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      Transposable Element Genomic Fissuring in Pyrenophora teres Is Associated With Genome Expansion and Dynamics of Host–Pathogen Genetic Interactions

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          Abstract

          Pyrenophora teres, P. teres f. teres (PTT) and P. teres f. maculata (PTM) cause significant diseases in barley, but little is known about the large-scale genomic differences that may distinguish the two forms. Comprehensive genome assemblies were constructed from long DNA reads, optical and genetic maps. As repeat masking in fungal genomes influences the final gene annotations, an accurate and reproducible pipeline was developed to ensure comparability between isolates. The genomes of the two forms are highly collinear, each composed of 12 chromosomes. Genome evolution in P. teres is characterized by genome fissuring through the insertion and expansion of transposable elements (TEs), a process that isolates blocks of genic sequence. The phenomenon is particularly pronounced in PTT, which has a larger, more repetitive genome than PTM and more recent transposon activity measured by the frequency and size of genome fissures. PTT has a longer cultivated host association and, notably, a greater range of host–pathogen genetic interactions compared to other Pyrenophora spp., a property which associates better with genome size than pathogen lifestyle. The two forms possess similar complements of TE families with Tc1/Mariner and LINE-like Tad-1 elements more abundant in PTT. Tad-1 was only detectable as vestigial fragments in PTM and, within the forms, differences in genome sizes and the presence and absence of several TE families indicated recent lineage invasions. Gene differences between P. teres forms are mainly associated with gene-sparse regions near or within TE-rich regions, with many genes possessing characteristics of fungal effectors. Instances of gene interruption by transposons resulting in pseudogenization were detected in PTT. In addition, both forms have a large complement of secondary metabolite gene clusters indicating significant capacity to produce an array of different molecules. This study provides genomic resources for functional genetics to help dissect factors underlying the host–pathogen interactions.

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          Mash: fast genome and metagenome distance estimation using MinHash

          Mash extends the MinHash dimensionality-reduction technique to include a pairwise mutation distance and P value significance test, enabling the efficient clustering and search of massive sequence collections. Mash reduces large sequences and sequence sets to small, representative sketches, from which global mutation distances can be rapidly estimated. We demonstrate several use cases, including the clustering of all 54,118 NCBI RefSeq genomes in 33 CPU h; real-time database search using assembled or unassembled Illumina, Pacific Biosciences, and Oxford Nanopore data; and the scalable clustering of hundreds of metagenomic samples by composition. Mash is freely released under a BSD license (https://github.com/marbl/mash). Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-0997-x) contains supplementary material, which is available to authorized users.
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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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              Gene prediction in eukaryotes with a generalized hidden Markov model that uses hints from external sources

              Background In order to improve gene prediction, extrinsic evidence on the gene structure can be collected from various sources of information such as genome-genome comparisons and EST and protein alignments. However, such evidence is often incomplete and usually uncertain. The extrinsic evidence is usually not sufficient to recover the complete gene structure of all genes completely and the available evidence is often unreliable. Therefore extrinsic evidence is most valuable when it is balanced with sequence-intrinsic evidence. Results We present a fairly general method for integration of external information. Our method is based on the evaluation of hints to potentially protein-coding regions by means of a Generalized Hidden Markov Model (GHMM) that takes both intrinsic and extrinsic information into account. We used this method to extend the ab initio gene prediction program AUGUSTUS to a versatile tool that we call AUGUSTUS+. In this study, we focus on hints derived from matches to an EST or protein database, but our approach can be used to include arbitrary user-defined hints. Our method is only moderately effected by the length of a database match. Further, it exploits the information that can be derived from the absence of such matches. As a special case, AUGUSTUS+ can predict genes under user-defined constraints, e.g. if the positions of certain exons are known. With hints from EST and protein databases, our new approach was able to predict 89% of the exons in human chromosome 22 correctly. Conclusion Sensitive probabilistic modeling of extrinsic evidence such as sequence database matches can increase gene prediction accuracy. When a match of a sequence interval to an EST or protein sequence is used it should be treated as compound information rather than as information about individual positions.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                18 April 2018
                2018
                : 9
                : 130
                Affiliations
                [1] 1Centre for Crop and Disease Management, Department of Environment and Agriculture, Curtin University , Bentley, WA, Australia
                [2] 2Centre for Crop Health, University of Southern Queensland , Toowoomba, QLD, Australia
                [3] 3Department of Plant Pathology, North Dakota State University , Fargo, ND, United States
                [4] 4Cereal Crops Research Unit, Red River Valley Agricultural Research Center, Agricultural Research Service, United States Department of Agriculture , Fargo, ND, United States
                Author notes

                Edited by: John R. Battista, Louisiana State University, United States

                Reviewed by: Yuriy L. Orlov, Institute of Cytology and Genetics (RAS), Russia; Kui Lin, Beijing Normal University, China

                *Correspondence: Simon R. Ellwood, srellwood@ 123456gmail.com

                This article was submitted to Evolutionary and Genomic Microbiology, a section of the journal Frontiers in Genetics

                Article
                10.3389/fgene.2018.00130
                5915480
                29720997
                2fe4c98a-583a-4396-ba7e-a6be40db5226
                Copyright © 2018 Syme, Martin, Wyatt, Lawrence, Muria-Gonzalez, Friesen and Ellwood.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 11 December 2017
                : 03 April 2018
                Page count
                Figures: 5, Tables: 3, Equations: 0, References: 101, Pages: 15, Words: 0
                Funding
                Funded by: Grains Research and Development Corporation 10.13039/501100000980
                Award ID: CUR00023 P5
                Categories
                Genetics
                Original Research

                Genetics
                barley net blotch,hordeum vulgare,single molecule sequencing dna,optical mapping,synteny,transposable element,repeat annotation

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