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      Shared Transcriptional Control and Disparate Gain and Loss of Aphid Parasitism Genes

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          Abstract

          Aphids are a diverse group of taxa that contain agronomically important species, which vary in their host range and ability to infest crop plants. The genome evolution underlying agriculturally important aphid traits is not well understood. We generated draft genome assemblies for two aphid species: Myzus cerasi (black cherry aphid) and the cereal specialist Rhopalosiphum padi. Using a de novo gene prediction pipeline on both these, and three additional aphid genome assemblies ( Acyrthosiphon pisum, Diuraphis noxia, and Myzus persicae), we show that aphid genomes consistently encode similar gene numbers. We compare gene content, gene duplication, synteny, and putative effector repertoires between these five species to understand the genome evolution of globally important plant parasites. Aphid genomes show signs of relatively distant gene duplication, and substantial, relatively recent, gene birth. Putative effector repertoires, originating from duplicated and other loci, have an unusual genomic organization and evolutionary history. We identify a highly conserved effector pair that is tightly physically linked in the genomes of all aphid species tested. In R. padi, this effector pair is tightly transcriptionally linked and shares an unknown transcriptional control mechanism with a subset of ∼50 other putative effectors and secretory proteins. This study extends our current knowledge on the evolution of aphid genomes and reveals evidence for an as-of-yet unknown shared control mechanism, which underlies effector expression, and ultimately plant parasitism.

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          LTRharvest, an efficient and flexible software for de novo detection of LTR retrotransposons

          Background Transposable elements are abundant in eukaryotic genomes and it is believed that they have a significant impact on the evolution of gene and chromosome structure. While there are several completed eukaryotic genome projects, there are only few high quality genome wide annotations of transposable elements. Therefore, there is a considerable demand for computational identification of transposable elements. LTR retrotransposons, an important subclass of transposable elements, are well suited for computational identification, as they contain long terminal repeats (LTRs). Results We have developed a software tool LTRharvest for the de novo detection of full length LTR retrotransposons in large sequence sets. LTRharvest efficiently delivers high quality annotations based on known LTR transposon features like length, distance, and sequence motifs. A quality validation of LTRharvest against a gold standard annotation for Saccharomyces cerevisae and Drosophila melanogaster shows a sensitivity of up to 90% and 97% and specificity of 100% and 72%, respectively. This is comparable or slightly better than annotations for previous software tools. The main advantage of LTRharvest over previous tools is (a) its ability to efficiently handle large datasets from finished or unfinished genome projects, (b) its flexibility in incorporating known sequence features into the prediction, and (c) its availability as an open source software. Conclusion LTRharvest is an efficient software tool delivering high quality annotation of LTR retrotransposons. It can, for example, process the largest human chromosome in approx. 8 minutes on a Linux PC with 4 GB of memory. Its flexibility and small space and run-time requirements makes LTRharvest a very competitive candidate for future LTR retrotransposon annotation projects. Moreover, the structured design and implementation and the availability as open source provides an excellent base for incorporating novel concepts to further improve prediction of LTR retrotransposons.
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            Amelioration of bacterial genomes: rates of change and exchange.

            Although bacterial species display wide variation in their overall GC contents, the genes within a particular species' genome are relatively similar in base composition. As a result, sequences that are novel to a bacterial genome-i.e., DNA introduced through recent horizontal transfer-often bear unusual sequence characteristics and can be distinguished from ancestral DNA. At the time of introgression, horizontally transferred genes reflect the base composition of the donor genome; but, over time, these sequences will ameliorate to reflect the DNA composition of the new genome because the introgressed genes are subject to the same mutational processes affecting all genes in the recipient genome. This process of amelioration is evident in a large group of genes involved in host-cell invasion by enteric bacteria and can be modeled to predict the amount of time required after transfer for foreign DNA to resemble native DNA. Furthermore, models of amelioration can be used to estimate the time of introgression of foreign genes in a chromosome. Applying this approach to a 1.43-megabase continuous sequence, we have calculated that the entire Escherichia coli chromosome contains more than 600 kb of horizontally transferred, protein-coding DNA. Estimates of amelioration times indicate that this DNA has accumulated at a rate of 31 kb per million years, which is on the order of the amount of variant DNA introduced by point mutations. This rate predicts that the E. coli and Salmonella enterica lineages have each gained and lost more than 3 megabases of novel DNA since their divergence.
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              Integration of mapped RNA-Seq reads into automatic training of eukaryotic gene finding algorithm

              We present a new approach to automatic training of a eukaryotic ab initio gene finding algorithm. With the advent of Next-Generation Sequencing, automatic training has become paramount, allowing genome annotation pipelines to keep pace with the speed of genome sequencing. Earlier we developed GeneMark-ES, currently the only gene finding algorithm for eukaryotic genomes that performs automatic training in unsupervised ab initio mode. The new algorithm, GeneMark-ET augments GeneMark-ES with a novel method that integrates RNA-Seq read alignments into the self-training procedure. Use of ‘assembled’ RNA-Seq transcripts is far from trivial; significant error rate of assembly was revealed in recent assessments. We demonstrated in computational experiments that the proposed method of incorporation of ‘unassembled’ RNA-Seq reads improves the accuracy of gene prediction; particularly, for the 1.3 GB genome of Aedes aegypti the mean value of prediction Sensitivity and Specificity at the gene level increased over GeneMark-ES by 24.5%. In the current surge of genomic data when the need for accurate sequence annotation is higher than ever, GeneMark-ET will be a valuable addition to the narrow arsenal of automatic gene prediction tools.
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                Author and article information

                Contributors
                Role: Associate Editor
                Journal
                Genome Biol Evol
                Genome Biol Evol
                gbe
                Genome Biology and Evolution
                Oxford University Press
                1759-6653
                October 2018
                25 August 2018
                25 August 2018
                : 10
                : 10
                : 2716-2733
                Affiliations
                [1 ]Cell and Molecular Sciences, The James Hutton Institute, Dundee, United Kingdom
                [2 ]Dundee Effector Consortium, The James Hutton Institute, Dundee, United Kingdom
                [3 ]Information and Computational Sciences, The James Hutton Institute, Dundee, United Kingdom
                [4 ]Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom
                [5 ]Division of Plant Sciences, School of Life Sciences, University of Dundee, Dundee, United Kingdom
                Author notes

                Data deposition: This project has been deposited at NCBI under the BioProject accessions PRJEB24287, PRJEB24204, PRJEB24317 and PRJEB24338. Genome assemblies and gene calls are available at http://bipaa.genouest.org/is/aphidbase/ and DOI:10.5281/zenodo.125293410.5281/zenodo.1252934.

                Corresponding authors: E-mails: j.bos@ 123456dundee.ac.uk ; se389@ 123456cam.ac.uk .
                Author information
                http://orcid.org/0000-0003-3222-8643
                Article
                evy183
                10.1093/gbe/evy183
                6186164
                30165560
                4e6ee203-934a-41d8-8b2f-c1e4568816ad
                © The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                : 23 August 2018
                Page count
                Pages: 18
                Funding
                Funded by: Biotechnology and Biological Sciences Research Council 10.13039/501100000268
                Award ID: BB/M014207/1
                Funded by: European Research Council 10.13039/100010663
                Award ID: 310190-APHIDHOST
                Funded by: Royal Society of Edinburgh 10.13039/501100000332
                Categories
                Research Article

                Genetics
                aphids,effectors,genome evolution,shared transcriptional control,horizontal gene transfer
                Genetics
                aphids, effectors, genome evolution, shared transcriptional control, horizontal gene transfer

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