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Advancing Soybean Improvement: Multi-Omics Strategies, Cutting-Edge Techniques and Bioinformatics Innovations

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A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Genetics, Genomics and Biotechnology".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 16151

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Special Issue Editor

Dr. Bahram Samanfar

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Agriculture and Agri-Food Canada, Ottawa Research and Development Centre, Ottawa, ON K1A 0C6, Canada
Interests: genomics/proteomics and transcriptomics of soybean; cell and molecular biology; CRISPR; plant biotechnology; genetics and genomics; DNA-based markers; allele-specific marker developments; systems biology; molecular breeding; host–pathogen interactions; bioinformatics; computational biology; QTL and GWAS analysis; time of flowering and maturity (genetics of photoperiod sensitivity) in soybean; soybean seed protein content; food allergy; microbiology

Special Issue Information

Dear Colleagues,

This Special Issue, “Advancing Soybean Improvement: Multi-Omics Strategies, Cutting-Edge Techniques and Bioinformatics Innovations”, focuses on the application of multi-omics approaches and bioinformatics tools in soybean research with the ultimate goal of enhancing soybean improvement efforts. Soybean is one of the most important legume crops, providing a significant source of protein and oil for human and animal consumption. However, soybean production faces numerous challenges, including biotic and abiotic stresses, which can significantly impact yield and quality.

This Special Issue aims to highlight the latest advances in multi-omics approaches, including genomics, transcriptomics, proteomics, and metabolomics, and their integration with bioinformatics tools in soybean research. The Special Issue will cover a wide range of topics, including, but not limited to, the identification and functional characterization of genes and pathways associated with important agronomic traits; understanding the molecular mechanisms underlying soybean responses to biotic and abiotic stresses; the exploration of soybean genetic diversity and population genomics; and the utilization of bioinformatics tools for data integration, analysis, and visualization in soybean research.

Contributions to this Special Issue may include original research articles, reviews, and perspectives that provide novel insights, methodologies, and applications of multi-omics approaches and bioinformatics in soybean improvement. The Special Issue will serve as a valuable resource for researchers, scientists, and practitioners working in the field of soybean research, plant breeding, genomics, and bioinformatics, and contribute to the advancement of soybean improvement strategies through cutting-edge multi-omics approaches and bioinformatics tools.

Dr. Bahram Samanfar
Guest Editor

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Keywords

glycine max
multi-omics approaches
bioinformatics tools
genomics research
transcriptomics analysis
proteomics profiling
metabolomics investigations
soybean improvement strategies
biotic stress responses
abiotic stress tolerance
molecular mechanism elucidation
genetic diversity assessment
population genomics studies
data integration techniques
advanced data analysis methods

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Published Papers (11 papers)

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Research

Jump to: Review, Other

17 pages, 6356 KiB

Open AccessArticle

Knockout of GmCKX3 Enhances Soybean Seed Yield via Cytokinin-Mediated Cell Expansion and Lipid Accumulation

by Xia Li, Xueyan Qian, Fangfang Zhao, Lu Niu, Yan Zhang, Siping Han, Dongyun Hao and Ziqi Chen

Plants 2025, 14(14), 2207; https://doi.org/10.3390/plants14142207 - 16 Jul 2025

Abstract

Soybean is a dual-purpose crop for food and oil, playing a crucial role in China’s grain production. Seed size and weight are key agronomic traits directly influencing the yield. Cytokinin oxidases/dehydrogenases (CKXs) specifically degrade certain isoforms of endogenous cytokinins (CKs), thereby modulating plant growth and seed development. However, their role in soybeans remains largely uncharacterized. In a previous genome-wide association study of 250 soybean core germplasms, we identified GmCKX3 as a yield-related gene. To elucidate its function, we developed GmCKX3-deficient mutants using CRISPR/Cas9 gene editing in soybean Williams82 and conducted a three-year phenotypic analysis. Loss of GmCKX3 function significantly enhanced the seed size and weight, which was attributed to an increased cell size and fat accumulation in the endosperm. This enhancement was driven by elevated endogenous CK levels resulting from suppressed GmCKX3 expression. Subcellular localization revealed that GmCKX3 resides in the endoplasmic reticulum and predominantly degrades the isopentenyladenine (iP)-type CK. Integrated transcriptomic and metabolomic analyses uncovered key genes and pathways involved in CK regulation, supporting GmCKX3’s central role in seed-trait modulation. These findings advance our understanding of cytokinin-mediated seed development and offer promising targets for molecular breeding aimed at improving the soybean yield. Full article

