De novo genome assembly of a high-protein soybean variety HJ117

De novo genome assembly of a high-protein soybean variety HJ117 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Data Note De novo genome assembly of a high-protein soybean variety HJ117 Zhi Liu, Qing Yang, Bingqiang Liu, Chenhui Li, Xiaolei Shi, Yu Wei, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3804386/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Mar, 2024 Read the published version in BMC Genomic Data → Version 1 posted 7 You are reading this latest preprint version Abstract Objectives: Soybean is an important feed and oil crop in the world due to its high protein and oil content. China has a collection of more than 43,000 soybean germplasm resources, which presents a rich genetic diversity for soybean breeding. However, this diversity poses great challenges to the genetic improvement of soybean. This study reports on the de novo genome assembly of HJ117, a soybean variety with high protein content of 52%. These findings will prove to be valuable resources for further soybean quality improvement research, and will aid in the elucidation of regulatory mechanisms underlying soybean protein content. Data description: We generated a contiguous reference genome of 1041.94 Mb for our sample using a combination of Illumina short reads (23.38 Gb) and PacBio long reads (25.58 Gb), with high-quality sequence coverage of approximately 22.44× and 24.55×, respectively. The assembly was further assisted by 114.5 Gb Hi-C data (109.9×), resulting in a contig N50 of 19.32 Mb and scaffold N50 of 51.43 Mb. Notably, Core Eukaryotic Genes Mapping Approach (CEGMA) assessment and Benchmarking Universal Single-Copy Orthologs (BUSCO) assessment results indicated that most core eukaryotic genes (97.18%) and genes in the BUSCO dataset (99.4%) were identified, and 96.44% of the genomic sequences were anchored onto twenty pseudochromosomes. Soybean De novo assembly Genome feature high protein content Objectives Objective Soybean [ Glycine max (L.) Merr.] is an important protein feed and vegetable oil crop worldwide. The cultivation of soy enables the production of various valuable products, including edible oils, biodiesel, and biofertilizers. [ 1 ]. The main protein source in poultry and livestock feed is meal derived from soybean seeds. Commercial soybean cultivars generally have a seed protein content ranging from approximately 38–42% on a dry weight basis [ 2 ]. To produce meal with a protein content of at least 47.5% from a soybean cultivar, the seed protein content needs to be at least 41.5% on a dry weight basis [ 2 ]. Enhancing the amino acid content of soybean seeds would further increase the economic value of soybean. Soy protein content is influenced by complex factors such as genotype, environment, and genotype–environment interactions [ 3 , 4 ]. Due to the strong negative correlations of soy protein content and oil content [ 4 ] with yield [ 5 ], it is quite difficult to increase soy protein content. In the early stages of soybean breeding, farmers primarily relied on repeatedly selecting preferred seeds from cultivated populations. [ 6 ]. Following that, artificial hybridization technology was introduced, and the initial artificially hybridized cultivated soybean was introduced in North America during the 1940s [ 7 ]. With the development and progress of molecular biology technology, marker-assisted selection (MAS) has been employed to expedite the breeding process. [ 8 ]. The publication of the initial reference genome of soybean (cultivar Williams 82) in 2010 [ 9 ] signaled the commencement of the soybean functional genomics research era [ 10 , 11 ]. The enhancement of sequencing technologies has significantly boosted the capacity to generate high-quality genome assemblies. Data description The Glycine max sample was collected from Shijiazhuang (37°6′25″N, 114°42′47″E). Genomic DNA and total RNA were isolated from leaf tissues. High-quality DNA was extracted using QIAGEN® Genomic kits. Three methods were used to quantify and check the extracted DNA, NanoDrop 2000 Spectrophotometer (Thermo Fischer Scientific), agarose gel electrophoresis and Qubit Fluorometer (Invitrogen). After the detection, the DNA was purified using AMPure PB beads (Pacbio 100-265-900), and the subsequent library construction utilized the final high-quality genomic DNA (gDNA). The size and concentration of the library fragments were assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies, USA). Qualified libraries were evenly loaded on SMRT Cell and sequenced for 30 hours using Sequel II/IIe system (Pacific Biosciences, CA, USA). Briefly, the DNA sample was initially fixed with formaldehyde and subsequently digested using HindIII restriction enzyme. Next, the DNA ends underwent repair and were labeled with biotin. Subsequently, T4 DNA ligase was used to ligate the interacting fragments to form a loop. After ligation, protease K was added for cross-linking, and then protein of ligated DNA fragments was digested to obtain purified DNA. Finally, the purified DNA was fragmented into sizes ranging from 300 to 500 base pairs. The biotin-labeled DNA fragments were then isolated using Dynabeads® M-280 Streptavidin (Life Technologies). Subsequently, the Hi-C library was constructed and sequenced on the Illumina NovaSeq6000 sequencing platform using paired-end reads of 150 base pairs. To ensure the acquisition of high-quality data, the raw polymerase reads were subjected to quality control using the PacBio SMRT-Analysis package ( https://www.pacb.com ). This involved filtering out the following types of polymerase reads: (1) polymerase reads less than 50 bp in length, (2) Polymerase readings with a mass value below 0.8, (3) a polymerase read comprising an adaptor attached to itself and removing the adaptor sequence in the polymerase read. Then use SMRTLink 9.0 (parameter --min-passes = 3 --min-rq = 0.99) to generate CCS reads for subsequent assembly. Hifiasm ( https://github.com/chhylp123/hifiasm ) was employed to assemble the