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      Comparative genomics of Steinernema reveals deeply conserved gene regulatory networks

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          Abstract

          Background

          Parasitism is a major ecological niche for a variety of nematodes. Multiple nematode lineages have specialized as pathogens, including deadly parasites of insects that are used in biological control. We have sequenced and analyzed the draft genomes and transcriptomes of the entomopathogenic nematode Steinernema carpocapsae and four congeners ( S. scapterisci, S. monticolum, S. feltiae, and S. glaseri).

          Results

          We used these genomes to establish phylogenetic relationships, explore gene conservation across species, and identify genes uniquely expanded in insect parasites. Protein domain analysis in Steinernema revealed a striking expansion of numerous putative parasitism genes, including certain protease and protease inhibitor families, as well as fatty acid- and retinol-binding proteins. Stage-specific gene expression of some of these expanded families further supports the notion that they are involved in insect parasitism by Steinernema. We show that sets of novel conserved non-coding regulatory motifs are associated with orthologous genes in Steinernema and Caenorhabditis.

          Conclusions

          We have identified a set of expanded gene families that are likely to be involved in parasitism. We have also identified a set of non-coding motifs associated with groups of orthologous genes in Steinernema and Caenorhabditis involved in neurogenesis and embryonic development that are likely part of conserved protein–DNA relationships shared between these two genera.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13059-015-0746-6) contains supplementary material, which is available to authorized users.

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          Most cited references 72

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          Evolution of genes and genomes on the Drosophila phylogeny.

          Comparative analysis of multiple genomes in a phylogenetic framework dramatically improves the precision and sensitivity of evolutionary inference, producing more robust results than single-genome analyses can provide. The genomes of 12 Drosophila species, ten of which are presented here for the first time (sechellia, simulans, yakuba, erecta, ananassae, persimilis, willistoni, mojavensis, virilis and grimshawi), illustrate how rates and patterns of sequence divergence across taxa can illuminate evolutionary processes on a genomic scale. These genome sequences augment the formidable genetic tools that have made Drosophila melanogaster a pre-eminent model for animal genetics, and will further catalyse fundamental research on mechanisms of development, cell biology, genetics, disease, neurobiology, behaviour, physiology and evolution. Despite remarkable similarities among these Drosophila species, we identified many putatively non-neutral changes in protein-coding genes, non-coding RNA genes, and cis-regulatory regions. These may prove to underlie differences in the ecology and behaviour of these diverse species.
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            Integrative analysis of the Caenorhabditis elegans genome by the modENCODE project.

            We systematically generated large-scale data sets to improve genome annotation for the nematode Caenorhabditis elegans, a key model organism. These data sets include transcriptome profiling across a developmental time course, genome-wide identification of transcription factor-binding sites, and maps of chromatin organization. From this, we created more complete and accurate gene models, including alternative splice forms and candidate noncoding RNAs. We constructed hierarchical networks of transcription factor-binding and microRNA interactions and discovered chromosomal locations bound by an unusually large number of transcription factors. Different patterns of chromatin composition and histone modification were revealed between chromosome arms and centers, with similarly prominent differences between autosomes and the X chromosome. Integrating data types, we built statistical models relating chromatin, transcription factor binding, and gene expression. Overall, our analyses ascribed putative functions to most of the conserved genome.
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              Inconsistency of phylogenetic estimates from concatenated data under coalescence.

              Although multiple gene sequences are becoming increasingly available for molecular phylogenetic inference, the analysis of such data has largely relied on inference methods designed for single genes. One of the common approaches to analyzing data from multiple genes is concatenation of the individual gene data to form a single supergene to which traditional phylogenetic inference procedures - e.g., maximum parsimony (MP) or maximum likelihood (ML) - are applied. Recent empirical studies have demonstrated that concatenation of sequences from multiple genes prior to phylogenetic analysis often results in inference of a single, well-supported phylogeny. Theoretical work, however, has shown that the coalescent can produce substantial variation in single-gene histories. Using simulation, we combine these ideas to examine the performance of the concatenation approach under conditions in which the coalescent produces a high level of discord among individual gene trees and show that it leads to statistically inconsistent estimation in this setting. Furthermore, use of the bootstrap to measure support for the inferred phylogeny can result in moderate to strong support for an incorrect tree under these conditions. These results highlight the importance of incorporating variation in gene histories into multilocus phylogenetics.
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                Author and article information

                Contributors
                adler.dillman@ucr.edu
                mmacchie@uci.edu
                finli6@gmail.com
                arogers@caltech.edu
                bawilli@caltech.edu
                igor.antoshechkin@caltech.edu
                mingmail@email.arizona.edu
                gzane01@gmail.com
                Luxiaojun@tsinghua.edu.cn
                eelewis@ucdavis.edu
                hgblair@bact.wisc.edu
                spstock@email.arizona.edu
                byron_adams@byu.edu
                pws@caltech.edu
                ali.mortazavi@uci.edu
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1474-7596
                1474-760X
                21 September 2015
                21 September 2015
                2015
                : 16
                : 1
                Affiliations
                [ ]Department of Nematology, University of California, Riverside, CA 92521 USA
                [ ]Department of Developmental and Cell Biology, University of California, Irvine, CA 92697 USA
                [ ]Department of Biology and Evolutionary Ecology Laboratories, Brigham Young University, Provo, UT 84602 USA
                [ ]Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125 USA
                [ ]Department of Entomology, University of Arizona, Tucson, AZ 85721 USA
                [ ]Division of Biology and Biomedical Sciences, Washington University, St Louis, MO 63110 USA
                [ ]Department of Bacteriology, University of Wisconsin-Madison, Madison, WI 53706 USA
                [ ]Department of Entomology and Nematology, University of California, Davis, CA 95616 USA
                [ ]Howard Hughes Medical Institute, Pasadena, CA 91125 USA
                Article
                746
                10.1186/s13059-015-0746-6
                4578762
                6919c4f0-a87a-4abf-8701-37aed0fc0de9
                © Dillman et al. 2015

                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.

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                © The Author(s) 2015

                Genetics

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