(This article belongs to the Special Issue Advancing Soybean Improvement: Multi-Omics Strategies, Cutting-Edge Techniques and Bioinformatics Innovations)

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19 pages, 871 KiB

Open AccessArticle

Multi-Locus GWAS Mapping and Candidate Gene Analysis of Anticancer Peptide Lunasin in Soybean (Glycine max L. Merr.)

by Rikki Locklear, Jennifer Kusumah, Layla Rashad, Felecia Lugaro, Sonia Viera, Nathan Kipyego, Faith Kipkosgei, Daisy Jerop, Shirley Jacquet, My Abdelmajid Kassem, Jiazheng Yuan, Elvira de Mejia and Rouf Mian

Plants 2025, 14(14), 2169; https://doi.org/10.3390/plants14142169 - 14 Jul 2025

Viewed by 77

Abstract

Soybean (Glycine max) peptide lunasin exhibits significant cancer-preventive, antioxidant, and hypocholesterolemic effects. This study aimed to identify quantitative trait nucleotides (QTNs) associated with lunasin content and to annotate the candidate genes in the soybean genome. The mapping panel of 144 accessions was gathered from the USDA Soybean Germplasm Collection, encompassing diverse geographical origins and genetic backgrounds, and was genotyped using SoySNP50K iSelect Beadchips. The lunasin content in soybean seeds was measured using the enzyme-linked immunosorbent assay (ELISA) method, with lipid-adjusted soybean flour prepared from seeds obtained from the Germplasm Resource Information Network (GRIN) of USDA-ARS in 2003 and from North Carolina in 2021, respectively. QTNs significantly related to lunasin content in soybean seeds were detected on 15 chromosomes, with LOD scores greater than 3.0, explaining various phenotypic variations identified using the R package mrMLM (v4.0). Significant QTNs on chromosomes 3, 13, 16, 18, and 20 were consistently identified across multiple models as being significantly associated with soybean lunasin content, based on assessment data from two years. Twenty-nine candidate genes were found, with 12 identified in seeds from 2003 and 17 from 2021. Our study is an important effort to understand the genetic basis and functional genes for lunasin production in soybean seeds. Full article

(This article belongs to the Special Issue Advancing Soybean Improvement: Multi-Omics Strategies, Cutting-Edge Techniques and Bioinformatics Innovations)

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28 pages, 1734 KiB

Open AccessArticle

Autofluorescence and Metabotyping of Soybean Varieties Using Confocal Laser Microscopy and High-Resolution Mass Spectrometric Approaches

by Mayya P. Razgonova, Muhammad A. Navaz, Ekaterina S. Butovets, Ludmila M. Lukyanchuk, Olga A. Chunikhina, Sezai Ercişli, Alexei N. Emelyanov and Kirill S. Golokhvast

Plants 2025, 14(13), 1995; https://doi.org/10.3390/plants14131995 - 30 Jun 2025

Viewed by 342

Abstract

This research examines a detailed metabolomic and comparative analysis of bioactive substances of soybean varieties: “Primorskaya-4”, “Primorskaya-86”, “Primorskaya-96”, “Locus”, “Sphere”, “Breeze”, “Namul”, and “Musson” by the laser confocal microscope CLSM 800 and the mass spectrometry of bioactive compounds by tandem mass spectrometry. The laser microscopy allowed us to clarify in detail the spatial arrangement of phenolic acids, flavonols, and anthocyanin contents in soybeans. Research has convincingly shown that the polyphenolic content of soybeans, and, in particular, the anthocyanins, are spatially localized mainly in the seed coat of soybeans. Tandem mass spectrometry was used to identify chemical constituents in soybean extracts. The results of initial studies revealed the presence of one hundred and fourteen compounds; sixty-nine of the target analytes were tentatively identified as compounds from polyphenol groups. Full article

(This article belongs to the Special Issue Advancing Soybean Improvement: Multi-Omics Strategies, Cutting-Edge Techniques and Bioinformatics Innovations)