HiFi reads, and the preliminarily assembled genome version (primary contigs) was obtained. To obtain chromosome level genome, we performed Hi-C assisted assembly. For the ~ 114.5 Gb raw reads (Data file 1 and Data file 2), preliminary quality control was performed using Fastp [ 14 ], and the resulting clean reads were subsequently aligned to primary contigs using hicup. Valid pair reads were utilized for further analysis. AllHIC was used for auxiliary assembly, and then Juicebox was used for fine-tune AllHIC clustering results. Finally, A genome was obtained with a contig N50 length of 19.32 Mb and a total contig length of 1041.94 Mb, as well as a scaffold N50 length of 51.43 Mb and a total scaffold length of 1041.95 Mb (Data file 3 and Data file 4). To assess the quality of the assembly the self-written script was used to perform statistics on the number of single chromosome cluster scaffolds, chromosome sequence length, and genome mounting rate. According to the number of sequences assembled to the chromosome level and the number of sequences that were not assembled to the chromosome level, the Hi-C mounting rate was calculated. The chromosome-level genome was partitioned into 500 Kb bins of equal length. The number of Hi-C read pairs spanning any two bins was used as the intensity signal to represent the interaction between the respective bins. Heatmaps (Data file 5) were generated based on these signals. BUSCO (Benchmarking Universal Single-Copy Orthologs: http://busco.ezlab.org/ ) [ 18 ] was also applied to perform a quality assessment of the genome. The conserved genes (248 genes) existing in six eukaryotes were selected to construct the core gene library for CEGMA [ 19 ] evaluation. The evaluation results revealed that the majority of core eukaryotic genes (97.18%) and genes in the BUSCO dataset (99.4%) were successfully identified (Data file 6). Repeatmasker [ 21 ] and repeatproteinmask ( http://www.repeatmasker.org/ ) were employed to identify sequences that exhibit similarity to known repeat sequences. LTR_FINDER [ 22 ] was used to perform de novo prediction. Totally, 361,475,923 bp RepBase TEs and 453,714,080 bp de novo repetitive sequences were identified, respectively (Data file 7). Structural prediction of genes was performed by using AUGUSTUS ( http://bioinf.uni-greifswald.de/AUGUSTUS/ ) [ 24 ] (Data file 8 and Data file 9). Then, we used the protein databases NR ( https://ftp.ncbi.nlm.nih.gov/blast/db/FASTA/ ), SwissProt ( http://www.uniprot.org/ ), KEGG ( http://www.genome.jp/kegg/ ) and InterPro ( https://www.ebi.ac.uk/interpro/ ) to annotate the gene set obtained from the gene structure annotation. A total of 57,151 genes were predicted, with 54,550 of these genes being functionally annotated in the database (Data file 10). The circular plot illustrates gene density, transposable element (TE) density, and GC density (Data file 11). The tRNAscan-SE [ 29 ] ( http://lowelab.ucsc.edu/tRNAscan-SE/ ) was used to identify tRNA sequences within the genome. Blast [ 30 ] alignment was used to find the rRNA in the genome. The prediction of miRNA and snRNA sequences within the genome was performed using INFERNAL ( http://infernal.janelia.org/ ). The copy number of miRNA, tRNA, rRNA and snRNA ranged from 68 to 5,116 (Data file 12). Table 1 Overview of data files/data sets. Label Name of data file/data set File type (file extension) Data repository and identifier (DOI or accession number) Data file1 Statistics on sequence data Spreadsheet (.xls) https://figshare.com/s/6de11eca18b3ccef8314 [ 12 ] Data file2 Hi-C raw data Fastq file (.fastq) https://ngdc.cncb.ac.cn/gsa [ 13 ] Data file3 Assembly statistics of HJ117 Spreadsheet (.xls) https://figshare.com/s/6de11eca18b3ccef8314 [ 15 ] Data file4 genome.fa Fasta file (.fasta) https://figshare.com/s/6de11eca18b3ccef8314 [ 16 ] Data file5 Hi-C interaction heatmap Image file (.tif ) https://figshare.com/s/6de11eca18b3ccef8314 [ 17 ] Data file6 Assessment results of CEGMA and BUSCO Spreadsheet (.xls) https://figshare.com/s/6de11eca18b3ccef8314 [ 20 ] Data file7 Results of transposable element classification statistics Spreadsheet (.xls) https://figshare.com/s/6de11eca18b3ccef8314 [ 23 ] Data file8 Results of gene structure prediction Spreadsheet (.xls) https://figshare.com/s/6de11eca18b3ccef8314 [ 25 ] Data file9 Glycine.max.gene.gff Gff file (.gff) https://figshare.com/s/6de11eca18b3ccef8314 [ 26 ] Data file10 Genome annotation of HJ117 Spreadsheet (.xls) https://figshare.com/s/6de11eca18b3ccef8314 [ 27 ] Data file11 Overview of the HJ117 reference genome Image file (.tif ) https://figshare.com/s/6de11eca18b3ccef8314 [ 28 ] Data file12 Statistics on non-coding RNA annotation results Spreadsheet (.xls) https://figshare.com/s/6de11eca18b3ccef8314 [ 31 ] Data file13 Clean RNA reads of leaf tissues Fastq file (.fastq) https://ngdc.cncb.ac.cn/gsa [ 34 ] Data file14 Clean HiFi data Fastq file (.fastq) https://ngdc.cncb.ac.cn/gsa [ 35 ] Limitations Soybean is considered to have undergone an allotetraploidy event [ 9 ] that have resulted in 75% of its genes being present in multiple copies [ 32 ]. Repetitive DNA made up ~ 54.4% of each genome [ 33 ]. In this study, 23.38 Gb Illumina short reads (Data file 13) and 25.58 Gb of PacBio long reads (Data file 14) were obtained, providing approximately 22.44× and 24.55× sequence coverage. Although Hi-C sequencing obtained ~ 114.5 Gb of data with a depth of 109.9×, the overall sequencing depth was relatively low, which may result in incomplete genomic information being obtained. The contig N50 length of the de novo assembled HJ117 genome is 19.32 Mb, and the scaffold N50 reaches 51.43 Mb, indicating that the genome assembly level has achieved the average level of soybean genome assemblies during the same period. However, gaps still exist in the genome. To achieve accurate genome assembly, optical mapping technology could be incorporated, and HiFi sequencing depth could be increased in the later stages. Alternatively, HJ117 genome could be assembled to a telomere-to-telomere level using