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      Telomere-to-telomere genome assembly of bitter melon ( Momordica charantia L. var. abbreviata Ser.) reveals fruit development, composition and ripening genetic characteristics

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          Abstract

          Momordica charantia L. var. abbreviata Ser. (Mca), known as bitter gourd or bitter melon, is a Momordica variety with medicinal value and belongs to the Cucurbitaceae family. In view of the lack of genomic information on bitter gourd and other Momordica species and to promote Mca genomic research, we assembled a 295.6-Mb telomere-to-telomere (T2T) high-quality Mca genome with six gap-free chromosomes after Hi-C correction. This genome is anchored to 11 chromosomes, which is consistent with the karyotype information, and comprises 98 contigs (N50 of 25.4 Mb) and 95 scaffolds (N50 of 25.4 Mb). The Mca genome harbors 19 895 protein-coding genes, of which 45.59% constitute predicted repeat sequences. Synteny analysis revealed variations involved in fruit quality during the divergence of bitter gourd. In addition, assay for transposase-accessible chromatin by high-throughput sequencing and metabolic analysis showed that momordicosides and other substances are characteristic of Mca fruit pulp. A combined transcriptomic and metabolomic analysis revealed the mechanisms of pigment accumulation and cucurbitacin biosynthesis in Mca fruit peels, providing fundamental molecular information for further research on Mca fruit ripening. This report provides a new genetic resource for Momordica genomic studies and contributes additional insights into Cucurbitaceae phylogeny.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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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
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                Author and article information

                Contributors
                Journal
                Hortic Res
                Hortic Res
                hr
                Horticulture Research
                Oxford University Press
                2662-6810
                2052-7276
                2023
                11 October 2022
                11 October 2022
                : 10
                : 1
                : uhac228
                Affiliations
                Institute of Agri-food Processing and Nutrition, Beijing Academy of Agricultural and Forestry Sciences , Beijing Key Laboratory of Fruits and Vegetable Storage and Processing, Key Laboratory of Vegetable Postharvest Processing of Ministry of Agriculture and Rural Areas, Beijing 100097, China
                Beijing Engineering and Technology Research Center of Food Additives, Beijing Advanced Innovation Center for Food Nutrition and Human Health, School of Food and Health , Beijing Technology and Business University (BTBU), Beijing, 100048, China
                Institute of Agri-food Processing and Nutrition, Beijing Academy of Agricultural and Forestry Sciences , Beijing Key Laboratory of Fruits and Vegetable Storage and Processing, Key Laboratory of Vegetable Postharvest Processing of Ministry of Agriculture and Rural Areas, Beijing 100097, China
                Ministry of Education Key Laboratory of Biodiversity Sciences and Ecological Engineering and State Key Laboratory of Genetic Engineering, Institute of Biodiversity Sciences and Institute of Plant Biology , School of Life Sciences, Fudan University, 2005 Songhu Road, Shanghai 200438, China
                School of Biosciences , University of Nottingham, Sutton Bonington Campus, Loughborough, Leicestershire, LE12 5RD, United Kingdom
                Institute of Agri-food Processing and Nutrition, Beijing Academy of Agricultural and Forestry Sciences , Beijing Key Laboratory of Fruits and Vegetable Storage and Processing, Key Laboratory of Vegetable Postharvest Processing of Ministry of Agriculture and Rural Areas, Beijing 100097, China
                Institute of Agri-food Processing and Nutrition, Beijing Academy of Agricultural and Forestry Sciences , Beijing Key Laboratory of Fruits and Vegetable Storage and Processing, Key Laboratory of Vegetable Postharvest Processing of Ministry of Agriculture and Rural Areas, Beijing 100097, China
                Beijing Engineering and Technology Research Center of Food Additives, Beijing Advanced Innovation Center for Food Nutrition and Human Health, School of Food and Health , Beijing Technology and Business University (BTBU), Beijing, 100048, China
                Beijing Engineering and Technology Research Center of Food Additives, Beijing Advanced Innovation Center for Food Nutrition and Human Health, School of Food and Health , Beijing Technology and Business University (BTBU), Beijing, 100048, China
                Biomarker Technologies Corporation , Beijing 101300, China
                Biomarker Technologies Corporation , Beijing 101300, China
                Penn State Univ, Huck Inst Life Sci, Dept Biol, University Pk , PA 16802 USA
                Institute of Agri-food Processing and Nutrition, Beijing Academy of Agricultural and Forestry Sciences , Beijing Key Laboratory of Fruits and Vegetable Storage and Processing, Key Laboratory of Vegetable Postharvest Processing of Ministry of Agriculture and Rural Areas, Beijing 100097, China
                Institute of Agri-food Processing and Nutrition, Beijing Academy of Agricultural and Forestry Sciences , Beijing Key Laboratory of Fruits and Vegetable Storage and Processing, Key Laboratory of Vegetable Postharvest Processing of Ministry of Agriculture and Rural Areas, Beijing 100097, China
                Author notes
                Corresponding authors: Jinhua Zuo, zuojinhua@ 123456126.com +861051503058; Qing Wang, wangqing@ 123456nercv.org ; Hong Ma, hxm16@ 123456psu.edu

                Anzhen Fu and Yanyan Zheng, These authors contributed equally to this work

                Article
                uhac228
                10.1093/hr/uhac228
                9832870
                36643758
                b866a379-59f4-4fda-b6a7-f29d855d3b41
                © 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
                : 4 July 2022
                : 26 September 2022
                : 01 January 2023
                Page count
                Pages: 16
                Categories
                Article
                AcademicSubjects/SCI01210
                AcademicSubjects/SCI01140

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