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24 pages, 8787 KiB

Open AccessArticle

Fine Mapping of QTLs/QTNs and Mining of Genes Associated with Race 7 of the Soybean Cercospora sojina by Combining Linkages and GWAS

by Yanzuo Liu, Bo Hu, Aitong Yu, Yuxi Liu, Pengfei Xu, Yang Wang, Junjie Ding, Shuzhen Zhang, Wen-Xia Li and Hailong Ning

Plants 2025, 14(13), 1988; https://doi.org/10.3390/plants14131988 - 29 Jun 2025

Viewed by 253

Abstract

Soybean frogeye leaf spot (FLS) disease has been reported globally and is caused by the fungus Cercospora sojina, which affects the growth, seed yield, and quality of soybean. Among the 15 physiological microspecies of C. sojina soybean in China, Race 7 is one of the main pathogenic microspecies. A few genes are involved in resistance to FLS, and they cannot meet the need to design molecular breeding methods for disease resistance. In this study, a soybean recombinant inbred line (RIL3613) population and a germplasm resource (GP) population were planted at two sites, Acheng (AC) and Xiangyang (XY). Phenotypic data on the percentage of leaf area diseased (PLAD) in soybean leaves were obtained via image recognition technology after the inoculation of seven physiological species and full onset at the R3 stage. Quantitative trait loci (QTLs) and quantitative trait nucleotides (QTNs) were mapped via linkage analysis and genome-wide association studies (GWASs), respectively. The resistance genes of FLS were subsequently predicted in the linkage disequilibrium region of the collocated QTN. We identified 114 QTLs and 18 QTNs in the RIL3613 and GP populations, respectively. A total of 14 QTN loci were colocalized in the two populations, six of which presented high phenotypic contributions. Through haplotype–phenotype association analysis and expression quantification, three genes (Glyma.06G300100, Glyma.06G300600, and Glyma.13G172300) located near molecular markers AX-90524088 and AX-90437152 (QTNs) are associated with FLS Chinese Race 7, identifying them as potential candidate resistance genes. These results provide a theoretical basis for the genetic mining of soybean antigray spot No. 7 physiological species. These findings also provide a theoretical basis for understanding the genetic mechanism underlying FLS resistance in soybeans. Full article

(This article belongs to the Special Issue Advancing Soybean Improvement: Multi-Omics Strategies, Cutting-Edge Techniques and Bioinformatics Innovations)

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17 pages, 6289 KiB

Open AccessArticle

Genome-Wide Identification of the Soybean AlkB Homologue Gene Family and Functional Characterization of GmALKBH10Bs as RNA m⁶A Demethylases and Expression Patterns under Abiotic Stress

by Jie Zhao, Tengfeng Yang, Peng Liu, Huijie Liu, Hui Zhang, Sichao Guo, Xiaoye Liu, Xiaoguang Chen and Mingjia Chen

Plants 2024, 13(17), 2491; https://doi.org/10.3390/plants13172491 - 5 Sep 2024

Cited by 5 | Viewed by 1905

Abstract

Soybean (Glycine max (L.) Merr) is one of the most important crops worldwide, but its yield is vulnerable to abiotic stresses. In Arabidopsis, the AlkB homologue (ALKBH) family genes plays a crucial role in plant development and stress response. However, the identification and functions of its homologous genes in soybean remain obscured. Here, we identified a total of 22 ALKBH genes in soybean and classified them into seven subfamilies according to phylogenetic analysis. Gene duplication events among the family members and gene structure, conserved domains, and motifs of all candidate genes were analyzed. By comparing the changes in the m⁶A levels on mRNA from hair roots between soybean seedlings harboring the empty vector and those harboring the GmALKBH10B protein, we demonstrated that all four GmALKBH10B proteins are bona fide m⁶A RNA demethylases in vivo. Subcellular localization and expression patterns of the GmALKBH10B revealed that they might be functionally redundant. Furthermore, an analysis of cis-elements coupled with gene expression data demonstrated that GmALKBH10B subfamily genes, including GmALKBH10B1, GmALKBH10B2, GmALKBH10B3, and GmALKBH10B4, are likely involved in the cis-elements’ response to various environmental stimuli. In summary, our study is the first to report the genome-wide identification of GmALKBH family genes in soybean and to determine the function of GmALKBH10B proteins as m⁶A RNA demethylases, providing insights into GmALKBH10B genes in response to abiotic stresses. Full article