ONT Ultra-long technology to obtain more comprehensive genomic information for HJ117. Abbreviations CEGMA: Core Eukaryotic Genes Mapping Approach BUSCO: Benchmarking Universal Single-Copy Orthologs DNA: Deoxyribonucleic Acid RNA: Ribonucleic Acid TE: Transposable Element Hi-C: High-resolution Chromosome Conformation Capture HiFi: High-Fidelity Sequencing Declarations Ethics approval and consent to participate The current study complies with relevant institutional, national, and international guidelines and legislation for experimental research and field studies on plants (either cultivated or wild), including the collection of plant material. Permissions were obtained to collect Glycine max samples. Sampling was conducted in Institute of Cereal and Oil Crops (ICOC), Hebei Academy of Agricultural and Forestry Sciences field plots and permission was granted by the ICOC to perform data collection. Consent for publication Not applicable. Availability of data and materials Data files 2, 13, 14 described in this Data note can be freely and openly accessed on the Genome Sequence Archive in National Genomics Data Center China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences under GSA: CRA014073 (https://ngdc.cncb.ac.cn/gsa) [13,34,35]. Data files 1, 3-12 are available on Figshare (https://figshare.com/) [12,15,16,17,20,23,25,26,27,28,31]. Please see table 1 and references for details and links to the data. Competing interests The authors declare no competing interests. Funding This work was financially supported by the National Key R&D Project (2021YFD1201602), National Natural Science Foundation of China (31871652), and Natural Science Foundation of Hebei (C2020301020). Authors’ contributions Zhi Liu data curation and writing-original draft; Qing Yang visualization of the work; Bingqiang Liu project administration; Chenhui Li and Xiaolei Shi resources; Yu Wei data curation; Yuefeng Guan, Chunyan Yang, Mengchen Zhang supervision; Long Yan conceptualization and methodology. Acknowledgements Not applicable. References Vianna GR, Cunha NB, Rech EL. Soybean seed protein storage vacuoles for expression of recombinant molecules. Curr Opin Plant Biol. 2023;71:102331. https://doi.org/10.1016/j.pbi.2022.102331 Willis S. The use of soybean meal and full fat soybean meal by the animal feed industry. In: 12 th Australian soybean conference. Soy Australia, Bundaberg 2003. Carver B.F., Burton J.W., Carter T.E., Wilson R.F. Response to environmental variation of soybean lines selected for altered unsaturated fatty acid composition. Crop Sci. 1986;26:1176–1181. https://doi.org/10.2135/cropsci1986.0011183X002600060021x Chaudhary J, Patil GB, Sonah H, et al. Expanding Omics Resources for Improvement of Soybean Seed Composition Traits. Front Plant Sci. 2015;6:1021. https://doi.org/10.3389/fpls.2015.01021 Kim M, Schultz S, Nelson RL, Diers BW. Identification and fine mapping of a soybean seed protein QTL from PI 407788A on chromosome 15. Crop Sci. 2016;56:219–225. https://doi.org/10.2135/cropsci2015.06.0340 Zhang M, Liu S, Wang Z, et al. Progress in soybean functional genomics over the past decade. Plant Biotechnol J. 2022;20(2):256-282. https://doi.org/10.1111/pbi.13682 Rincker K, Nelson RL, Specht J, Sleper D, Cary T, Cianzio S, Casteel, S.et al. Genetic improvement of U.S. soybean in maturity groups II, III, and IV. Crop Sci. 2014;54, 1419–1432. https://doi.org/10.2135/cropsci2013.10.0665 Li MW, Wang Z, Jiang B, Kaga A, Wong FL, Zhang G, Han T. et al. Impacts of genomic research on soybean improvement in East Asia. Theor. Appl. Genet. 2020;133, 1655–1678. https://doi.org/10.1007/s00122-019-03462-6 Schmutz J, Cannon SB, Schlueter J, et al. Genome sequence of the palaeopolyploid soybean. Nature. 2010;463(7278):178-183. https://doi.org/10.1038/nature08670 Li MW, Xin D, Gao Y, et al. Using genomic information to improve soybean adaptability to climate change. J Exp Bot. 2017;68(8):1823-1834. https://doi.org/10.1093/jxb/erw348 Wang Z, Tian Z. Genomics progress will facilitate molecular breeding in soybean. Sci China Life Sci. 2015;58(8):813-815. https://doi.org/10.1007/s11427-015-4908-2 Data file 1: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023). Data file 2: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023). Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34(17):i884-i890. https://doi.org/10.1093/bioinformatics/bty560 Data file 3: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023). Data file 4: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023). Data file 5: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023). Simão FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31(19):3210-3212. https://doi.org/10.1093/bioinformatics/btv351 Parra G, Bradnam K, Korf I. CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes. Bioinformatics. 2007;23(9):1061-1067. https://doi.org/10.1093/bioinformatics/btm071 Data file 6: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023). Chen N. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr Protoc Bioinformatics. 2004;Chapter 4: https://doi.org/10.1002/0471250953.bi0410s05 Xu Z, Wang H. LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Res. 2007;35(Web Server issue):W265-W268. https://doi.org/10.1093/nar/gkm286 Data file 7: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023). Stanke M, Diekhans M, Baertsch R, Haussler D. Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics. 2008;24(5):637-644. https://doi.org/10.1093/bioinformatics/btn013 Data file 8: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023). Data file 9: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023). Data file 10: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023). Data file 11: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023). Lowe TM, Eddy SR. