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      The high-quality sequencing of the Brassica rapa ‘XiangQingCai’ genome and exploration of genome evolution and genes related to volatile aroma

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          Abstract

          ‘Vanilla’ (XQC, brassica variety chinensis) is an important vegetable crop in the Brassica family, named for its strong volatile fragrance. In this study, we report the high-quality chromosome-level genome sequence of XQC. The assembled genome length was determined as 466.11 Mb, with an N50 scaffold of 46.20 Mb. A total of 59.50% repetitive sequences were detected in the XQC genome, including 47 570 genes. Among all examined Brassicaceae species, XQC had the closest relationship with B. rapa QGC (‘QingGengCai’) and B. rapa Pakchoi. Two whole-genome duplication (WGD) events and one recent whole-genome triplication (WGT) event occurred in the XQC genome in addition to an ancient WGT event. The recent WGT was observed to occur during 21.59–24.40 Mya (after evolution rate corrections). Our findings indicate that XQC experienced gene losses and chromosome rearrangements during the genome evolution of XQC. The results of the integrated genomic and transcriptomic analyses revealed critical genes involved in the terpenoid biosynthesis pathway and terpene synthase (TPS) family genes. In summary, we determined a chromosome-level genome of B. rapa XQC and identified the key candidate genes involved in volatile fragrance synthesis. This work can act as a basis for the comparative and functional genomic analysis and molecular breeding of B. rapa in the future.

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          Most cited references86

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          Fast and accurate short read alignment with Burrows–Wheeler transform

          Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
            • Record: found
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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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              HISAT: a fast spliced aligner with low memory requirements.

              HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.

                Author and article information

                Contributors
                Journal
                Hortic Res
                Hortic Res
                hr
                Horticulture Research
                Oxford University Press
                2662-6810
                2052-7276
                October 2023
                15 September 2023
                15 September 2023
                : 10
                : 10
                : uhad187
                Affiliations
                Suzhou Academy of Agricultural Sciences , Suzhou, Jiangsu 215155, China
                College of Life Sciences, North China University of Science and Technology , Tangshan, Hebei 063210, China
                Suzhou Academy of Agricultural Sciences , Suzhou, Jiangsu 215155, China
                Suzhou Polytechnic Institute of Agriculture , Suzhou, Jiangsu 215008, China
                Suzhou Academy of Agricultural Sciences , Suzhou, Jiangsu 215155, China
                Suzhou Academy of Agricultural Sciences , Suzhou, Jiangsu 215155, China
                College of Life Sciences, North China University of Science and Technology , Tangshan, Hebei 063210, China
                College of Life Sciences, North China University of Science and Technology , Tangshan, Hebei 063210, China
                Suzhou Academy of Agricultural Sciences , Suzhou, Jiangsu 215155, China
                Suzhou Academy of Agricultural Sciences , Suzhou, Jiangsu 215155, China
                College of Life Sciences, North China University of Science and Technology , Tangshan, Hebei 063210, China
                Author notes
                Correspondence authors. E-mails: saaslzk@ 123456qq.com ; songxm@ 123456ncst.edu.cn

                These authors contributed equally to the work.

                Article
                uhad187
                10.1093/hr/uhad187
                10611556
                37899953
                02037296-e1c6-4aca-a43e-7c9ca7256aec
                © The Author(s) 2023. Published by Oxford University Press on behalf of Nanjing Agricultural University.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 7 May 2023
                : 8 September 2023
                : 13 October 2023
                Page count
                Pages: 11
                Funding
                Funded by: Natural Science Foundation of Hebei;
                Award ID: C2021209005
                Funded by: Suzhou Municipal Bureau of Agriculture and Rural Affairs, the National Natural Science Foundation of China;
                Award ID: 32172583
                Funded by: Suzhou Agricultural Science and Technology Innovation project;
                Award ID: SNG2020065
                Award ID: SNG2020045
                Categories
                Article
                AcademicSubjects/SCI01140

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