Sample instruction: The waterlogging-tolerant cultivar Zhongzhi No. 13 (WT) and the waterlogging-susceptible strain ZZM0563 (WS) were used in this study. The two plants were potted under the same growth and experimental conditions and treated with waterlogging simultaneously for 15 h during the flowering stage. For the sesame of both genotypes, a total of 10 samples were collected at equivalent stages: 0, 3, 9, and 15 h under waterlogging stress as well as 20 h post-drainage [1].

In this project, gene expression levels were calculated based on the number of unique matched reads to the sesame genome and were normalized to reads per kilobase of transcript per million mapped reads (RPKM) using Cufflinks 2.0 software among 10 samples [2]. After curation, 22,509 genes, representing 82.912% of all sesame genes, were detected to express among 10 samples in sesame.

WGCNA is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings [3].

In this project, the members of module were confirmed with the parameter: kME > 0.7, FPKM > 1 and minimum module size: 30. After curation, 7,345 genes were used to perform co-expression analysis of sesame genes among 10 samples.

Note: Click the different color blocks on the left side of below pictures to get co-expression genes in this experiments.

1. Linhai Wang, Donghua Li, Yanxin Zhang, Yuan Gao, Jingyin Yu, Xin Wei, Xiurong Zhang. Tolerant and Susceptible Sesame Genotypes Reveal Waterlogging Stress Response Patterns. PLoS ONE 2016, 11(3): e0149912, doi:10.1371/journal.pone.0149912.

2. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010, 28(5):511–5. doi: 10.1038/nbt.1621.

3. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 2008, 9(1):559, doi: 10.1186/1471-2105-9-559.