Seqanswers Leaderboard Ad

Collapse

Announcement

Collapse
No announcement yet.
X
 
  • Filter
  • Time
  • Show
Clear All
new posts

  • differentially methylated sites with methylKit

    I want to generate an outfile containing or C site (CG, CHG or CHH) that are differentially methylated (DM) using the R package methylKit

    The problem is that I got a file that is empty.

    I understand that outfiles listing hypermethylated or Hypomethylated can be empty simply because there are not DM sites between the compared samples. However I'm sure this is not the case and please let me explain before go to my question.

    I have 4 samples ( control vs treatment with 2 replicates each). If I compare tiles of 100 bp with a step.size of 50 I get a bunch of DMRs. However if I try to found difference with single base resolution I got nothing. This do not have sense to me and I suspect I'm doing something wrong during the analysis. I willl appreciate if someone can read the commands i used and help me to identify my error.

    #### Methyl seq sample detail
    SAMPLE

    C4_ctrl BC1 REP1 control
    C7_ctrl BC5 REP2 control

    C4_PEG BC3 REP1 treatment
    C7_PEG BC7 REP2 treatment



    ### In this example I'm comparing control=ctrl samples against the
    ## treatment=PEG in any context (CG, CHG, CHH) from; chr1
    > library(methylKit);
    > library(graphics);
    > comparisonName="C_ctrl_vs_C_PEG_anyContext_site_by_site";ou
    > chrom="chr1";
    > C4_ctrl="BC1"; C7_ctrl="BC5";
    > C4_PEG="BC3"; C7_PEG="BC7";
    > ctrl1=paste(C4_ctrl, chrom, "MethylKit.inputdata.anyContext.txt", sep=".");
    > ctrl2=paste(C7_ctrl, chrom, "MethylKit.inputdata.anyContext.txt", sep=".");
    >PEG1=paste(C4_PEG, chrom, "MethylKit.inputdata.anyContext.txt", sep=".");
    >PEG2=paste(C7_PEG, chrom, "MethylKit.inputdata.anyContext.txt", sep=".");
    > file.list <- list(ctrl1,ctrl2,PEG1,PEG2);
    > myobj <- read(file.list, sample.id = list(ctrl1,ctrl2,PEG1,PEG2), assembly="TAIR10", treatment=c(1,1,0,0), context="anyContext");
    filtered.myobj=filterByCoverage(myobj,lo.count=10,lo.perc=NULL,hi.count=NULL,hi.perc=99.9)
    >meth <- unite(myobj, destrand = FALSE);
    >myDiff <- calculateDiffMeth(meth, num.cores=4);

    #filtering for significant differential methylation
    > myDiff.hyper <- get.methylDiff(myDiff, difference = 10, qvalue = 0.01, type = "hyper");
    > myDiff.hypo <- get.methylDiff(myDiff, difference = 10, qvalue = 0.01, type = "hypo");

    #output data for hypermethylated, hypomethylated, and all tiles

    > outfile1 = paste(comparisonName, chrom, "hyper", "txt", sep=".");
    write.table(as.data.frame(myDiff.hyper), file=outfile1, sep="\t", col.names=TRUE, row.names=FALSE);
    > outfile2 = paste(comparisonName, chrom, "hypo", "txt", sep=".");
    write.table(as.data.frame(myDiff.hypo), file=outfile2, sep="\t", col.names=TRUE, row.names=FALSE);
    > outfile3 = paste(comparisonName, chrom, "united", "txt", sep=".");
    write.table(as.data.frame(meth), file=outfile3, sep="\t", col.names=TRUE, row.names=FALSE);

    ###the result is that outfile 1 and outfile2 are empty.. What I did wrong?

    ###I will appreciate any help

Latest Articles

Collapse

  • seqadmin
    Essential Discoveries and Tools in Epitranscriptomics
    by seqadmin




    The field of epigenetics has traditionally concentrated more on DNA and how changes like methylation and phosphorylation of histones impact gene expression and regulation. However, our increased understanding of RNA modifications and their importance in cellular processes has led to a rise in epitranscriptomics research. “Epitranscriptomics brings together the concepts of epigenetics and gene expression,” explained Adrien Leger, PhD, Principal Research Scientist...
    04-22-2024, 07:01 AM
  • seqadmin
    Current Approaches to Protein Sequencing
    by seqadmin


    Proteins are often described as the workhorses of the cell, and identifying their sequences is key to understanding their role in biological processes and disease. Currently, the most common technique used to determine protein sequences is mass spectrometry. While still a valuable tool, mass spectrometry faces several limitations and requires a highly experienced scientist familiar with the equipment to operate it. Additionally, other proteomic methods, like affinity assays, are constrained...
    04-04-2024, 04:25 PM

ad_right_rmr

Collapse

News

Collapse

Topics Statistics Last Post
Started by seqadmin, Yesterday, 11:49 AM
0 responses
13 views
0 likes
Last Post seqadmin  
Started by seqadmin, 04-24-2024, 08:47 AM
0 responses
16 views
0 likes
Last Post seqadmin  
Started by seqadmin, 04-11-2024, 12:08 PM
0 responses
61 views
0 likes
Last Post seqadmin  
Started by seqadmin, 04-10-2024, 10:19 PM
0 responses
60 views
0 likes
Last Post seqadmin  
Working...
X