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 1997;25(5):955-964. https://doi.org/10.1093/nar/25.5.955 McGinnis S, Madden TL. BLAST: at the core of a powerful and diverse set of sequence analysis tools. Nucleic Acids Res. 2004;32(Web Server issue):W20-W25. https://doi.org/10.1093/nar/gkh435 Data file 12: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023). Roulin A, Auer PL, Libault M, et al. The fate of duplicated genes in a polyploid plant genome. Plant J. 2013;73(1):143-153. https://doi.org/10.1111/tpj.12026 Liu Y, Du H, Li P, et al. Pan-Genome of Wild and Cultivated Soybeans. Cell. 2020;182(1):162-176.e13. https://doi.org/10.1016/j.cell.2020.05.023 Data file 13: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023). Data file 14: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Mar, 2024 Read the published version in BMC Genomic Data → Version 1 posted Editorial decision: Revision requested 23 Jan, 2024 Reviews received at journal 19 Jan, 2024 Reviewers agreed at journal 10 Jan, 2024 Reviewers invited by journal 09 Jan, 2024 Submission checks completed at journal 07 Jan, 2024 Editor assigned by journal 07 Jan, 2024 First submitted to journal 25 Dec, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3804386","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Data Note","associatedPublications":[],"authors":[{"id":265803702,"identity":"8053cac2-d29f-472e-ba3a-a3478d74fcb3","order_by":0,"name":"Zhi Liu","email":"","orcid":"","institution":"National Soybean Improvement Center Shijiazhuang Sub- Center, Hebei Academy of Agricultural and Forestry Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zhi","middleName":"","lastName":"Liu","suffix":""},{"id":265803703,"identity":"e47dbbd2-fc45-47ab-834a-d939e23ffe77","order_by":1,"name":"Qing 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12:14:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3804386/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3804386/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12863-024-01213-1","type":"published","date":"2024-03-04T15:01:45+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":52432348,"identity":"7f4be5e5-91d3-4c87-b84e-af521872fbf7","added_by":"auto","created_at":"2024-03-11 15:12:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":220620,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3804386/v1/061636de-1f67-4610-8aa2-b375e0810541.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"De novo genome assembly of a high-protein soybean variety HJ117","fulltext":[{"header":"Objectives","content":"\u003cp\u003eObjective\u003c/p\u003e\n\u003cp\u003eSoybean [\u003cem\u003eGlycine max\u003c/em\u003e (L.) Merr.] is an important protein feed and vegetable oil crop worldwide. The cultivation of soy enables the production of various valuable products, including edible oils, biodiesel, and biofertilizers. [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. The main protein source in poultry and livestock feed is meal derived from soybean seeds. Commercial soybean cultivars generally have a seed protein content ranging from approximately 38–42% on a dry weight basis [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. To produce meal with a protein content of at least 47.5% from a soybean cultivar, the seed protein content needs to be at least 41.5% on a dry weight basis [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]. Enhancing the amino acid content of soybean seeds would further increase the economic value of soybean. Soy protein content is influenced by complex factors such as genotype, environment, and genotype–environment interactions [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]. Due to the strong negative correlations of soy protein content and oil content [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e] with yield [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e], it is quite difficult to increase soy protein content.\u003c/p\u003e\n\u003cp\u003eIn the early stages of soybean breeding, farmers primarily relied on repeatedly selecting preferred seeds from cultivated populations. [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]. Following that, artificial hybridization technology was introduced, and the initial artificially hybridized cultivated soybean was introduced in North America during the 1940s [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]. With the development and progress of molecular biology technology, marker-assisted selection (MAS) has been employed to expedite the breeding process. [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]. The publication of the initial reference genome of soybean (cultivar Williams 82) in 2010 [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e] signaled the commencement of the soybean functional genomics research era [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. The enhancement of sequencing technologies has significantly boosted the capacity to generate high-quality genome assemblies.\u003c/p\u003e\n\n\n\n"},{"header":"Data description","content":"\u003cp\u003eThe \u003cem\u003eGlycine max\u003c/em\u003e sample was collected from Shijiazhuang (37°6′25″N, 114°42′47″E). Genomic DNA and total RNA were isolated from leaf tissues. High-quality DNA was extracted using QIAGEN® Genomic kits. Three methods were used to quantify and check the extracted DNA, NanoDrop 2000 Spectrophotometer (Thermo Fischer Scientific), agarose gel electrophoresis and Qubit Fluorometer (Invitrogen). After the detection, the DNA was purified using AMPure PB beads (Pacbio 100-265-900), and the subsequent library construction utilized the final high-quality genomic DNA (gDNA). The size and concentration of the library fragments were assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies, USA). Qualified libraries were evenly loaded on SMRT Cell and sequenced for 30 hours using Sequel II/IIe system (Pacific Biosciences, CA, USA).