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      Saccharina genomes provide novel insight into kelp biology

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          Abstract

          Seaweeds are essential for marine ecosystems and have immense economic value. Here we present a comprehensive analysis of the draft genome of Saccharina japonica, one of the most economically important seaweeds. The 537-Mb assembled genomic sequence covered 98.5% of the estimated genome, and 18,733 protein-coding genes are predicted and annotated. Gene families related to cell wall synthesis, halogen concentration, development and defence systems were expanded. Functional diversification of the mannuronan C-5-epimerase and haloperoxidase gene families provides insight into the evolutionary adaptation of polysaccharide biosynthesis and iodine antioxidation. Additional sequencing of seven cultivars and nine wild individuals reveal that the genetic diversity within wild populations is greater than among cultivars. All of the cultivars are descendants of a wild S. japonica accession showing limited admixture with S. longissima. This study represents an important advance toward improving yields and economic traits in Saccharina and provides an invaluable resource for plant genome studies.

          Abstract

          Kelps are ecologically and economically important seaweeds. Here the authors sequence the genome of Saccharina japonica to gain insights into the evolutionary adaptation of polysaccharide biosynthesis, iodine concentration and antioxidation mechanisms and the population genetics of kelp domestication.

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          Most cited references34

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          KaKs_Calculator 2.0: A Toolkit Incorporating Gamma-Series Methods and Sliding Window Strategies

          We present an integrated stand-alone software package named KaKs_Calculator 2.0 as an updated version. It incorporates 17 methods for the calculation of nonsynonymous and synonymous substitution rates; among them, we added our modified versions of several widely used methods as the gamma series including γ-NG, γ-LWL, γ-MLWL, γ-LPB, γ-MLPB, γ-YN and γ-MYN, which have been demonstrated to perform better under certain conditions than their original forms and are not implemented in the previous version. The package is readily used for the identification of positively selected sites based on a sliding window across the sequences of interests in 5’ to 3’ direction of protein-coding sequences, and have improved the overall performance on sequence analysis for evolution studies. A toolbox, including C++ and Java source code and executable files on both Windows and Linux platforms together with a user instruction, is downloadable from the website for academic purpose at https://sourceforge.net/projects/kakscalculator2/.
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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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              Gene prediction in novel fungal genomes using an ab initio algorithm with unsupervised training.

              We describe a new ab initio algorithm, GeneMark-ES version 2, that identifies protein-coding genes in fungal genomes. The algorithm does not require a predetermined training set to estimate parameters of the underlying hidden Markov model (HMM). Instead, the anonymous genomic sequence in question is used as an input for iterative unsupervised training. The algorithm extends our previously developed method tested on genomes of Arabidopsis thaliana, Caenorhabditis elegans, and Drosophila melanogaster. To better reflect features of fungal gene organization, we enhanced the intron submodel to accommodate sequences with and without branch point sites. This design enables the algorithm to work equally well for species with the kinds of variations in splicing mechanisms seen in the fungal phyla Ascomycota, Basidiomycota, and Zygomycota. Upon self-training, the intron submodel switches on in several steps to reach its full complexity. We demonstrate that the algorithm accuracy, both at the exon and the whole gene level, is favorably compared to the accuracy of gene finders that employ supervised training. Application of the new method to known fungal genomes indicates substantial improvement over existing annotations. By eliminating the effort necessary to build comprehensive training sets, the new algorithm can streamline and accelerate the process of annotation in a large number of fungal genome sequencing projects.
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                Author and article information

                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Pub. Group
                2041-1723
                24 April 2015
                : 6
                : 6986
                Affiliations
                [1 ]Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences , Qingdao 266071, China
                [2 ]Computational Genomics Lab, Beijing Institutes of Life Science, Chinese Academy of Sciences , Beijing 100101, China
                [3 ]College of Life Sciences, University of Chinese Academy of Sciences , Beijing 100049, China
                [4 ]Tianjin Key Laboratory for Industrial Biosystems and Bioprocessing Engineering, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences , Tianjin 300308, China
                [5 ]School of Life Sciences, Shandong University of Technology , Zibo 255049, China
                [6 ]State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, Institute of Plant Biology, School of Life Sciences, Fudan University , Shanghai 200433, China
                Author notes
                [*]

                These authors contributed equally to this work.

                Article
                ncomms7986
                10.1038/ncomms7986
                4421812
                25908475
                3b5f6ec4-cc8c-4c6e-9834-e2e7190b3240
                Copyright © 2015, Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 27 January 2015
                : 20 March 2015
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