Seqanswers Leaderboard Ad

Collapse

Announcement

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

  • Determine different methylation patterns after Bismark

    Hi Guys,

    I have a bismark sam-file including 66 different targets. The next step will be to detect different subpopulations for all targets in terms of the CpG island - considering only the reads with a coverage of e.g. 80%. I am not interested in the statistics of every single CpG position. Since I have not found any tool for that, I wonder how I can do this step efficiently. What would be your approach?

    Cheers!

  • #2
    It's really unclear what you're actually trying to do. You say you're not interested in individual CpGs, which implies that you'd like to look at regions. Are the 66 different target different samples or did you perform targeted bisulfite sequencing? In the case of the latter, how big are the regions and would you like to just summarise methylation over them? If not, you'll need to provide more details on what you'd actually like to do.

    Comment


    • #3
      Sorry for being unclear. Well I have 66 different regions of interest. And so far I found serveral tools that gave me proportions of every single CpG (methylated - unmethylated) or show me fully methylated regions like CellMethy. However, I wanna see, which subpopulations are found in each region. Lets say we have a region with 4 CpGs. What patterns can be found in that region? X-x-x-x? Or also x-X-x-x?

      Comment


      • #4
        You'd have to code something to do that then, it's not something most people are interested in looking at.

        Comment


        • #5
          That's what I've expected. I am writing a perl script and will put in on github.

          Comment

          Latest Articles

          Collapse

          • seqadmin
            Best Practices for Single-Cell Sequencing Analysis
            by seqadmin



            While isolating and preparing single cells for sequencing was historically the bottleneck, recent technological advancements have shifted the challenge to data analysis. This highlights the rapidly evolving nature of single-cell sequencing. The inherent complexity of single-cell analysis has intensified with the surge in data volume and the incorporation of diverse and more complex datasets. This article explores the challenges in analysis, examines common pitfalls, offers...
            Today, 07:15 AM
          • seqadmin
            Latest Developments in Precision Medicine
            by seqadmin



            Technological advances have led to drastic improvements in the field of precision medicine, enabling more personalized approaches to treatment. This article explores four leading groups that are overcoming many of the challenges of genomic profiling and precision medicine through their innovative platforms and technologies.

            Somatic Genomics
            “We have such a tremendous amount of genetic diversity that exists within each of us, and not just between us as individuals,”...
            05-24-2024, 01:16 PM

          ad_right_rmr

          Collapse

          News

          Collapse

          Topics Statistics Last Post
          Started by seqadmin, Today, 08:18 AM
          0 responses
          8 views
          0 likes
          Last Post seqadmin  
          Started by seqadmin, Today, 08:04 AM
          0 responses
          10 views
          0 likes
          Last Post seqadmin  
          Started by seqadmin, 06-03-2024, 06:55 AM
          0 responses
          13 views
          0 likes
          Last Post seqadmin  
          Started by seqadmin, 05-30-2024, 03:16 PM
          0 responses
          27 views
          0 likes
          Last Post seqadmin  
          Working...
          X