\u003c/p\u003e\u003cp\u003eBriefly, the DNA sample was initially fixed with formaldehyde and subsequently digested using HindIII restriction enzyme. Next, the DNA ends underwent repair and were labeled with biotin. Subsequently, T4 DNA ligase was used to ligate the interacting fragments to form a loop. After ligation, protease K was added for cross-linking, and then protein of ligated DNA fragments was digested to obtain purified DNA. Finally, the purified DNA was fragmented into sizes ranging from 300 to 500 base pairs. The biotin-labeled DNA fragments were then isolated using Dynabeads® M-280 Streptavidin (Life Technologies). Subsequently, the Hi-C library was constructed and sequenced on the Illumina NovaSeq6000 sequencing platform using paired-end reads of 150 base pairs.\u003c/p\u003e\u003cp\u003eTo ensure the acquisition of high-quality data, the raw polymerase reads were subjected to quality control using the PacBio SMRT-Analysis package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.pacb.com\u003c/span\u003e\u003c/span\u003e). This involved filtering out the following types of polymerase reads: (1) polymerase reads less than 50 bp in length, (2) Polymerase readings with a mass value below 0.8, (3) a polymerase read comprising an adaptor attached to itself and removing the adaptor sequence in the polymerase read. Then use SMRTLink 9.0 (parameter --min-passes = 3 --min-rq = 0.99) to generate CCS reads for subsequent assembly.\u003c/p\u003e\u003cp\u003eHifiasm (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/chhylp123/hifiasm\u003c/span\u003e\u003c/span\u003e) was employed to assemble the HiFi reads, and the preliminarily assembled genome version (primary contigs) was obtained. To obtain chromosome level genome, we performed Hi-C assisted assembly. For the ~ 114.5 Gb raw reads (Data file 1 and Data file 2), preliminary quality control was performed using Fastp [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e], and the resulting clean reads were subsequently aligned to primary contigs using hicup. Valid pair reads were utilized for further analysis. AllHIC was used for auxiliary assembly, and then Juicebox was used for fine-tune AllHIC clustering results. Finally, A genome was obtained with a contig N50 length of 19.32 Mb and a total contig length of 1041.94 Mb, as well as a scaffold N50 length of 51.43 Mb and a total scaffold length of 1041.95 Mb (Data file 3 and Data file 4).\u003c/p\u003e\u003cp\u003eTo assess the quality of the assembly the self-written script was used to perform statistics on the number of single chromosome cluster scaffolds, chromosome sequence length, and genome mounting rate. According to the number of sequences assembled to the chromosome level and the number of sequences that were not assembled to the chromosome level, the Hi-C mounting rate was calculated. The chromosome-level genome was partitioned into 500 Kb bins of equal length. The number of Hi-C read pairs spanning any two bins was used as the intensity signal to represent the interaction between the respective bins. Heatmaps (Data file 5) were generated based on these signals. BUSCO (Benchmarking Universal Single-Copy Orthologs: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://busco.ezlab.org/\u003c/span\u003e\u003c/span\u003e) [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e] was also applied to perform a quality assessment of the genome. The conserved genes (248 genes) existing in six eukaryotes were selected to construct the core gene library for CEGMA [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e] evaluation. The evaluation results revealed that the majority of core eukaryotic genes (97.18%) and genes in the BUSCO dataset (99.4%) were successfully identified (Data file 6).\u003c/p\u003e\u003cp\u003eRepeatmasker [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e] and repeatproteinmask (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.repeatmasker.org/\u003c/span\u003e\u003c/span\u003e) were employed to identify sequences that exhibit similarity to known repeat sequences. LTR_FINDER [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e] was used to perform de novo prediction. Totally, 361,475,923 bp RepBase TEs and 453,714,080 bp de novo repetitive sequences were identified, respectively (Data file 7). Structural prediction of genes was performed by using AUGUSTUS (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinf.uni-greifswald.de/AUGUSTUS/\u003c/span\u003e\u003c/span\u003e) [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e] (Data file 8 and Data file 9). Then, we used the protein databases NR (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ftp.ncbi.nlm.nih.gov/blast/db/FASTA/\u003c/span\u003e\u003c/span\u003e), SwissProt (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.uniprot.org/\u003c/span\u003e\u003c/span\u003e), KEGG (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genome.jp/kegg/\u003c/span\u003e\u003c/span\u003e) and InterPro (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/interpro/\u003c/span\u003e\u003c/span\u003e) to annotate the gene set obtained from the gene structure annotation. A total of 57,151 genes were predicted, with 54,550 of these genes being functionally annotated in the database (Data file 10). The circular plot illustrates gene density, transposable element (TE) density, and GC density (Data file 11). The tRNAscan-SE [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://lowelab.ucsc.edu/tRNAscan-SE/\u003c/span\u003e\u003c/span\u003e) was used to identify tRNA sequences within the genome. Blast [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e] alignment was used to find the rRNA in the genome. The prediction of miRNA and snRNA sequences within the genome was performed using INFERNAL (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://infernal.janelia.org/\u003c/span\u003e\u003c/span\u003e). The copy number of miRNA, tRNA, rRNA and snRNA ranged from 68 to 5,116 (Data file 12). \u0026nbsp;\u003c/p\u003e\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eOverview of data files/data sets.