(This article belongs to the Special Issue Advancing Soybean Improvement: Multi-Omics Strategies, Cutting-Edge Techniques and Bioinformatics Innovations)

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13 pages, 1969 KiB

Open AccessArticle

QTL Detection of Salt Tolerance at Soybean Seedling Stage Based on Genome-Wide Association Analysis and Linkage Analysis

by Maolin Sun, Tianxin Zhao, Shuang Liu, Jinfeng Han, Yuhe Wang, Xue Zhao, Yongguang Li, Weili Teng, Yuhang Zhan and Yingpeng Han

Plants 2024, 13(16), 2283; https://doi.org/10.3390/plants13162283 - 16 Aug 2024

Cited by 1 | Viewed by 1410

Abstract

The utilization of saline land is a global challenge, and cultivating salt-tolerant soybean varieties is beneficial for improving the efficiency of saline land utilization. Exploring the genetic basis of salt-tolerant soybean varieties and developing salt-tolerant molecular markers can effectively promote the process of soybean salt-tolerant breeding. In the study, the membership function method was used to evaluate seven traits related to salt tolerance and comprehensive salt tolerance at the soybean seedling stage; genome-wide association analysis (GWAS) was performed in a natural population containing 200 soybean materials; and linkage analysis was performed in 112 recombinant inbred lines (RIL) population to detect quantitative trait loci (QTLs) of salt tolerance. In the GWAS, 147 SNPs were mapped, explaining 5.28–17.16% of phenotypic variation. In the linkage analysis, 10 QTLs were identified, which could explain 6.9–16.16% of phenotypic variation. And it was found that there were two co-located regions between the natural population and the RIL population, containing seven candidate genes of salt tolerance in soybean. In addition, one colocalization interval was found to contain qZJS-15-1, rs47665107, and rs4793412, all of which could explain more than 10% of phenotypic variation rates, making it suitable for molecular marker development. The physical positions of rs47665107 and rs47934112 were included in qZJS-15-1. Therefore, a KASP marker was designed and developed using Chr. 15:47907445, which was closely linked to the qZJS-15-1. This marker could accurately and clearly cluster the materials of salt-tolerant genotypes in the heterozygous population tested. The QTLs and KASP markers found in the study provide a theoretical and technical basis for accelerating the salt-tolerant breeding of soybean. Full article

(This article belongs to the Special Issue Advancing Soybean Improvement: Multi-Omics Strategies, Cutting-Edge Techniques and Bioinformatics Innovations)

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17 pages, 2819 KiB

Open AccessArticle

Selective Genotyping and Phenotyping for Optimization of Genomic Prediction Models for Populations with Different Diversity

by Marina Ćeran, Vuk Đorđević, Jegor Miladinović, Marjana Vasiljević, Vojin Đukić, Predrag Ranđelović and Simona Jaćimović

Plants 2024, 13(7), 975; https://doi.org/10.3390/plants13070975 - 28 Mar 2024

Cited by 1 | Viewed by 1704

Abstract

To overcome the different challenges to food security caused by a growing population and climate change, soybean (Glycine max (L.) Merr.) breeders are creating novel cultivars that have the potential to improve productivity while maintaining environmental sustainability. Genomic selection (GS) is an advanced approach that may accelerate the rate of genetic gain in breeding using genome-wide molecular markers. The accuracy of genomic selection can be affected by trait architecture and heritability, marker density, linkage disequilibrium, statistical models, and training set. The selection of a minimal and optimal marker set with high prediction accuracy can lower genotyping costs, computational time, and multicollinearity. Selective phenotyping could reduce the number of genotypes tested in the field while preserving the genetic diversity of the initial population. This study aimed to evaluate different methods of selective genotyping and phenotyping on the accuracy of genomic prediction for soybean yield. The evaluation was performed on three populations: recombinant inbred lines, multifamily diverse lines, and germplasm collection. Strategies adopted for marker selection were as follows: SNP (single nucleotide polymorphism) pruning, estimation of marker effects, randomly selected markers, and genome-wide association study. Reduction of the number of genotypes was performed by selecting a core set from the initial population based on marker data, yet maintaining the original population’s genetic diversity. Prediction ability using all markers and genotypes was different among examined populations. The subsets obtained by the model-based strategy can be considered the most suitable for marker selection for all populations. The selective phenotyping based on makers in all cases had higher values of prediction ability compared to minimal values of prediction ability of multiple cycles of random selection, with the highest values of prediction obtained using AN approach and 75% population size. The obtained results indicate that selective genotyping and phenotyping hold great potential and can be integrated as tools for improving or retaining selection accuracy by reducing genotyping or phenotyping costs for genomic selection. Full article

