SoyBase, the USDA-ARS soybean genetics and genomics database

USDA-ARS-CICGRU, Department of Agronomy, Iowa State University, Ames, IA 50011, USA.
Nucleic Acids Research (Impact Factor: 9.11). 12/2009; 38(Database issue):D843-6. DOI: 10.1093/nar/gkp798
Source: PubMed


SoyBase, the USDA-ARS soybean genetic database, is a comprehensive repository for professionally curated genetics, genomics
and related data resources for soybean. SoyBase contains the most current genetic, physical and genomic sequence maps integrated
with qualitative and quantitative traits. The quantitative trait loci (QTL) represent more than 18 years of QTL mapping of
more than 90 unique traits. SoyBase also contains the well-annotated ‘Williams 82’ genomic sequence and associated data mining
tools. The genetic and sequence views of the soybean chromosomes and the extensive data on traits and phenotypes are extensively
interlinked. This allows entry to the database using almost any kind of available information, such as genetic map symbols,
soybean gene names or phenotypic traits. SoyBase is the repository for controlled vocabularies for soybean growth, development
and trait terms, which are also linked to the more general plant ontologies. SoyBase can be accessed at

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    • "Transcripts for 152 annotated WRKY genes were detected on SoyBase EST database ( and/or on five global expression experiments: SuperSAGE of soybean leaves 12, 24 and 48 hours after inoculation (hai) of P. pachyrhizi [46], RNA-Seq of microdissected lesions 10 days after inoculation of P. pachyrhizi, two different microarrays of leaves 12 and 120 hai of P. pachyrhizi (available in the current literature) and RNA-Seq expression data of healthy plants in different developmental stages [47], available at SoyBase [48]. The GmWRKY genes were distributed over the 20 soybean chromosomes with protein sequences ranging from 121 to 1,356 amino acids in length (Table 1 and Additional file 1). "
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    ABSTRACT: Background Many previous studies have shown that soybean WRKY transcription factors are involved in the plant response to biotic and abiotic stresses. Phakopsora pachyrhizi is the causal agent of Asian Soybean Rust, one of the most important soybean diseases. There are evidences that WRKYs are involved in the resistance of some soybean genotypes against that fungus. The number of WRKY genes already annotated in soybean genome was underrepresented. In the present study, a genome-wide annotation of the soybean WRKY family was carried out and members involved in the response to P. pachyrhizi were identified.ResultsAs a result of a soybean genomic databases search, 182 WRKY-encoding genes were annotated and 33 putative pseudogenes identified. Genes involved in the response to P. pachyrhizi infection were identified using superSAGE, RNA-Seq of microdissected lesions and microarray experiments. Seventy-five genes were differentially expressed during fungal infection. The expression of eight WRKY genes was validated by RT-qPCR. The expression of these genes in a resistant genotype was earlier and/or stronger compared with a susceptible genotype in response to P. pachyrhizi infection. Soybean somatic embryos were transformed in order to overexpress or silence WRKY genes. Embryos overexpressing a WRKY gene were obtained, but they were unable to convert into plants. When infected with P. pachyrhizi, the leaves of the silenced transgenic line showed a higher number of lesions than the wild-type plants. Conclusions The present study reports a genome-wide annotation of soybean WRKY family. The participation of some members in response to P. pachyrhizi infection was demonstrated. The results contribute to the elucidation of gene function and suggest the manipulation of WRKYs as a strategy to increase fungal resistance in soybean plants.
    BMC Plant Biology 09/2014; 14(1):236. DOI:10.1186/s12870-014-0236-0 · 3.81 Impact Factor
    • "Different type of molecular markers has been used to map genomic location of major genes and quantitative trait loci (QTLs) for many traits of agronomic and economic importance in soybean. More than thousand QTLs representing more than 90 agronomically important traits have been mapped in soybean (Grant et al. 2010 ). Current information on all mapped QTLs in soybean is available on the USDA-ARS soybean genetic database SoyBase ( "
