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      The Corylus mandshurica genome provides insights into the evolution of Betulaceae genomes and hazelnut breeding

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          Abstract

          Hazelnut is popular for its flavor, and it has also been suggested that hazelnut is beneficial to cardiovascular health because it is rich in oleic acid. Here, we report the first high-quality chromosome-scale genome for the hazelnut species Corylus mandshurica (2 n = 22), which has a high concentration of oleic acid in its nuts. The assembled genome is 367.67 Mb in length, and the contig N50 is 14.85 Mb. All contigs were assembled into 11 chromosomes, and 28,409 protein-coding genes were annotated. We reconstructed the evolutionary trajectories of the genomes of Betulaceae species and revealed that the 11 chromosomes of the hazelnut genus were derived from the most ancestral karyotype in Betula pendula, which has 14 protochromosomes, by inferring homology among five Betulaceae genomes. We identified 96 candidate genes involved in oleic acid biosynthesis, and 10 showed rapid evolution or positive selection. These findings will help us to understand the mechanisms of lipid synthesis and storage in hazelnuts. Several gene families related to salicylic acid metabolism and stress responses experienced rapid expansion in this hazelnut species, which may have increased its stress tolerance. The reference genome presented here constitutes a valuable resource for molecular breeding and genetic improvement of the important agronomic properties of hazelnut.

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          KEGG: kyoto encyclopedia of genes and genomes.

          M Kanehisa (2000)
          KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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            RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies

            Motivation: Phylogenies are increasingly used in all fields of medical and biological research. Moreover, because of the next-generation sequencing revolution, datasets used for conducting phylogenetic analyses grow at an unprecedented pace. RAxML (Randomized Axelerated Maximum Likelihood) is a popular program for phylogenetic analyses of large datasets under maximum likelihood. Since the last RAxML paper in 2006, it has been continuously maintained and extended to accommodate the increasingly growing input datasets and to serve the needs of the user community. Results: I present some of the most notable new features and extensions of RAxML, such as a substantial extension of substitution models and supported data types, the introduction of SSE3, AVX and AVX2 vector intrinsics, techniques for reducing the memory requirements of the code and a plethora of operations for conducting post-analyses on sets of trees. In addition, an up-to-date 50-page user manual covering all new RAxML options is available. Availability and implementation: The code is available under GNU GPL at https://github.com/stamatak/standard-RAxML. Contact: alexandros.stamatakis@h-its.org Supplementary information: Supplementary data are available at Bioinformatics online.
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              fastp: an ultra-fast all-in-one FASTQ preprocessor

              Abstract Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2–5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.
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                Author and article information

                Contributors
                yangyongzhi2008@gmail.com
                Journal
                Hortic Res
                Hortic Res
                Horticulture Research
                Nature Publishing Group UK (London )
                2662-6810
                2052-7276
                1 March 2021
                1 March 2021
                2021
                : 8
                : 54
                Affiliations
                [1 ]GRID grid.32566.34, ISNI 0000 0000 8571 0482, State Key Laboratory of Grassland Agro-Ecosystem, Institute of Innovation Ecology & School of Life Sciences, , Lanzhou University, ; Lanzhou, China
                [2 ]GRID grid.13291.38, ISNI 0000 0001 0807 1581, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education & State Key Laboratory of Hydraulics & Mountain River Engineering, College of Life Sciences, , Sichuan University, ; Chengdu, China
                [3 ]GRID grid.458477.d, ISNI 0000 0004 1799 1066, CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, ; 666303 Mengla, Yunnan China
                [4 ]GRID grid.9227.e, ISNI 0000000119573309, Center of Plant Ecology, Core Botanical Gardens, Chinese Academy of Sciences, ; 666303 Mengla, Yunnan China
                Author information
                http://orcid.org/0000-0002-8999-4894
                Article
                495
                10.1038/s41438-021-00495-1
                7917096
                33642584
                916a5062-5b9e-4f6b-9fd4-51d228994753
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 22 June 2020
                : 11 January 2021
                : 20 January 2021
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                © The Author(s) 2021

                comparative genomics,agricultural genetics
                comparative genomics, agricultural genetics

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