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This article shows you that methylKit is for differential methylation analysis. The content is concise and easy to understand. It will definitely make your eyes shine. I hope you can gain something through the detailed introduction of this article.

methylKit is an R package for analyzing methylation sequencing data. It not only supports WGBS, RRBS and methylation sequencing of target regions, but also supports oxBS-sq, TAB-seq and other analysis of 5hmc data. Its core function is differential methylation analysis and annotation of differential methylation sites and regions.

The installation process is as follows:

source("http://bioconductor.org/biocLite.R")

biocLite("methylKit")

It is recommended to install with the latest version of R so that you can use the latest version of methylKist.

Difference analysis using methylKit consists of 3 steps

1. Read raw data

One raw data per sample, methylKit supports methylation calling files in two formats

plain text format

contents are as follows

Each row is a methylated site, coverage represents the number of reads covering the site, freqC represents the proportion of methylated C, and freqT represents the proportion of unmethylated C. This plain text format is intuitive, the file size is much smaller than bam files, and the reading speed is faster.

The process of reading plain text format is as follows

The treatment parameter specifies the grouping of samples, 0 for control group, 1 for treatment group

BAM files

Directly read the bam file generated by Bismark software comparison, and realize it through processBismarkAln

The usage is as follows:

2. Combine data from all samples

Methylation profiles of all samples were combined to obtain methylation profiles of all samples, using the following

meth=unite(myobj, destrand=FALSE)

The contents of meth are as follows, which is actually a merger of the previous methylation calling files.

In the process of merging, by default, only if all samples contain the site will be retained, essentially the intersection of all samples taken, if you want to take union, you can modify the value of the min.per.group parameter, the value of this parameter represents the minimum number of samples in each group covering the site will be retained, if set to 1, it is union.

meth.min=unite(myobj,min.per.group=1L)

3. perform gap analysis

Differential methylation analysis is performed by the calculateDiffMeth function, which is used as follows

myDiff=calculateDiffMeth(meth)

The results of differential methylation can be divided into two broad categories, depending on whether methylation C becomes more or less:

hypermethylated

hypomethylated

Hypermethylated means that there is more methylated C in the treatment group than in the control group; hypermethylated means that there is less methylated C in the treatment group than in the control group.

The getMethylDiff function is used to extract the results of the difference analysis. The usage is as follows

The difference function indicates a threshold for difference, which is retained only if the difference is greater than that threshold, starting with the value of meth.diff, noting that the absolute value is greater than the value of difference.

In addition to the difference threshold, there is a qvalue threshold below which results are retained. In methylKit, p-values are corrected using the SILM algorithm, unlike our conventional BH algorithm.

The type parameter defines the type of difference. If you only care about hypermethylated or hypomethylated, you can set the value of the type parameter to filter separately.

In methylKit, its differential analysis always focuses on the combined methylation profile. If your methylation profile has one methylation site per row, the differential analysis results in differential methylation sites; if your methylation profile has one methylation region per row, the differential analysis results in differential methylation regions. The examples above are for differentially methylated sites. Here is an analysis of differentially methylated regions.

The first problem encountered is how to define the methylation region. In methylKit, the methylation region is defined according to the sliding window method. The default window size is 10000 bp, and the step size is 10000bp. It is realized by tileMethylCounts function.

The codes for the complete differential methylation region analysis are as follows:

That's methylKit for differential methylation analysis. Have you learned anything? If you want to learn more skills or enrich your knowledge reserves, please pay attention to the industry information channel.

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