scholarly journals How did the soybean genome evolve?

2021 ◽  
Author(s):  
Sylvain Bischof
Keyword(s):  
Soybean Genome

Integration and Insertion Site of EPSPs Gene on the Soybean Genome in Genetically Modified Glyphosate-Resistant Soybean

2010 ◽  
Vol 36 (3) ◽  
pp. 365-375 ◽  
Author(s):  
Xiao-Bo WANG ◽  
Ling-Xue JIANG ◽  
Li WEI ◽  
Lin LIU ◽  
Wei LU ◽  
...  
Keyword(s):  
Genetically Modified ◽  
Insertion Site ◽  
Soybean Genome ◽  
I Gene

Analysis of Nuclear Gene Codon Bias on Soybean Genome and Transcriptome

2011 ◽  
Vol 37 (6) ◽  
pp. 965-974 ◽  
Author(s):  
Le ZHANG ◽  
Long-Guo JIN ◽  
Ling LUO ◽  
Yue-Ping WANG ◽  
Zhi-Min DONG ◽  
...  
Keyword(s):  
Codon Bias ◽  
Nuclear Gene ◽  
Soybean Genome

scholarly journals Genome Duplication in Soybean (Glycine subgenus soja)

Genetics ◽  
1996 ◽  
Vol 144 (1) ◽  
pp. 329-338 ◽  
Author(s):  
R C Shoemaker ◽  
K Polzin ◽  
J Labate ◽  
J Specht ◽  
E C Brummer ◽  
...  
Keyword(s):  
Seed Protein ◽  
Genome Duplication ◽  
Fragment Length Polymorphism ◽  
Gene Families ◽  
Soybean Genome ◽  
Seed Composition ◽  
Linkage Groups ◽  
Mapping Data ◽  
Or Gene ◽  
Average Size

Abstract Restriction fragment length polymorphism mapping data from nine populations (Glycine max × G. soja and G. max × G. max) of the Glycine subgenus soja genome led to the identification of many duplicated segments of the genome. Linkage groups contained up to 33 markers that were duplicated on other linkage groups. The size of homoeologous regions ranged from 1.5 to 106.4 cM, with an average size of 45.3 cM. We observed segments in the soybean genome that were present in as many as six copies with an average of 2.55 duplications per segment. The presence of nested duplications suggests that at least one of the original genomes may have undergone an additional round of tetraploidization. Tetraploidization, along with large internal duplications, accounts for the highly duplicated nature of the genome of the subgenus. Quantitative trait loci for seed protein and oil showed correspondence across homoeologous regions, suggesting that the genes or gene families contributing to seed composition have retained similar functions throughout the evolution of the chromosomes.

scholarly journals A Classification of Basic Helix-Loop-Helix Transcription Factors of Soybean

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Karen A. Hudson ◽  
Matthew E. Hudson
Keyword(s):  
Transcription Factors ◽  
Genome Sequence ◽  
Search Algorithm ◽  
Soybean Genome ◽  
Binding Potential ◽  
Future Research ◽  
Basic Helix Loop Helix ◽  
Helix Loop Helix ◽  
Bhlh Genes ◽  
Wide Range

The complete genome sequence of soybean allows an unprecedented opportunity for the discovery of the genes controlling important traits. In particular, the potential functions of regulatory genes are a priority for analysis. The basic helix-loop-helix (bHLH) family of transcription factors is known to be involved in controlling a wide range of systems critical for crop adaptation and quality, including photosynthesis, light signalling, pigment biosynthesis, and seed pod development. Using a hidden Markov model search algorithm, 319 genes with basic helix-loop-helix transcription factor domains were identified within the soybean genome sequence. These were classified with respect to their predicted DNA binding potential, intron/exon structure, and the phylogeny of the bHLH domain. Evidence is presented that the vast majority (281) of these 319 soybean bHLH genes are expressed at the mRNA level. Of these soybean bHLH genes, 67% were found to exist in two or more homeologous copies. This dataset provides a framework for future studies on bHLH gene function in soybean. The challenge for future research remains to define functions for the bHLH factors encoded in the soybean genome, which may allow greater flexibility for genetic selection of growth and environmental adaptation in this widely grown crop.

scholarly journals Mapping of transgenic alleles in soybean using a nanopore-based sequencing strategy

