Prioritization and Evaluation of Flooding Tolerance Genes in Soybean [Glycine max (L.) Merr.]

Abstract

Soybean [Glycine max (L.) Merr.] is one of the most important legume crops abundant in edible protein and oil in the world. In recent years there has been increasingly more drastic weather caused by climate change, with flooding, drought, and unevenly distributed rainfall gradually increasing in terms of the frequency and intensity worldwide. Severe flooding has caused extensive losses to soybean production and there is an urgent need to breed strong soybean seeds with high flooding tolerance. The present study demonstrates bioinformatics big data mining and integration, meta-analysis, gene mapping, gene prioritization, and systems biology for identifying prioritized genes of flooding tolerance in soybean. A total of 83 flooding tolerance genes (FTgenes), according to the appropriate cut-off point, were prioritized from 36,705 test genes collected from multidimensional genomic features linking to soybean flooding tolerance. Several validation results using independent samples from SoyNet, genome-wide association study, SoyBase, GO database, and transcriptome databases all exhibited excellent agreement, suggesting these 83 FTgenes were significantly superior to others. These results provide valuable information and contribution to research on the varieties selection of soybean.

Agricultural Big Data Architectures in the Context of Climate Change: A Systematic Literature Review

“…Each data source provides temperature, humidity, illumination, atmospheric pressure, soil electrical conductivity, soil and vegetation moisture content, and soil salinity [52]. On the other hand, genomic data related to flood tolerance were found [65].…”

“…Linear regression and multiple linear regression were used by Bendre et al [67], Sumalatha and Akila [64], and Reddy and Sureshbabu [39]. Modified random forest and SoyNet algorithms were used by Lai et al [65]. K-means were also used by Sumalatha and Akila [64].…”

An advanced systems biology framework of feature engineering for cold tolerance genes discovery from integrated omics and non-omics data in soybean

“…In the data mapping process, annotations of OnO entities (transcripts, proteins, SNPs, SSRs, and QTLs) with bioinformatics were matched to annotated genes. A window spanning 20 kb upstream/downstream of a gene was used in gene annotation mapping (Lai et al, 2021). In addition, annotated pathways (e.g., GeneOntology, GO-terms) were matched with corresponding gene sets.…”

“…With the development of integration methods, it is becoming available to incorporate complex biological information from several omics and nonomics (OnO) data. Recently, Lai et al (2021) proposed a framework of multi-dimensional databases integration, combining genomic and genotypic data, and a step functionbased weighting scheme to select flooding tolerance genes. Integrating knowledge derived from genetic information and multiple omics data, coupled with bioinformatics and bioanalytical approaches, can improve gene discovery to better understand complex networks of interactions between genes, proteins, metabolites and environmental factors within a complex phenotypic trait (e.g., cold tolerance and response to cold stress).…”

“…Several prioritization approaches were proposed for complex traits in human diseases. Recently, the idea of gene prioritization was applied to rice bacterial leaf blight (Xia et al, 2013), Arabidopsis thaliana flowering-time (Zhai et al, 2016), and soybean flooding tolerance (Lai et al, 2021). Nevertheless, there is still some space left for improvement.…”