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  • Advice on PE ChIP Alignment & Pre-processing Steps??

    Hi All

    Being a newbie to bioinformatics, once again I am here with what some would find a really silly question but if I don't ask how am I going to learn. So here goes:

    We have done some ChIP-Seq experiments. Samples were run on HiSeqV4. We got PE reads. the sequencing facility aligns, process and give us data as cram files. I go from cram to bam using cramtools-3.0. And if I check stats of my bam using bamtools, all samples except input have very high level of duplication (over 80%). If I remove duplicates, I am left with very few reads and hence peak-callers (macs2 & homer) don't return enough peaks.

    If I decompress cram to fastq (using cramtools) and then align it myself using bwa mem, I get 0% duplicates. Note that I am aligning to the same reference genome (obtained via cramtools getref ) as the sequencing facility. Using samtools view -H path/to/my/cram/file.cram | grep PG, I figured that the pipeline used to align and process the reads at the sequencing facility uses the same aligner (bwa) but processes the data further with other tools e.g. bamcollate2, bam12auxmerge, bamsormadup, AlignmentFilter, bamstreamingmarkduplicates, etc.

    From different ChIP-Seq papers, I have never found the data being processed and filtered so much. My feeling is that these filters and processing might not be required for ChIP-Seq data.

    My question is: am I alright to decompress the crams and align myself or is my data not that great after all?

    Any tips, tricks, comments, remarks will be highly appreciated!!!

    Thanks very much

    fh

  • #2
    For starters, why don't you identify a few reads that are duplicated in the facility-generated BAM, then compare to the same reads in your self-generated BAM? If you have trouble interpreting the results, post the reads here so we can help.

    Comment


    • #3
      Originally posted by HESmith View Post
      For starters, why don't you identify a few reads that are duplicated in the facility-generated BAM, then compare to the same reads in your self-generated BAM? If you have trouble interpreting the results, post the reads here so we can help.
      Hi HESmith,
      Thanks for the reply. Do you mean I should just extract some duplicated reads from the facility-generated bam and see if they're present in my self-generated bam? The number of reads is very similar between the bam.

      My impression is that these extra tools used in the facility somehow flags reads as duplicates but when I decompress and realign it, I get rid of the flags somehow and hence there are no duplicates.

      Comment


      • #4
        Question: how did you determine that your self-aligned reads did not contain any duplicates?

        Comment


        • #5
          I'd find it unlikely that a ChIP library had 0% duplication. They are in general highly duplicated as you are sequencing a very limited set of input template.

          Comment


          • #6
            @HESmith

            bamtools stats -in /path/to/my/bam/self-aligned.bam
            Last edited by fh331; 10-24-2016, 01:49 PM.

            Comment


            • #7
              Originally posted by fanli View Post
              I'd find it unlikely that a ChIP library had 0% duplication. They are in general highly duplicated as you are sequencing a very limited set of input template.
              Hi fanli

              I agree with you. Since I have used 'bamtools stats' function to get a quick idea. I am assuming bamtools isn't very stringent in marking duplicates. If I use piccard markduplicates, I think there will be some level of duplication. I can put updated info about that tomorrow.

              On the other hand, what level of duplication is normal?

              Comment


              • #8
                Originally posted by fh331 View Post
                Do you mean I should just extract some duplicated reads from the facility-generated bam and see if they're present in my self-generated bam?
                Duplicate reads are identified by alignment (chromosome/position) information. You want to determine if that information is the same for facility vs. self alignments. Find a few duplicates in the former, then examine the same reads in the latter. Either the alignment information will match (which means that bamtools is not counting the duplicates) or not (indicates a discrepancy b/t the aligners) or the duplicates are missing from the latter (indicates removal of duplicates).

                Comment


                • #9
                  Samtools tview

                  Always look at the reads, not just the stats. The number of unique fragments is what matters, not the duplication rate. 80% duplicates would be useless if you sequenced 2 M reads, but may be ok if you sequenced 200 M.

                  Comment


                  • #10
                    Originally posted by HESmith View Post
                    Duplicate reads are identified by alignment (chromosome/position) information. You want to determine if that information is the same for facility vs. self alignments. Find a few duplicates in the former, then examine the same reads in the latter. Either the alignment information will match (which means that bamtools is not counting the duplicates) or not (indicates a discrepancy b/t the aligners) or the duplicates are missing from the latter (indicates removal of duplicates).
                    I ran piccard MarkDuplicates on my self-aligned bams and if i check the stats on bamtools after marking duplicates, it returns the same level of duplication. So i think i was just not doing it the right way. After running bwa, I guess i need to markduplicates before checking stats. Something learnt by newbie!

                    Comment


                    • #11
                      Glad that you were able to sort out the problem.

                      Comment


                      • #12
                        Originally posted by Chipper View Post
                        Samtools tview

                        Always look at the reads, not just the stats. The number of unique fragments is what matters, not the duplication rate. 80% duplicates would be useless if you sequenced 2 M reads, but may be ok if you sequenced 200 M.
                        Hi Chipper,

                        Thanks for the reply. How does this tview work? I can't seem to find anything about it besides this: http://samtools.sourceforge.net/tview.shtml

                        which isn't very informative

                        Comment


                        • #13
                          'tview' is a terminal-based genome viewer. It would allow a quick spot-check of duplication (by visualizing the endpoints of the aligned reads), but it wouldn't calculate the fraction of your reads that are unique.

                          Comment


                          • #14
                            Originally posted by fh331 View Post
                            Hi Chipper,

                            Thanks for the reply. How does this tview work? I can't seem to find anything about it besides this: http://samtools.sourceforge.net/tview.shtml

                            which isn't very informative
                            found it in samtools manual!!! thanks

                            Comment


                            • #15
                              @HESmith

                              Thanks very much. I highly appreciate help from all the experienced users.

                              For future reference, what can I do better to avoid getting so much duplication levels in chipseq samples? Is it better to start with a lot of DNA, less number of pcr cycles during library prepartion? Any tips would make my life way easier!

                              Comment

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