SFGD: a comprehensive platform for mining functional information from soybean transcriptome data and its use in identifying acyl-lipid metabolism pathways.
BACKGROUND
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.


METHODS
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.


CONCLUSIONS
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 http://bioinformatics.cau.edu.cn/SFGD/, with all data available for downloading.
Juan
J
Yu
Zhenhai
Z
Zhang
Jiangang
J
Wei
Yi
Y
Ling
State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China. x_wenying@yahoo.com.
Wenying
W
Xu
Zhen
Z
Su
D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D057225:Data Mining / Q000379:methods; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D018506:Gene Expression Regulation, Plant; D053263:Gene Regulatory Networks; D023281:Genomics / Q000379:methods; D059014:High-Throughput Nucleotide Sequencing; D020407:Internet; D050356:Lipid Metabolism / Q000235:genetics; D053858:Metabolic Networks and Pathways; D035683:MicroRNAs / Q000235:genetics; D059372:Nucleotide Motifs; D009928:Organ Specificity / Q000235:genetics; D011401:Promoter Regions, Genetic; D013025:Glycine max / Q000235:genetics* / Q000378:metabolism; D059467:Transcriptome
D035683:MicroRNAs