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    ABSTRACT: Soybean is an agronomically important crop that is endowed with rich seed protein and oil. It enriches the soil by fixing nitrogen through symbiosis with bacteria. In addition to human consumption, soybean is a major protein source in animal feeds and is also becoming a major crop for biodiesel production. A major landmark in soybean genomics research was its draft genome sequence assembly (cultivar Williams 82) following whole-genome shot gun (WGS) approach. It revealed 950 Mb (megabases) of assembled and anchored sequence as against the predicted 1,115 Mb genome consequently representing 85 % of the whole genome. Development of comprehensive physical map employing chiefly Bacterial artificial chromosomes (BAC) and Binary large-insert BAC clones (BIBAC) have assisted in the whole genome sequencing venture and in targeted genetic marker development, accelerating positional cloning approaches along with the generation of rapid and robust EST maps. Comprehensive Expressed Sequence Tags (ESTs) repository and genome sequence of the crop have helped in sound integration of physical map with the genetic map. In order to perform genetic and genomic analysis various molecular markers like RFLP, RAPD, AFLP, SSR, SNP etc. have been employed on RIL or F2 populations. In addition the genome is typified with single nucleotide polymorphisms (SNPs) and its utilization in molecular breeding applications like QTL mapping, positional cloning and association mapping studies is gaining impetus. QTLs associated with foremost traits of agronomic interests including QTLs for Aphid resistance, Soybean Cyst Nematode (SCN) resistance among others have been identified and validated. Further molecular marker assisted QTL introgression and gene pyramiding for traits like enhanced seed protein concentration and Soybean Mosaic Virus (SMV) resistance, insect resistance etc. have been accomplished. Legume comparative genomics using orthologous genomic regions have addressed queries relating to Nucleotide binding-Leucine rich repeat (NB-LRRs) genes, polyploidy, and genome evolution. In the soybean functional genomics arena, in addition to the conventional assays involving qRT-PCR, Northern blotting, global gene expression analysis like Serial analysis of gene expression (SAGE), microarrays kind strategies are being widely employed. With the identification of micro RNAs (miRNAs) as ultimate gene effector molecules identification and characterization of novel miRNAs in soybean is gaining a momentum. Thus the rapid development of soybean genomics and transcriptomics has provided tremendous opportunity for the genetic improvement of soybean. © Springer Science+Business Media New York 2014. All rights are reserved.
    Legumes in Omics Era, Edited by Gupta S., et al, 05/2014: chapter Advances in Soybean Genomics: pages 41-71; Springer.
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    • "Some platforms and web services have been built, (e.g. SoyBase [22], SoyGD [23] and SGMD [24]), and other databases are shown in Table 1. These databases contain diverse information, such as genomic data, expressed sequence tags [24] and microarray expression data. "
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    ABSTRACT: Soybean (Glycine max L.) is one of the world's most important leguminous crops producing high-quality protein and oil. Increasing the relative oil concentration in soybean seeds is many researchers' goal, but a complete analysis platform of functional annotation for the genes involved in the soybean acyl-lipid pathway is still lacking. Following the success of soybean whole-genome sequencing, functional annotation has become a major challenge for the scientific community. Whole-genome transcriptome analysis is a powerful way to predict genes with biological functions. It is essential to build a comprehensive analysis platform for integrating soybean whole-genome sequencing data, the available transcriptome data and protein information. This platform could also be used to identify acyl-lipid metabolism pathways.Description: In this study, we describe our construction of the Soybean Functional Genomics Database (SFGD) using Generic Genome Browser (Gbrowse) as the core platform. We integrated microarray expression profiling with 255 samples from 14 groups' experiments and mRNA-seq data with 30 samples from four groups' experiments, including spatial and temporal transcriptome data for different soybean development stages and environmental stresses. The SFGD includes a gene co-expression regulatory network containing 23,267 genes and 1873 miRNA-target pairs, and a group of acyl-lipid pathways containing 221 enzymes and more than 1550 genes. The SFGD also provides some key analysis tools, i.e. BLAST search, expression pattern search and cis-element significance analysis, as well as gene ontology information search and single nucleotide polymorphism display. The SFGD is a comprehensive database integrating genome and transcriptome data, and also for soybean acyl-lipid metabolism pathways. It provides useful toolboxes for biologists to improve the accuracy and robustness of soybean functional genomics analysis, further improving understanding of gene regulatory networks for effective crop improvement. The SFGD is publically accessible at, with all data available for downloading.
    BMC Genomics 04/2014; 15(1):271. DOI:10.1186/1471-2164-15-271 · 3.99 Impact Factor
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