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

Abstract

BackgroundSoybean (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.DescriptionIn 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.ConclusionThe 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.

ccNET: Database of co-expression networks with functional modules for diploid and polyploidGossypium

“…with functional annotations have been integrated from several groups, including the PLACE database (24), AthaMap webserver (25), PlantCARE database (26) and text-mining results. The significance test is a statistical algorithm based on Z scores and P -value filtering (43,44) that can identify significance elements involved in a gene set. The cotton gene promoter region was taken to be 3000 bp and the Z-score can be calculated based on the frequency of motif occurrence.…”

Meta-analysis and transcriptome profiling reveal hub genes for soybean seed storage composition during seed development

“…The other coexpression network contained five hub genes based on WGCNA analysis (Figure b), including three homologues of classical transcription factors (TFs), ABI3b ( Glyma.08G357600 ) and two LEC1d homologues ( Glyma.15G118800 and Glyma.09G014100 ). Glyma.09G250400 was annotated as a putative adipose‐regulatory protein (Seipin), which was shown to participate in the WRI1 network and is involved in soybean lipid biosynthesis (Yu et al, ); it was also matched with MQTLOil‐1. Glyma.10G246300 was annotated as a cupin family protein, which may affect SSP as previously described (Mtwisha, Farrant, Brandt, Hlongwane, & Lindsey, ).…”

Selection for a Zinc-Finger Protein Contributes to Seed Oil Increase during Soybean Domestication

“…with plenty of lipid biosynthesis-related genes (Yu et al, 2014). WRI1 homologs in many plant species, such as ZmWRI1 and BnWRI1, are proved to participate in lipogenesis (Shen et al, 2010;Li et al, 2015).…”