Chromosomal positioning and epigenetic architecture influence DNA methylation patterns triggered by galactic cosmic radiation.
- DNA.methylation.R
- Genes.for.hic.astronauts.R
- Microarray.R
- RNAseq.R
R Scripts contain all necessary code to perform the analysis and generate all figures included in the article entilted: "Chromosomal positioning and epigenetic architecture influence DNA methylation patterns triggered by galactic cosmic radiation" published in XXX.
- GSE108187_processed_matrix.csv (you can download it here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE108187)
- GSE108187_colnames.xlsx
- DMPs.annotations.all.probes.2000.rdata (you can generate it via in second step of DNA.methylation.R script - it takes several hours and 200MB of disk space)
- DMRs.newest.GRanges.rdata
- DMRs.newest.annotations.rdata
- Bed.newest.GRanges.rdata
The rest of files needed for certain step are avaiable on publicly available respositories and are decribied in detail in the article.
- Install and load packages
- Import and prepare primary files
- Differential methylation analysis
- Pick significant DMPs
- DMRs Building
- Simulate DMRs building with rows permutation (Optional step - can take several hours to run based on number of CPUs and repetitions)
- Load and prepare lists of DMRs
- Acute methylation change versus control between DMPs (Figure_1D; Figure_S1B; Figure_S1C; Figure_1D)
- % of DMPs in DMRs (Figure_S1H)
- DMPs which changed at least of 0.58 or -0.58 M Value versus control (Figure_1E; Figure_1D; Figure_S1D)
- Detailed methylation patterns (Figure_S1E; Figure_S1F)
- Select DMRs, DMPs in or out of DMRs and all DMPs for further analysis
- Annotate data.raw and prepare for further normalization
- Annotate Genic Locations (Figure_1G)
- Annotate to CpG Islands (Figure_1H; Figure_1I; Figure_S1J; Figure_S1K; Figure_S1L)
- Chronic methylation change (Figure_4A; Figure_4B; Figure_S4A)
- Chromosomes with the highest DMPs frequency (Figure_S2B; Figure_S2C; Figure_S2D; Figure_S2E; Figure_S2F; Figure_S2G)
- Assign Hi-C compartments to the data
- Data preparation for Hi-C analysis
- Order and plot data.raw chromosome position (Figure_2B; Figure_S2A)
- Chromosomal frequencies in each nucleus layer (Figure_2D; Figure_S2I; Figure_S2J; Figure_S2K; Figure_S2L; Figure_S2M)
- DMPs frequency and DNA methylation change within nucleus layers (Figure_2C; Figure_2E; Figure_S2H; Figure_S2N)
- Load and prepare histone modifications datasets for methylation analysis
- Assign histone modifications to Hi-C layers (Figure_3B; Figure_3C; Figure_3D; Figure_3E; Figure_3F; Figure_S3B; Figure_S3C; Figure_S3D)
- Plot data.raw probes into histone modifications picks and Hi-C layers (Figure_3A; Figure_S4A)
- Data preparation for regulatory regions (promoter and enhancers) analysis
- Add Hi-C to regulatory regions (Figure_S3F)
- DNA Methylation change in regulatory regions (Figure_3G; Figure_S3I; Figure_S3J; Figure_S3K; Figure_S3L; Figure_S3G)
- Baseline methylation level in regulatory regions
- Add Hi-C to regulatory regions (without DMPs overlapping) (Figure_S3E)
We suggest running it chronologically as some steps may have consequences in further ones.
This step creates space.genes.rdata file necessary for RNAseq.R script
- Load packages
- Microarray for Fe - Heart (Figure_4F; Figure_4G)
- Microarray for Si - Breast (Figure_S3A; Figure_S3B)
- Microarray for Fe - Heart _ Protons (Figure_S3C)
- Microarray for Si - Breast _ Gamma Rays (Figure_S5E; Figure_S5F)
- Microarray for Cs137 - Blood (Figure_S5G; Figure_S5H)
- Load packages
- RNA-seq - Fe liver (Figure_4D; Figure_4E)
- RNA-seq - Co57 retina (Figure_S5I; Figure_S5J)
- RNA-seq - Astronauts - blood (Figure_4G; Figure_4H)
- Astronauts - Hi-C (Figure_4F; Figure_S4M)