(This article belongs to the Special Issue Advancing Soybean Improvement: Multi-Omics Strategies, Cutting-Edge Techniques and Bioinformatics Innovations)

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10 pages, 2652 KiB

Open AccessArticle

Inheritance of Early Stomatal Closure Trait in Soybean: Ellis × N09-13890 Population

by Avat Shekoofa, Victoria Moser, Kripa Dhakal, Isha Poudel and Vince Pantalone

Plants 2023, 12(18), 3227; https://doi.org/10.3390/plants12183227 - 11 Sep 2023

Cited by 1 | Viewed by 1700

Abstract

Drought conditions exhibit various physiological and morphological changes in crops and thus reduce crop growth and yield. In order to mitigate the negative impacts of drought stress on soybean (Glycine max L. Merr.) production, identification and selection of genotypes that are best adapted to limited water availability in a specific environmental condition can be an effective strategy. This study aimed to assess the inheritance of early stomatal closure traits in soybeans using a population of recombinant inbred lines (RILs) derived from a cross between N09-13890 and Ellis. Thirty soybean lines were subjected to progressive water-deficit stress using a dry-down experiment. The experiment was conducted from June to November 2022 at the West Tennessee Research and Education Center (WTREC), University of Tennessee in Jackson, TN, under controlled environment conditions. This study identified significant differences among soybean lines in their early stomatal closure thresholds. The fraction of transpirable soil water (FTSW) thresholds among 30 tested lines ranged from 0.18 to 0.80, at which the decline in transpiration with soil drying was observed. Almost 65% of the RILs had FTSW threshold values between 0.41 to 0.80. These results, indicating inheritance, are supportive of the expression of early stomatal closure trait in progeny lines at a high level in cultivar development for water-deficit stress conditions. Thus, identifying the differences in genotypes of water use and their response to water-deficit stress conditions can provide a foundation for selecting new cultivars that are best adapted to arid and semi-arid agricultural production systems. Full article

(This article belongs to the Special Issue Advancing Soybean Improvement: Multi-Omics Strategies, Cutting-Edge Techniques and Bioinformatics Innovations)

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20 pages, 3565 KiB

Open AccessArticle

Application of SVR-Mediated GWAS for Identification of Durable Genetic Regions Associated with Soybean Seed Quality Traits

by Mohsen Yoosefzadeh-Najafabadi, Sepideh Torabi, Dan Tulpan, Istvan Rajcan and Milad Eskandari

Plants 2023, 12(14), 2659; https://doi.org/10.3390/plants12142659 - 16 Jul 2023

Cited by 7 | Viewed by 3054

Abstract

Soybean (Glycine max L.) is an important food-grade strategic crop worldwide because of its high seed protein and oil contents. Due to the negative correlation between seed protein and oil percentage, there is a dire need to detect reliable quantitative trait loci (QTL) underlying these traits in order to be used in marker-assisted selection (MAS) programs. Genome-wide association study (GWAS) is one of the most common genetic approaches that is regularly used for detecting QTL associated with quantitative traits. However, the current approaches are mainly focused on estimating the main effects of QTL, and, therefore, a substantial statistical improvement in GWAS is required to detect associated QTL considering their interactions with other QTL as well. This study aimed to compare the support vector regression (SVR) algorithm as a common machine learning method to fixed and random model circulating probability unification (FarmCPU), a common conventional GWAS method in detecting relevant QTL associated with soybean seed quality traits such as protein, oil, and 100-seed weight using 227 soybean genotypes. The results showed a significant negative correlation between soybean seed protein and oil concentrations, with heritability values of 0.69 and 0.67, respectively. In addition, SVR-mediated GWAS was able to identify more relevant QTL underlying the target traits than the FarmCPU method. Our findings demonstrate the potential use of machine learning algorithms in GWAS to detect durable QTL associated with soybean seed quality traits suitable for genomic-based breeding approaches. This study provides new insights into improving the accuracy and efficiency of GWAS and highlights the significance of using advanced computational methods in crop breeding research. Full article