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eLabel\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eName of data file/data set\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eFile type (file extension)\u003c/p\u003e\n \u003c/th\u003e\u003cth align=\"left\"\u003e\n \u003cp\u003eData repository and identifier (DOI or accession number)\u003c/p\u003e\n \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eData file1\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eStatistics on sequence data\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSpreadsheet (.xls)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://figshare.com/s/6de11eca18b3ccef8314\u003c/span\u003e\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eData file2\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eHi-C raw data\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eFastq file (.fastq)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ngdc.cncb.ac.cn/gsa\u003c/span\u003e\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eData file3\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eAssembly statistics of HJ117\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSpreadsheet (.xls)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://figshare.com/s/6de11eca18b3ccef8314\u003c/span\u003e\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eData file4\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003egenome.fa\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eFasta file (.fasta)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://figshare.com/s/6de11eca18b3ccef8314\u003c/span\u003e\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eData file5\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eHi-C interaction heatmap\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eImage file (.tif )\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://figshare.com/s/6de11eca18b3ccef8314\u003c/span\u003e\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eData file6\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eAssessment results of CEGMA and BUSCO\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSpreadsheet (.xls)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://figshare.com/s/6de11eca18b3ccef8314\u003c/span\u003e\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eData file7\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eResults of transposable element classification statistics\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSpreadsheet (.xls)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://figshare.com/s/6de11eca18b3ccef8314\u003c/span\u003e\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eData file8\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eResults of gene structure prediction\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSpreadsheet (.xls)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://figshare.com/s/6de11eca18b3ccef8314\u003c/span\u003e\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eData file9\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eGlycine.max.gene.gff\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eGff file (.gff)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://figshare.com/s/6de11eca18b3ccef8314\u003c/span\u003e\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eData file10\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eGenome annotation of HJ117\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSpreadsheet (.xls)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://figshare.com/s/6de11eca18b3ccef8314\u003c/span\u003e\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eData file11\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eOverview of the HJ117 reference genome\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eImage file (.tif )\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://figshare.com/s/6de11eca18b3ccef8314\u003c/span\u003e\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eData file12\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eStatistics on non-coding RNA annotation results\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eSpreadsheet (.xls)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://figshare.com/s/6de11eca18b3ccef8314\u003c/span\u003e\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eData file13\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eClean RNA reads of leaf tissues\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eFastq file (.fastq)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ngdc.cncb.ac.cn/gsa\u003c/span\u003e\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eData file14\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eClean HiFi data\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003eFastq file (.fastq)\u003c/p\u003e\n \u003c/td\u003e\u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ngdc.cncb.ac.cn/gsa\u003c/span\u003e\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e"},{"header":"Limitations","content":"\u003cp\u003eSoybean is considered to have undergone an allotetraploidy event [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e] that have resulted in 75% of its genes being present in multiple copies [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. Repetitive DNA made up ~ 54.4% of each genome [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. In this study, 23.38 Gb Illumina short reads (Data file 13) and 25.58 Gb of PacBio long reads (Data file 14) were obtained, providing approximately 22.44× and 24.55× sequence coverage. Although Hi-C sequencing obtained ~ 114.5 Gb of data with a depth of 109.9×, the overall sequencing depth was relatively low, which may result in incomplete genomic information being obtained.\u003c/p\u003e\u003cp\u003eThe contig N50 length of the de novo assembled HJ117 genome is 19.32 Mb, and the scaffold N50 reaches 51.43 Mb, indicating that the genome assembly level has achieved the average level of soybean genome assemblies during the same period. However, gaps still exist in the genome. To achieve accurate genome assembly, optical mapping technology could be incorporated, and HiFi sequencing depth could be increased in the later stages. Alternatively, HJ117 genome could be assembled to a telomere-to-telomere level using ONT Ultra-long technology to obtain more comprehensive genomic information for HJ117.