2.5 Genome wide methylome analysis
After visual inspection of the quality of DNA methylation profiles using the Integrative Genomics Viewer (Thorvaldsdóttir et al., 2013), we performed the analysis of the methylome at the genome’s scale. In order to evaluate the global methylation level, metagene analysis was performed using deepTools (version 2.0, Ramirez et al., 2016) command “computeMatrix” to generate read abundance from all samples over genomic regions: promoter, 5’UTR exons, coding exon, first intron, internal introns (located in non-flanking regions of genes), last intron, 3’UTR exon and Transcription End Site (TES). This matrix was then used to create, using deepTools command “plotProfile”, a metagene profile from 2kb upstream of the Transcription Start Site (TSS) to 2 kb downstream of the Transcription End Site (TES). The same method was used to generate a profile plot of the level of methylation across all genomic regions.
The methylation profiles of the samples were studied using the R package MethylKit (Akalin et al. 2012). The alignment BED files were first converted into a tabular file suitable for the MethylKit package using the methylextract2methylkit tool (version 0.1.0). In order to increase the power of the statistical tests, the samples were filtered according to read coverage. Bases that had less than 10X coverage and those that had greater than 99.9th percentile coverage in each sample were filtered out from the analysis to account for potential PCR bias.
Hierarchical clustering analysis and principal component analysis were performed using the ”ClusterSamples” and ”PCASamples” functions, respectively, of the Methylkit R package. These analyses were based on similarities in the methylation patterns of the samples from each salinity condition. A distance correlation matrix was generated with the Pearson method and the clustering was performed using the Ward method.