Let’s suppose that we have the following read-count data set obtained from Bismark ‘CX_report’ files:

folder <- "~/data/"
files <- list.files(path = folder)
# [1] "~/data/Col_8-1a.fq_bismark_bt2.deduplicated.bismark.CX_report"
# [2] "~/data/Col_8-1b.fq_bismark_bt2.deduplicated.bismark.CX_report"
# [3] "~/data/Col_8-1c.fq_bismark_bt2.deduplicated.bismark.CX_report"

This data set can be read with the following code:

library(MethylIT)
col0 <- readCounts2GRangesList(filenames = paste0(folder, files),
sample.id = c("col1", "col2", "col3"),
pattern = "^[1-5]",
columns = c(seqnames = 1, start = 2,
strand = 3, mC = 4, uC = 5,
context = 6))

Since the methylome data are from Arabidopsis thaliana (a plant), we have added the column 6 from each Bismark ‘CX_report’ file, which for each cytosine site carries the information of its corresponding methylation context: CG, CHG, or CHH. Users familiar with Linux OS can easily check which is the number of each required column by using Linux command line ‘head’ function.

head BS-10.CX_report.txt
1       1       +       0       0       CHH     CCC
1       2       +       0       0       CHH     CCT
1       3       +       0       0       CHH     CTA
1       8       +       0       0       CHH     CCC
1       9       +       0       0       CHH     CCT
1       10      +       0       0       CHH     CTA
1       15      +       0       0       CHH     CCC
1       16      +       0       0       CHH     CCT
1       17      +       0       0       CHH     CTA
1       22      +       0       0       CHH     CCC

Plants methylome contains chloroplast and mitochondrial chromosomes, which are not included in methylome analysis. In the current case, we can exclude these chromosomes by setting:

pattern = "^[1-5]"

That is, only rows starting with any of the integer numbers from 1 to 5 are read. Notice that readCounts2GRangesList function is able to deal with different file formats. The user just need to provide the number of the specified columns, which for different aligner programs can be different.

It is very important to notice that Methyl-IT pipeline assumes that all the data provided have acceptable quality. It is up to the user to guaranty the quality of its methylome data set. For example, to include chloroplast chromosome, will artificially decrease the levels of noise in the treatment and control group of samples, which definitely will lead to a wrong estimation of the cutpoints in the signal detection step.

### Analysis per methylation context

Usually, in plant epigenomic field, the methylation analysis is performed by methylation context. After apply the script above, the three methylation contexts were included in each sample. The readCounts2GRangesList function can be used to read only one specified methylation context, but it also increase the number of files per sample.

The next code will select the methylation context that want, say “CHG”, from the above samples:

col0_chg <- lapply(col0, function(s) s[s$context == "CHG"], keep.attr = TRUE) Next, to compute the reference sample, keep in mind that we have two columns carrying integer numbers and third column carrying a character vector, which will be not included in the computation. To prevent error we use ‘columns = 1:2’ ref_chg <- poolFromGRlist(LR = col0_chg, stat = "sum", columns = 1:2, num.cores = 10L) ## DMPs Let’s suppose that we have set of DMPs estimated for each methylation context: dmps_cg, dmp_chg, and dmps_chh. For DMG or DMR analyses we usually include all the methylation contexts. nams <- names(dmps_cg) dmps <- dmps_cg for (k in 1:length(nams)) { dmps[[k]] <- c(dmps_cg[[k]], dmps_chg[[k]], dmps_chh[[k]]) } names(dmps) <- nams ## Annotation file Gene annotation is needed for DMG estimation. Some organisms (not well annotated yet) reports several “contig” sequences which are not included between the known chromosomes. In the most frequent case, as in the case of Arabidopsis, we do not include chloroplast and mitochondrial chromosomes. For Arabidopsis, following code does job when the “gtf” is used: library(rtracklayer) AG = import("~/data/TAIR10_gff3/Arabidopsis_thaliana.TAIR10.46.gtf.gz") gene = AG[ AG$type == "gene", c( "gene_id", "gene_biotype", "gene_name" ) ]
gene = gene[ gene\$gene_biotype == "protein_coding", c("gene_id", "gene_name") ]
seqlevels(gene, pruning.mode = "coarse") <- c("1", "2", "3", "4", "5")
gene = sortBySeqnameAndStart(gene)

In fact, the critical step in the above code is:

seqlevels(gene, pruning.mode = "coarse") <- c("1", "2", "3", "4", "5")

## General suggestions

1. Theoretically, the nonlinear fit to estimate the probability distribution of the information divergence and the cutpoint estimation must be done per chromosome. However, for organisms with small methylome, as in the case of Arbidopsis, we have not detected significant differences, so far, when the methylome reprogramming is genome-wide. Nevertheless, if you suspect that the methylation reprogramming is affecting specific chromosomes, then we strongly suggest to perform the analysis per chromosome. In the case of human methylome, the analysis per chromosome is the right way to proceed.

2. The cutpoint estimation step is critical. The evaluation of the classification performance is a very important step. Typically, a classification performance with accuracy below 95% would indicate that either something is wrong in some previous step (not necessarily in Methyl-IT steps) or that really there are not too much difference between your control and treatment groups, as would be the case, for example, in the analysis of different seed development stages. Please read and be familiar with the vignette:
Optimal cutpoint for the methylation signal.