LjaFGD: Lonicera japonica functional genomics database

Abstract

Lonicera japonica Thunb., a traditional Chinese herb, has been used for treating human diseases for thousands of years. Recently, the genome of L. japonica has been decoded, providing valuable information for research into gene function. However, no comprehensive database for gene functional analysis and mining is available for L. japonica. We therefore constructed LjaFGD (www.gzybioinformatics.cn/LjaFGD and bioinformatics.cau.edu.cn/LjaFGD), a database for analyzing and comparing gene function in L. japonica. We constructed a gene coexpression network based on 77 RNA-seq samples, and then annotated genes of L. japonica by alignment against protein sequences from public databases. We also introduced several tools for gene functional analysis, including Blast, motif analysis, gene set enrichment analysis, heatmap analysis, and JBrowse. Our coexpression network revealed that MYB and WRKY transcription factor family genes were coexpressed with genes encoding key enzymes in the biosynthesis of chlorogenic acid and luteolin in L. japonica. We used flavonol synthase 1 (LjFLS1) as an example to show the reliability and applicability of our database. LjaFGD and its various associated tools will provide researchers with an accessible platform for retrieving functional information on L. japonica genes to further biological discovery.

Integrated volatile metabolomic and transcriptomic analysis provides insights into the regulation of floral scents between two contrasting varieties of Lonicera japonica

“…Knowledge of the mechanism underlying the accumulation of VOCs associated with floral scents remains limited. The decoded genome of L. japonica has provided valuable information for research into gene function and transcriptomic analysis ( Pu et al, 2020 ; Xiao et al, 2021 ).…”

GURFAP: A Platform for Gene Function Analysis in Glycyrrhiza Uralensis

“…According to the PCC distribution result, we found that the correlation between most genes was not high, mainly concentrated in the middle part ( Figure 1C ), and with the increase or decrease of correlation, the number of gene pairs gradually decreased ( Figure 1C ). Biological networks are usually scale-free with low network density ( Khanin and Wit, 2006 ; Broido and Clauset, 2019 ; Xiao et al, 2021 ). For the positive co-expression network, we evaluated and set the threshold of PCC in 0.6–0.95 to calculate the scale-free model fit index (R 2 ) and network density respectively.…”

“…Genes in a co-expression network have similar expression patterns, so we reasoned that they may be regulated by the same transcription factors. Our previous co-expression network and motif analysis discovered the biosynthesis pathways of chlorogenic acid and luteolin in L. japonica might be regulated by MYB, bHLH, WRKY ( Xiao et al, 2021 ). Here, gene co-expression network and motif analysis show transcription factors might participate in the regulation of glycyrrhizin biosynthesis.…”

“…Cis-acting elements (Motif) enrichment analysis tool we used in the platform was as our previous research ( Yang et al, 2020 ; Xiao et al, 2021 ). Motifs significantly enriched ( p -value<0.05) were defined by calculating p -value and z-score as below formula: …”

“…At the same time, a variety of gene function analysis tools were introduced to GelFAP, which facilitated the research on gene function of G. elata. Xiao et al ( Xiao et al, 2021 ) established the functional genome database (LjaFGD) of Lonicera japonica . In this platform, the researchers collected 77 transcriptome data to construct the co-expression network of L. japonica , and then they found that MYB and WRKY transcription factor family co-expressed with key enzyme genes in the biosynthesis of chlorogenic acid and luteolin.…”