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCEGMA: Core Eukaryotic Genes Mapping Approach\u003c/p\u003e\n\u003cp\u003eBUSCO: Benchmarking Universal Single-Copy Orthologs\u003c/p\u003e\n\u003cp\u003eDNA: Deoxyribonucleic Acid\u003c/p\u003e\n\u003cp\u003eRNA: Ribonucleic Acid\u003c/p\u003e\n\u003cp\u003eTE:\u0026nbsp;Transposable Element\u003c/p\u003e\n\u003cp\u003eHi-C: High-resolution Chromosome Conformation Capture\u003c/p\u003e\n\u003cp\u003eHiFi: High-Fidelity Sequencing\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003cbr\u003e\u0026nbsp;\u003c/strong\u003eThe current study complies with relevant institutional, national, and international guidelines and legislation for experimental research and field studies on plants (either cultivated or wild), including the collection of plant material. Permissions were obtained to collect \u003cem\u003eGlycine max\u003c/em\u003e samples. Sampling was conducted in Institute of Cereal and Oil Crops (ICOC), Hebei Academy of Agricultural and Forestry Sciences field plots and permission was granted by the ICOC to perform data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003cbr\u003e\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u0026nbsp;\u003cbr\u003eData files 2, 13, 14 described in this Data note can be freely and openly accessed on the Genome Sequence Archive in National Genomics Data Center China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences under GSA: CRA014073 (https://ngdc.cncb.ac.cn/gsa) [13,34,35].\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eData files 1, 3-12 are available on Figshare (https://figshare.com/) [12,15,16,17,20,23,25,26,27,28,31]. Please see table 1 and references for details and links to the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003cbr\u003e\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003cbr\u003e\u0026nbsp;\u003c/strong\u003eThis work was financially supported by the National Key R\u0026amp;D Project (2021YFD1201602), National Natural Science Foundation of China (31871652), and Natural Science Foundation of Hebei (C2020301020).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003cbr\u003e\u0026nbsp;\u003c/strong\u003eZhi Liu\u0026nbsp;data curation and writing-original draft;\u0026nbsp;Qing Yang\u0026nbsp;visualization of the work; Bingqiang Liu project administration; Chenhui Li and Xiaolei Shi resources; Yu Wei data curation; Yuefeng Guan, Chunyan Yang, Mengchen Zhang supervision; Long Yan conceptualization and methodology.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVianna GR, Cunha NB, Rech EL. Soybean seed protein storage vacuoles for expression of recombinant molecules. Curr Opin Plant Biol. 2023;71:102331. https://doi.org/10.1016/j.pbi.2022.102331\u003c/li\u003e\n\u003cli\u003eWillis S. The use of soybean meal and full fat soybean meal by the animal feed industry. In: 12\u003csup\u003eth\u003c/sup\u003e Australian soybean conference. Soy Australia, Bundaberg 2003.\u003c/li\u003e\n\u003cli\u003eCarver B.F., Burton J.W., Carter T.E., Wilson R.F. Response to environmental variation of soybean lines selected for altered unsaturated fatty acid composition. Crop Sci. 1986;26:1176\u0026ndash;1181. https://doi.org/10.2135/cropsci1986.0011183X002600060021x\u003c/li\u003e\n\u003cli\u003eChaudhary J, Patil GB, Sonah H, et al. Expanding Omics Resources for Improvement of Soybean Seed Composition Traits. Front Plant Sci. 2015;6:1021. https://doi.org/10.3389/fpls.2015.01021\u003c/li\u003e\n\u003cli\u003eKim M, Schultz S, Nelson RL, Diers BW. Identification and fine mapping of a soybean seed protein QTL from PI 407788A on chromosome 15. Crop Sci. 2016;56:219\u0026ndash;225. https://doi.org/10.2135/cropsci2015.06.0340\u003c/li\u003e\n\u003cli\u003eZhang M, Liu S, Wang Z, et al. Progress in soybean functional genomics over the past decade. Plant Biotechnol J. 2022;20(2):256-282. https://doi.org/10.1111/pbi.13682\u003c/li\u003e\n\u003cli\u003eRincker K, Nelson RL, Specht J, Sleper D, Cary T, Cianzio S, Casteel, S.et al. Genetic improvement of U.S. soybean in maturity groups II, III, and IV. Crop Sci. 2014;54, 1419\u0026ndash;1432. https://doi.org/10.2135/cropsci2013.10.0665\u003c/li\u003e\n\u003cli\u003eLi MW, Wang Z, Jiang B, Kaga A, Wong FL, Zhang G, Han T. et al. Impacts of genomic research on soybean improvement in East Asia. Theor. Appl. Genet. 2020;133, 1655\u0026ndash;1678. https://doi.org/10.1007/s00122-019-03462-6\u003c/li\u003e\n\u003cli\u003eSchmutz J, Cannon SB, Schlueter J, et al. Genome sequence of the palaeopolyploid soybean. Nature. 2010;463(7278):178-183. https://doi.org/10.1038/nature08670 \u003c/li\u003e\n\u003cli\u003eLi MW, Xin D, Gao Y, et al. Using genomic information to improve soybean adaptability to climate change. J Exp Bot. 2017;68(8):1823-1834. https://doi.org/10.1093/jxb/erw348\u003c/li\u003e\n\u003cli\u003eWang Z, Tian Z. Genomics progress will facilitate molecular breeding in soybean. Sci China Life Sci. 2015;58(8):813-815. https://doi.org/10.1007/s11427-015-4908-2\u003c/li\u003e\n\u003cli\u003eData file 1: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023).\u003c/li\u003e\n\u003cli\u003eData file 2: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023).\u003c/li\u003e\n\u003cli\u003eChen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34(17):i884-i890. https://doi.org/10.1093/bioinformatics/bty560\u003c/li\u003e\n\u003cli\u003eData file 3: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023).\u003c/li\u003e\n\u003cli\u003eData file 4: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023).\u003c/li\u003e\n\u003cli\u003eData file 5: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023).\u003c/li\u003e\n\u003cli\u003eSim\u0026atilde;o FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics. 2015;31(19):3210-3212. https://doi.org/10.1093/bioinformatics/btv351\u003c/li\u003e\n\u003cli\u003eParra G, Bradnam K, Korf I. CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes. Bioinformatics. 