Chapter 3 92 3.5.7 Gene ontology enrichment analysis Gene Ontology enrichment analyses were done with the GOseq R package (Young et al., 2010) with Kallisto output (TPM). Gene Ontology annotation (ITAG4.1) was obtained from Sol Genomics Network (solgenomics.net). A term was considered significantly enriched if it has a p value < 0.05 and a fold enrichment > 1. Fold enrichment was calculated as (genes annotated with a term in the query dataset / total genes in the dataset) / (genes annotated with a term in the background set / total expressed genes). The results were visualized using R’s “pheatmap” function. 3.5.8 Co-expression network analysis Co-expression network modules were generated using the WGCNA R package (version 1.68) (Horvath and Langfelder, 2009). To prepare the data, individual libraries for each treatment and tissue were first quantile normalized using RPKM and then logtransformed with log2(x+1) prior to analysis. 75% of the most variable genes were used for analysis The selection of a threshold value was guided by visualizing the scale-free topology model fit (R²) across a range of soft thresholds, ultimately opting for a threshold of 7. Subsequently, an unsigned network was constructed using the “blockwiseModules” function, employing the bicor correlation measure and specifying specific parameters, including a maximum portion of outliers at 0.05, a mergeCutHeight of 0.35, and a minimum module size of 20To have a visual representation of gene expression in each module, we drew heat maps for each module with Graphpad Prism 9 (Figure S3.3). GO enrichment analysis was performed via GOseq R package and adjusted P value cut-off of 0.05 and q value cut-off of 0.05. The network was visualized using Cytoscape (version 3.9.1). To identify key genes within the network, we employed the CytoHubba plugin (Chin et al., 2014). We extracted the top 20 genes, and those genes that appeared in the top ranks across all four CytoHubba ranking methods, namely maximal clique centrality (MCC), edge percolated component (EPC), maximum neighborhood component (MNC), and node connect degree, were designated as hub genes. 3.5.9 Motif enrichment and TF identification To predict motifs, we followed this procedure: Firstly, we acquired 500 bp promoters of the FR-responsive DEGs from the Phytozome database (Goodstein et al., 2012). Subsequently, we employed MEME Suite (McLeay & Bailey, 2010) with default settings to conduct motif prediction. Later, we conduct our result with Tomtom for comparison of one or more motifs with a database of established motifs (O’Malley et al., 2016).
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