How to compare methylation array data with methylation MeDip-seq data?

Sample of a Txt file from 450k array : * ID_REF VALUE Detection.Pval * cg00000029 0.533466 0 * cg00000108 0.9221188 0 The VALUE represents the amount of methylation(0 - unmethylated and 1 - very highly methylated) Sample of a Bed file from Medip seq data: * chr1 1 1000 0.000852090112432486 * chr1 501 1500 0.0005609473955776 * chr1 1001 2000 0 * chr1 1501 2500 0 * chr1 2001 3000 0

5.9 years ago by

Devon Ryan ♦ 98k

Freiburg, Germany

Devon Ryan ♦ 98k wrote:

N.B., the R packages I reference below are available on Bioconductor.

I happen to have been comparing some human 450k samples to mouse RRBS samples recently, so perhaps I can provide some guidance.

library("FDb.InfiniumMethylation.hg19")
anno <- get450k()

The anno GRanges object now contains a convenient annotation of each probe on the array and the CpG that it should be informative about. I can also recommend the minfi package, which facilitates a lot of the raw file processing.

Regarding incorporating the MeDIP-seq dataset, that's a little tougher. One possibility is to simply correlate the methylation in the two files. This can be conveniently done by creating a GRanges object from the MeDIP-seq file and then using findOverlaps() to get overlapping CpGs from the 450k array. You would then need to get average (or median) methylation of the probes for each of the overlapped regions and then you can plot/calculate the correlation. How well they're correlated remains to be seen. I've generally been unimpressed with MeDIP-seq (we do mostly bisulfite sequencing).

ADD COMMENT • link written 5.9 years ago by Devon Ryan ♦ 98k

I have a data_1 which is in text format with columns, chr (representing chromosome number), stable_id, start, end & methylation. This is in txt format, mm9 version.

I have a data_2 which is in bigwig format with columns, seqnames, ranges, strand, methylation score. This is in mm10 format. (over 10 million rows)

I am to compare the data_1$start, data_1$end with data_2$ranges and compute the average methylation score and number of CpG islands.

Steps I followed which I believe is a long route.

Step:1 - Converted data_1 to a file format like 'chrN:start-end' and exported the CSV .
Step:2 - Used this CSV file, uploaded to ucsc genome browser LiftOver tool, converted from mm9 to mm10 - Output was a bed file.
Step:3 - Replaced the start and end of data_1 file with new start and end coordinates of the liftovered output bed file.
Step: 4- Comparing the start and end of data_1 with data_2, This is where I am stuck, takes a lot of time using R to process. IS there a simpler way than what I followed? New to field. Please explain in steps.

ADD REPLY • link modified 4.9 years ago • written 4.9 years ago by startup_biostar • 10

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