2007;23(9):1061-1067. https://doi.org/10.1093/bioinformatics/btm071\u003c/li\u003e\n\u003cli\u003eData file 6: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023).\u003c/li\u003e\n\u003cli\u003eChen N. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr Protoc Bioinformatics. 2004;Chapter 4: https://doi.org/10.1002/0471250953.bi0410s05\u003c/li\u003e\n\u003cli\u003eXu Z, Wang H. LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Res. 2007;35(Web Server issue):W265-W268. https://doi.org/10.1093/nar/gkm286\u003c/li\u003e\n\u003cli\u003eData file 7: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023).\u003c/li\u003e\n\u003cli\u003eStanke M, Diekhans M, Baertsch R, Haussler D. Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics. 2008;24(5):637-644. https://doi.org/10.1093/bioinformatics/btn013\u003c/li\u003e\n\u003cli\u003eData file 8: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023).\u003c/li\u003e\n\u003cli\u003eData file 9: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023).\u003c/li\u003e\n\u003cli\u003eData file 10: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023).\u003c/li\u003e\n\u003cli\u003eData file 11: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023).\u003c/li\u003e\n\u003cli\u003eLowe TM, Eddy SR. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 1997;25(5):955-964. https://doi.org/10.1093/nar/25.5.955\u003c/li\u003e\n\u003cli\u003eMcGinnis S, Madden TL. BLAST: at the core of a powerful and diverse set of sequence analysis tools. Nucleic Acids Res. 2004;32(Web Server issue):W20-W25. https://doi.org/10.1093/nar/gkh435\u003c/li\u003e\n\u003cli\u003eData file 12: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023).\u003c/li\u003e\n\u003cli\u003eRoulin A, Auer PL, Libault M, et al. The fate of duplicated genes in a polyploid plant genome. Plant J. 2013;73(1):143-153. https://doi.org/10.1111/tpj.12026\u003c/li\u003e\n\u003cli\u003eLiu Y, Du H, Li P, et al. Pan-Genome of Wild and Cultivated Soybeans. Cell. 2020;182(1):162-176.e13. https://doi.org/10.1016/j.cell.2020.05.023\u003c/li\u003e\n\u003cli\u003eData file 13: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023).\u003c/li\u003e\n\u003cli\u003eData file 14: De novo genome assembly of a high-protein soybean variety-HJ117. Figshare. 2023. https://figshare.com/s/6de11eca18b3ccef8314 (2023).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomic-data","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gtic","sideBox":"Learn more about [BMC Genomic Data](http://bmcgenet.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gtic/default.aspx","title":"BMC Genomic Data","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Soybean, De novo assembly, Genome feature, high protein content","lastPublishedDoi":"10.21203/rs.3.rs-3804386/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3804386/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjectives: \u003cbr\u003e\n Soybean is an important feed and oil crop in the world due to its high protein and oil content. China has a collection of more than 43,000 soybean germplasm resources, which presents a rich genetic diversity for soybean breeding. However, this diversity poses great challenges to the genetic improvement of soybean. This study reports on the de novo genome assembly of HJ117, a soybean variety with high protein content of 52%. These findings will prove to be valuable resources for further soybean quality improvement research, and will aid in the elucidation of regulatory mechanisms underlying soybean protein content.\u003c/p\u003e\n\u003cp\u003eData description: \u003cbr\u003e\nWe generated a contiguous reference genome of 1041.94 Mb for our sample using a combination of Illumina short reads (23.38 Gb) and PacBio long reads (25.58 Gb), with high-quality sequence coverage of approximately 22.44× and 24.55×, respectively. The assembly was further assisted by 114.5 Gb Hi-C data (109.9×), resulting in a contig N50 of 19.32 Mb and scaffold N50 of 51.43 Mb. Notably, Core Eukaryotic Genes Mapping Approach (CEGMA) assessment and Benchmarking Universal Single-Copy Orthologs (BUSCO) assessment results indicated that most core eukaryotic genes (97.18%) and genes in the BUSCO dataset (99.4%) were identified, and 96.44% of the genomic sequences were anchored onto twenty pseudochromosomes.\u003c/p\u003e","manuscriptTitle":"De novo genome assembly of a high-protein soybean variety HJ117","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-09 22:00:09","doi":"10.21203/rs.3.rs-3804386/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-01-23T09:42:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-01-19T13:56:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"590382fa-966b-4656-a410-377681b21836","date":"2024-01-10T14:48:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-09T11:50:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-08T04:38:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-08T04:38:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomic Data","date":"2023-12-25T12:12:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomic-data","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gtic","sideBox":"Learn more about [BMC Genomic Data](http://bmcgenet.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gtic/default.aspx","title":"BMC Genomic Data","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"748219c3-1f13-4ae4-a9d1-89ed6e15b62e","owner":[],"postedDate":"January 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-03-11T15:10:35+00:00","versionOfRecord":{"articleIdentity":"rs-3804386","link":"https://doi.org/10.1186/s12863-024-01213-1","journal":{"identity":"bmc-genomic-data","isVorOnly":false,"title":"BMC Genomic Data"},"publishedOn":"2024-03-04 15:01:45","publishedOnDateReadable":"March 4th, 2024"},"versionCreatedAt":"2024-01-09 22:00:09","video":"","vorDoi":"10.1186/s12863-024-01213-1","vorDoiUrl":"https://doi.org/10.1186/s12863-024-01213-1","workflowStages":[]},"version":"v1","identity":"rs-3804386","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3804386","identity":"rs-3804386","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}