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  • IGV visualization pattern for RNA-Seq mapping

    Hello everyone,

    When I look at RNA-Seq mapping in IGV, why reads are aligned to the opposite strand?

    In the example below, the gene is annotated in the reverse strand (which I understand as a synonym of negative/minus strand), but the reads are aligned to the positive strand (foward/plus strand). It's because is cDNA? (I used Group alignments by first-in-pair strand and Color alignments by first-of-pair strand options in IGV)

    Click image for larger version

Name:	igv_rnaseq_mapping_example.png
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    Sorry for the silly question, but I could not figured out it by myself...

    Many thanks.

  • #2
    Which library prep did you use to get this data? Many of the preps now are strand specific. Did you generate this data yourself or get it from somewhere else?

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    • #3
      It's polyA capture, paired-end (2x100bp) and strand-specific (dUTP) RNA-Seq. But the sequencing was performed by the BGI company, which sequencing protocol (DNBseq platform) differs a little bit from Illumina, and I'm not completely sure how this affects the analysis.

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      • #4
        I have done this with my data and your settings, and see that you can see the data in two blocks. By moving the mouse wheel down, I can see the other block (it is separated as NEGATIVE and POSITIVE.). I used dUTP and Illumina, though.
        I your reads setting are set to Extended or squished mode (not collapsed), it is likely occupying a lot of your screen
        Am I right?
        Last edited by AntonioRFranco; 11-10-2022, 03:38 AM.

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        • #5
          Hello AntonioRFranco,

          I'm using the Squished mode and the majority of reads are aligned to positive strand (you can see the only reads aligned to minus strand at the top of the image).

          I don't know how to count reads in IGV, but I counted them with htseq-count and there were 6661 reads for this gene in this sample (in fact, maybe more than this because I filtered by reads aligned in proper pairs before counting).

          Maybe could you share your visualization?

          Thanks!

          Comment

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