2019 ◽  
Vol 70 (15) ◽  
pp. 3825-3833 ◽  
Author(s):  
Shengjun Li ◽  
Shangang Jia ◽  
Lili Hou ◽  
Hanh Nguyen ◽  
Shirley Sato ◽  
...  
Keyword(s):  
High Throughput ◽  
Soybean Genome ◽  
Transgenic Technology ◽  
Transgenic Soybean ◽  
Pooled Dna ◽  
Sequencing Strategy ◽  
Ds Element ◽  
Soybean Plants ◽  
Cost Efficient ◽  
Transgene Insertion

Abstract Transgenic technology was developed to introduce transgenes into various organisms to validate gene function and add genetic variations >40 years ago. However, the identification of the transgene insertion position is still challenging in organisms with complex genomes. Here, we report a nanopore-based method to map the insertion position of a Ds transposable element originating in maize in the soybean genome. In this method, an oligo probe is used to capture the DNA fragments containing the Ds element from pooled DNA samples of transgenic soybean plants. The Ds element-enriched DNAs are then sequenced using the MinION-based platform of Nanopore. This method allowed us to rapidly map the Ds insertion positions in 51 transgenic soybean lines through a single sequencing run. This strategy is high throughput, convenient, reliable, and cost-efficient. The transgenic allele mapping protocol can be easily translated to other eukaryotes with complex genomes.

scholarly journals Registration of USDA-Max × Soja Core Set-1: Recovering 99% of Wild Soybean Genome from PI 366122 in 17 Agronomic Interspecific Germplasm Lines

2019 ◽  
Vol 13 (2) ◽  
pp. 217-236 ◽  
Author(s):  
David Eickholt ◽  
Thomas E. Carter ◽  
Earl Taliercio ◽  
David Dickey ◽  
Lisa O. Dean ◽  
...  
Keyword(s):  
Wild Soybean ◽  
Soybean Genome ◽  
Core Set

scholarly journals An Integrated Genetic Linkage Map of the Soybean Genome

Crop Science ◽  
1999 ◽  
Vol 39 (5) ◽  
pp. 1464-1490 ◽  
Author(s):  
P.B. Cregan ◽  
T. Jarvik ◽  
A. L. Bush ◽  
R. C. Shoemaker ◽  
K. G. Lark ◽  
...  
Keyword(s):  
Linkage Map ◽  
Genetic Linkage ◽  
Genetic Linkage Map ◽  
Soybean Genome

scholarly journals A new decade and new data at SoyBase, the USDA-ARS soybean genetics and genomics database

2020 ◽  
Vol 49 (D1) ◽  
pp. D1496-D1501
Author(s):  
Anne V Brown ◽  
Shawn I Conners ◽  
Wei Huang ◽  
Andrew P Wilkey ◽  
David Grant ◽  
...  
Keyword(s):  
Soybean Genome ◽  
Genetic Maps ◽  
Pedigree Information ◽  
Data Sets ◽  
Visualization System ◽  
Interactive Tools ◽  
Genome Viewer ◽  
Reference Quality ◽  
Soybean Genetics ◽  
Genome Assemblies

Abstract SoyBase, a USDA genetic and genomics database, holds professionally curated soybean genetic and genomic data, which is integrated and made accessible to researchers and breeders. The site holds several reference genome assemblies, as well as genetic maps, thousands of mapped traits, expression and epigenetic data, pedigree information, and extensive variant and genotyping data sets. SoyBase displays include genetic, genomic, and epigenetic maps of the soybean genome. Gene expression data is presented in the genome viewer as heat maps and pictorial and tabular displays in gene report pages. Millions of sequence variants have been added, representing variations across various collections of cultivars. This variant data is explorable using new interactive tools to visualize the distribution of those variants across the genome, between selected accessions. SoyBase holds several reference-quality soybean genome assemblies, accessible via various query tools and browsers, including a new visualization system for exploring the soybean pan-genome. SoyBase also serves as a nexus of announcements pertinent to the greater soybean research community. The database also includes a soybean-specific anatomic and biochemical trait ontology. The database can be accessed at https://soybase.org.

scholarly journals Structural Variants in the Soybean Genome Localize to Clusters of Biotic Stress-Response Genes

2012 ◽  
Vol 159 (4) ◽  
pp. 1295-1308 ◽  
Author(s):  
Leah K. McHale ◽  
William J. Haun ◽  
Wayne W. Xu ◽  
Pudota B. Bhaskar ◽  
Justin E. Anderson ◽  
...  
Keyword(s):  
Stress Response ◽  
Biotic Stress ◽  
Soybean Genome ◽  
Structural Variants ◽  
Stress Response Genes ◽  
Biotic Stress Response
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