(This article belongs to the Special Issue Advancing Soybean Improvement: Multi-Omics Strategies, Cutting-Edge Techniques and Bioinformatics Innovations)

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Review

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28 pages, 570 KiB

Open AccessReview

Harnessing Multi-Omics Strategies and Bioinformatics Innovations for Advancing Soybean Improvement: A Comprehensive Review

by Siwar Haidar, Julia Hooker, Simon Lackey, Mohamad Elian, Nathalie Puchacz, Krzysztof Szczyglowski, Frédéric Marsolais, Ashkan Golshani, Elroy R. Cober and Bahram Samanfar

Plants 2024, 13(19), 2714; https://doi.org/10.3390/plants13192714 - 28 Sep 2024

Cited by 4 | Viewed by 2736

Abstract

Soybean improvement has entered a new era with the advent of multi-omics strategies and bioinformatics innovations, enabling more precise and efficient breeding practices. This comprehensive review examines the application of multi-omics approaches in soybean—encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics. We first explore pre-breeding and genomic selection as tools that have laid the groundwork for advanced trait improvement. Subsequently, we dig into the specific contributions of each -omics field, highlighting how bioinformatics tools and resources have facilitated the generation and integration of multifaceted data. The review emphasizes the power of integrating multi-omics datasets to elucidate complex traits and drive the development of superior soybean cultivars. Emerging trends, including novel computational techniques and high-throughput technologies, are discussed in the context of their potential to revolutionize soybean breeding. Finally, we address the challenges associated with multi-omics integration and propose future directions to overcome these hurdles, aiming to accelerate the pace of soybean improvement. This review serves as a crucial resource for researchers and breeders seeking to leverage multi-omics strategies for enhanced soybean productivity and resilience. Full article

(This article belongs to the Special Issue Advancing Soybean Improvement: Multi-Omics Strategies, Cutting-Edge Techniques and Bioinformatics Innovations)

Other

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14 pages, 770 KiB

Open AccessBrief Report

Genome-Wide Association Study on Imputed Genotypes of 180 Eurasian Soybean Glycine max Varieties for Oil and Protein Contents in Seeds

by Nadezhda A. Potapova, Irina V. Zorkoltseva, Alexander S. Zlobin, Andrey B. Shcherban, Anna V. Fedyaeva, Elena A. Salina, Gulnara R. Svishcheva, Tatiana I. Aksenovich and Yakov A. Tsepilov

Plants 2025, 14(2), 255; https://doi.org/10.3390/plants14020255 - 17 Jan 2025

Cited by 1 | Viewed by 1114

Abstract

Soybean (Glycine max) is a leguminous plant with a broad range of applications, particularly in agriculture and food production, where its seed composition—especially oil and protein content—is highly valued. Improving these traits is a primary focus of soybean breeding programs. In this study, we conducted a genome-wide association study (GWAS) to identify genetic loci linked to oil and protein content in seeds, using imputed genotype data for 180 Eurasian soybean varieties and the novel “genotypic twins” approach. This dataset encompassed 87 Russian and European cultivars and 93 breeding lines from Western Siberia. We identified 11 novel loci significantly associated with oil and protein content in seeds (p-value < 1.5 × 10⁻⁶), including one locus on chromosome 11 linked to protein content and 10 loci associated with oil content (chromosomes 1, 5, 11, 16, 17, and 18). The protein-associated locus is located near a gene encoding a CBL-interacting protein kinase, which is involved in key biological processes, including stress response mechanisms such as drought and osmotic stress. The oil-associated loci were linked to genes with diverse functions, including lipid transport, nutrient reservoir activity, and stress responses, such as Sec14p-like phosphatidylinositol transfer proteins and Germin-like proteins. These findings suggest that the loci identified not only influence oil and protein content but may also contribute to plant resilience under environmental stress conditions. The data obtained from this study provide valuable genetic markers that can be used in breeding programs to optimize oil and protein content, particularly in varieties adapted to Russian climates, and contribute to the development of high-yielding, nutritionally enhanced soybean cultivars. Full article

(This article belongs to the Special Issue Advancing Soybean Improvement: Multi-Omics Strategies, Cutting-Edge Techniques and Bioinformatics Innovations)

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