Seqanswers Leaderboard Ad

Collapse
X
 
  • Filter
  • Time
  • Show
Clear All
new posts
  • travelk
    Member
    • Jul 2013
    • 20

    Test for read coverage uniformity of genes?

    Does anyone know of a program/package/method to test coverage uniformity of reads over individual genes?

    My differential expression analysis shows 1500 genes... but when I look closer at IGV, I see that some of the genes that come up are artefacts in that all the reads of a particular sample map only to the UTR or within an intron. (I'm analysing single cell data so it's a bit messy that way.) Is there a way to test whether the reads cover all (or most) of the exons of a gene, rather than one by one in IGV?

    Thanks for your help!
  • blancha
    Senior Member
    • May 2013
    • 367

    #2
    I see that some of the genes that come up are artefacts in that all the reads of a particular sample map only to the UTR or within an intron
    Reads aligning to the introns will not be counted, so they will not affect the differential expression analysis.

    Reads aligning to the UTR will be counted, obviously. However, you should be aware that some library preps will results in a very strong 3' bias, when using poly-A enrichment. We have uniform coverage at my institute even when doing poly-A enrichment. I do occasionally get data from other institutes, with a different library prep protocol, who do show this bias. If your genes show a systematic bias, you wouldn't want to exclude genes with a majority of reads aligning to the 3' UTR. If the majority of your genes have uniform coverage, you don't need to worry about this bias.

    Is there a way to test whether the reads cover all (or most) of the exons of a gene, rather than one by one in IGV?
    You could count the reads for individual exons with featureCounts. It will be difficult to have an algorithm to determine uniform coverage, though, in my opinion. Some exons are spliced out, and will therefore not have any reads align to them. There is also always some variation in the number of reads that align to exons.

    Obviously, you should eliminate genes with low count reads, which is generally the most effective manner of removing artifacts.

    If you still have artifacts after removing genes with low counts, I would rather investigate more the source of the artifacts than try and determine if the coverage is uniform.
    For example, do the peaks correspond to repeat regions, or miRNAs for example?
    Last edited by blancha; 10-20-2015, 12:47 PM.

    Comment

    • WhatsOEver
      Senior Member
      • Apr 2012
      • 215

      #3
      I use RSeqQC to check for potential amplification biases in single cell data. The geneBody_coverage script is the one you are probably looking for. The nice thing is that the script is linked to R scripts in order to produce some easy-to-interpret graphics. The following code will calculate the coverage over a set of housekepping genes supplied with RSeqQC (But you are of course free to supply your own bed file):

      Code:
      python /usr/local/RSeQC-2.6.1/scripts/geneBody_coverage.py --refgene /usr/local/RSeQC-2.6.1/resources/hg19.HouseKeepingGenes.bed --input=/txtFileWithBamFilesToAnalyse --out-prefix ./genebodyCov_hg19housekeeping

      Comment

      • travelk
        Member
        • Jul 2013
        • 20

        #4
        Ok, I've tested it, and RSeQC is exactly the type of thing I was looking for. I need to modify it slightly to suit my purposes but it lets me automate the verification of my genes of interest.

        Thanks for the suggestion!

        Comment

        • maxsalm
          Member
          • Feb 2015
          • 18

          #5
          Hi there! You may also find mRIN useful: http://www.ncbi.nlm.nih.gov/pubmed/26234653

          Comment

          Latest Articles

          Collapse

          • seqadmin
            Pathogen Surveillance with Advanced Genomic Tools
            by seqadmin




            The COVID-19 pandemic highlighted the need for proactive pathogen surveillance systems. As ongoing threats like avian influenza and newly emerging infections continue to pose risks, researchers are working to improve how quickly and accurately pathogens can be identified and tracked. In a recent SEQanswers webinar, two experts discussed how next-generation sequencing (NGS) and machine learning are shaping efforts to monitor viral variation and trace the origins of infectious...
            03-24-2025, 11:48 AM
          • seqadmin
            New Genomics Tools and Methods Shared at AGBT 2025
            by seqadmin


            This year’s Advances in Genome Biology and Technology (AGBT) General Meeting commemorated the 25th anniversary of the event at its original venue on Marco Island, Florida. While this year’s event didn’t include high-profile musical performances, the industry announcements and cutting-edge research still drew the attention of leading scientists.

            The Headliner
            The biggest announcement was Roche stepping back into the sequencing platform market. In the years since...
            03-03-2025, 01:39 PM

          ad_right_rmr

          Collapse

          News

          Collapse

          Topics Statistics Last Post
          Started by seqadmin, 03-20-2025, 05:03 AM
          0 responses
          49 views
          0 reactions
          Last Post seqadmin  
          Started by seqadmin, 03-19-2025, 07:27 AM
          0 responses
          57 views
          0 reactions
          Last Post seqadmin  
          Started by seqadmin, 03-18-2025, 12:50 PM
          0 responses
          50 views
          0 reactions
          Last Post seqadmin  
          Started by seqadmin, 03-03-2025, 01:15 PM
          0 responses
          201 views
          0 reactions
          Last Post seqadmin  
          Working...