylogenetic inferences, and other studies using molecular tools. As such, our study provides an important framework for understanding some of the underlying molecular mechanisms involved in molluscan body plan diversification and hints towards functions of key developmental genes in molluscan morphogenesis.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12864-016-3080-9) contains supplementary material, which is available to authorized users.

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          Most cited references87

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          Sea anemone genome reveals ancestral eumetazoan gene repertoire and genomic organization.

          Sea anemones are seemingly primitive animals that, along with corals, jellyfish, and hydras, constitute the oldest eumetazoan phylum, the Cnidaria. Here, we report a comparative analysis of the draft genome of an emerging cnidarian model, the starlet sea anemone Nematostella vectensis. The sea anemone genome is complex, with a gene repertoire, exon-intron structure, and large-scale gene linkage more similar to vertebrates than to flies or nematodes, implying that the genome of the eumetazoan ancestor was similarly complex. Nearly one-fifth of the inferred genes of the ancestor are eumetazoan novelties, which are enriched for animal functions like cell signaling, adhesion, and synaptic transmission. Analysis of diverse pathways suggests that these gene "inventions" along the lineage leading to animals were likely already well integrated with preexisting eukaryotic genes in the eumetazoan progenitor.
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            Next-generation transcriptome assembly.

            Transcriptomics studies often rely on partial reference transcriptomes that fail to capture the full catalogue of transcripts and their variations. Recent advances in sequencing technologies and assembly algorithms have facilitated the reconstruction of the entire transcriptome by deep RNA sequencing (RNA-seq), even without a reference genome. However, transcriptome assembly from billions of RNA-seq reads, which are often very short, poses a significant informatics challenge. This Review summarizes the recent developments in transcriptome assembly approaches - reference-based, de novo and combined strategies - along with some perspectives on transcriptome assembly in the near future.
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              A new generation of homology search tools based on probabilistic inference.

              Many theoretical advances have been made in applying probabilistic inference methods to improve the power of sequence homology searches, yet the BLAST suite of programs is still the workhorse for most of the field. The main reason for this is practical: BLAST's programs are about 100-fold faster than the fastest competing implementations of probabilistic inference methods. I describe recent work on the HMMER software suite for protein sequence analysis, which implements probabilistic inference using profile hidden Markov models. Our aim in HMMER3 is to achieve BLAST's speed while further improving the power of probabilistic inference based methods. HMMER3 implements a new probabilistic model of local sequence alignment and a new heuristic acceleration algorithm. Combined with efficient vector-parallel implementations on modern processors, these improvements synergize. HMMER3 uses more powerful log-odds likelihood scores (scores summed over alignment uncertainty, rather than scoring a single optimal alignment); it calculates accurate expectation values (E-values) for those scores without simulation using a generalization of Karlin/Altschul theory; it computes posterior distributions over the ensemble of possible alignments and returns posterior probabilities (confidences) in each aligned residue; and it does all this at an overall speed comparable to BLAST. The HMMER project aims to usher in a new generation of more powerful homology search tools based on probabilistic inference methods.
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                Author and article information

                Contributors
                andre.luiz.de.oliveira@univie.ac.at
                tim.wollesen@univie.ac.at
                alen.kristof@univie.ac.at
                maik.scherholz@univie.ac.at
                emanuel.redl@univie.ac.at
                christiane.todt@uib.no
                cblei@mncn.csic.es
                andreas.wanninger@univie.ac.at
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                10 November 2016
                10 November 2016
                2016
                : 17
                : 905
                Affiliations
                [1 ]Department of Integrative Zoology, Faculty of Life Sciences, University of Vienna, Althanstraße 14, Vienna, 1090 Austria
                [2 ]University of Bergen, University Museum, The Natural History Collections, Allégaten 41, 5007 Bergen, Norway
                [3 ]Museo Nacional de Ciencias Naturales, Spanish National Research Council (CSIC), José Gutiérrez Abascal 2, Madrid, 28006 Spain
                [4 ]Institute of Biology, University of Leipzig, Leipzig, 04103 Germany
                Author information
                http://orcid.org/0000-0002-3266-5838
                Article
                3080
                10.1186/s12864-016-3080-9
                5103448
                e10607a9-cecd-4907-b749-5285c90a11e5
                © The Author(s). 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 2 March 2016
                : 8 September 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100002428, Austrian Science Fund;
                Award ID: P24276-B22
                Funded by: Ciência sem Fronteiras
                Award ID: 6090/13-3
                Funded by: Ramon y Cajal
                Award ID: RYC-2014-15615
                Funded by: ASSEMBLE
                Award ID: 835 (SBR-1)
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2016

                Genetics
                bioinformatics,evodevo,evolution,evolutionary developmental biology,genomics,mollusca,next-generation sequencing,ngs,transcriptomics,rna-seq

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