- software for correlating genome-wide DNA methylation and gene expression. - Background: DNA methylation is a major epigenetic modification involved in regulating gene expression. - The effects of DNA methylation on gene expression differ by genomic location and vary across kingdoms, species and environmental conditions. - To identify the functional regulatory roles of DNA methylation, the correlation between DNA methylation changes and alterations in gene expression is crucial. - With the advance of next-generation sequencing, genome-wide methylation and gene expression profiling have become feasible. - Current bioinformatics tools for investigating such correlation are designed to the assessment of DNA methylation at CG sites. - The correlation of non-CG methylation and gene expression is very limited. - Results: Here, we developed a bioinformatics web tool, MethGET (Methylation and Gene Expression Teller), that is specialized to analyse the association between genome-wide DNA methylation and gene expression. - For single-methylome analyses, MethGET provides Pearson correlations and ordinal associations to investigate the relationship between DNA methylation and gene expression.. - These functions enable the detailed investigation of the role of DNA methylation in gene regulation.. - Conclusions: MethGET is a Python software that correlates DNA methylation and gene expression. - DNA methylation is one of the best-studied epigen- etic mechanisms and refers to a process by which a me- thyl group is added to a cytosine [2]. - In plants, DNA methylation is found in three sequence contexts: CG, CHG, and CHH (H represents A, T or C), whereas in animals, it is mostly observed at CG sites [3]. - DNA methylation at different genomic locations may have different impacts on regu- lating the expression of genes and transposable elements (TEs) [7]. - Typically, DNA methylation in the promoter region may repress gene expression [8]. - Dynamic changes in DNA methylation in the genome- wide profile (i.e., methylome) often affect gene expres- sion with specific functional outcomes [14]. - In plants, DNA methylation can shape the transcriptome of the plant during seed germination and under biotic and abiotic stresses [15, 16]. - In mammals, alterations of DNA methylation have been shown to be associated with altered gene expression in the development of can- cer and cardiovascular diseases [17]. - The relationship between methylation changes and gene expression changes under different biological conditions and at dif- ferent timepoints is important, but the effects of DNA methylation on gene expression remain unclear and complicated [18]. - There are sev- eral bioinformatics tools for DNA methylation analyses, but only a few can correlate DNA methylation and gene expression for customized analyses, such as COHCAP [21], PiiL [22], and ViewBS [23]. - COHCAP and PiiL can integrate DNA methylation with gene expression, but. - MethHC [24] and iMETHYL [25] are databases of methylation and gene expression.. - Therefore, bioinformatics tools specialized for evaluating the correl- ation between DNA methylation and gene expression could help facilitate epigenomic research.. - In this research, we developed MethGET, web-based bioinformatics software for analyzing the correlation be- tween genome-wide DNA methylation and gene expres- sion. - MethGET allows users to upload their own DNA methylation and gene expression data for any species.. - MethGET includes single-methylome analyses for view- ing the correlation within a single sample and multiple- methylome analyses for detecting the correlations be- tween DNA methylation changes and gene expression changes between two groups of samples. - It also deter- mines DNA methylation in different contexts (CG, CHG, and CHH) and across different genomic regions (gene body, promoter, exon, and intron) to explore the different roles of methylation mechanisms in gene ex- pression. - Thus, MethGET serves as a useful tool for scien- tists to unveil the role of DNA methylation in regulating gene expression.. - these analyses include the following: 1) correlation ana- lyses of genome-wide DNA methylation and gene expres- sion (correlation). - 2) ordinal association analyses with genes ranked by gene expression level (ordinal associ- ation). - 3) distribution of DNA methylation by groups of genes with different expression levels (grouping statistics);. - these analyses in- clude the following: 1) gene-level associations between DNA methylation changes and gene expression changes. - The inputs of MethGET are DNA methylation (CGmap file as methylation calls), gene expression (tab-delimited text file), and gene annotation (GTF file) data. - The quality control of DNA methylation (WGBS) and gene expression data (RNA-seq) is usually performed before or during alignment. - CGmap files in- cluding the DNA methylation levels, read counts and methylation context of each cytosine are the output of the bisulfite specific aligners such as BS-Seeker and its vari- ants [29–31]. - Correlation analyses of genome-wide DNA methylation and gene expression (correlation). - To display the correlation between genome-wide DNA methylation and gene expression, MethGET generates scat- terplots and 2D kernel density plots. - 2b that genes with lower expression are enriched in both high and low DNA methylation levels.. - Ordinal association analyses with genes ranked by gene expression level (ordinal association). - suggesting a dif- ferential association or usage between DNA methylation and gene expression at different genomic regions.. - Distribution of DNA methylation by groups of genes with different expression levels (grouping statistics). - In addition, the correlation coefficient of DNA methylation and gene expres- sion in each group as well as descriptive statistics (such as. - 2 Correlation analyses of genome-wide DNA methylation and gene expression (human data). - To profile DNA methylation around genes across different expression groups, MethGET provides two kinds of meta- gene plots: “region” and “site” plots (Fig. - This can help to elucidate the mechanisms of DNA methylation at certain bases around a specific point.. - In this analysis, users can define the number of groups for separat- ing genes by gene expression levels, and they can also de- fine the number of windows in “region” and “site” plots for averaging DNA methylation levels.. - 3 Ordinal association analyses with genes ranked by gene expression level (human data). - Scatterplot and fitting curves of DNA methylation and relative gene expression. - 4 Distribution of DNA methylation by groups of genes with different expression levels (Arabidopsis data). - a The boxplot shows the gene body methylation pattern in 10 different gene expression groups. - Moreover, the correlation can be explored at the gene level to understand the DNA methylation regula- tory network associated with gene expression changes.. - Gene-level associations between DNA methylation changes and gene expression changes (comparison). - DNA methylation changes between two groups of sam- ples may exert a specific functional impact on gene ex- pression between them (e.g., mutants, treatments, stresses). - a The “ region ” plot shows the DNA methylation pattern around the gene body region. - a Gene-level associations between DNA methylation changes and gene expression changes. - The red dots represent differential genes of DNA methylation and gene expression (bi-variate Gaussian mixture model. - b Visualization of DNA methylation and gene expression data together. - methylation levels and gene expression within an indi- vidual group. - To identify the genes with clear changes of DNA methylation and gene expression (i.e., differential genes), we incorporated the Gaussian Mixture Model (Gaussian- Mixture module from the scikit-learn package in py- thon) in the bi-variate correlation plot [39]. - These genes with different DNA methylation statuses associated with gene expression changes are important because their expression may po- tentially be regulated by differences in DNA methylation between the two groups. - Visualization of DNA methylation and gene expression data together (heatmap). - Each row represents a gene, and the DNA methylation level and gene expression are averaged within each group in the columns. - We first investigated the relationship between DNA methylation and gene expres- sion in the rice methylome via single-methylome analyses.. - This result is in line with a recent study showing a positive correlation between CHH promoter methylation and gene expression in rice [42].. - In addition, we utilized MethGET to examine whether the gene expression changes observed during the tissue culture process were associated with DNA methylation.. - Figure 7b shows that most genes showing a significantly changes of the CHH gene body methylation and gene expression (bi-variate Gaussian mixture model. - Therefore, MethGET is not only a useful tool for investigating the effects of DNA methylation on gene expression but also able to reveal novel results.. - Only a few tools can integrate DNA methylation with gene expression. - It provides differential methylation analyses and the correlations between DNA methylation and gene expression [21]. - The PiiL tool is an integrated DNA methylation and gene expression pathway browser.. - It allows the visualization of CpG methylation and gene expression related to pathways in a single sample or groups of samples [22]. - For correlating between DNA methylation and gene expression, ViewBS provides metagene plots in the MethOverRegion function. - Several methylation databases can be used to visualize DNA methylation and gene expression on a web platform.. - MethHC is a database of DNA methylation and mRNA/. - It provides cell-type-specific browser tracks (e.g., CD4T, monocytes, and neutrophils) for examining DNA methylation variation, gene expres- sion and single-nucleotide variants in any region of the human genome [25]. - a The CHH methylation level trend increased with the increase in gene expression in the promoter region. - b Most differential genes showed decreases in both CHH methylation and gene expression in gene body regions. - epigenetic regulation of gene expression by DNA methylation.. - MethGET was developed for the correlation of genome- wide DNA methylation and gene expression data. - The correlation between genic CHH methylation and gene expression in all genes and non-TE-related genes.. - DNA methylation and its basic function.. - Establishing, maintaining and modifying DNA methylation patterns in plants and animals. - RNA-directed DNA methylation regulates parental genomic imprinting at several loci in Arabidopsis. - Putting DNA methylation in context: from genomes to gene expression in plants. - DNA methylation: a form of epigenetic control of gene expression. - On the presence and role of human gene-body DNA methylation. - Divergence of gene body DNA methylation and evolution of plant duplicate genes. - Correlation patterns between DNA methylation and gene expression in the Cancer genome atlas. - Evolutionary transition of promoter and gene body DNA methylation across invertebrate – vertebrate boundary. - Notes on the role of dynamic DNA methylation in mammalian development. - Plant genomic DNA methylation in response to stresses:. - DNA methylation in cancer: too much, but also too little.. - Dynamic DNA methylation: in the right place at the right time. - DNA methylation analysis: choosing the right method. - COHCAP: an integrative genomic pipeline for single- nucleotide resolution DNA methylation analysis. - PiiL: visualization of DNA methylation and gene expression data in gene pathways. - MethHC: a database of DNA methylation and gene expression in human cancer. - Human body epigenome maps reveal noncanonical DNA methylation variation. - Deubiquitinating enzyme OTU5 contributes to DNA methylation patterns and is critical for phosphate nutrition signals. - Role of DNA methylation dynamics in desiccation and salinity stress responses in rice cultivars
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