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  • Paired end alignments -- What to throw away and what to keep?

    I am working with a fairly dense dataset containing 12 samples each containing 45 million paired end reads.

    I trimmed and quality filtered these reads using trimmomatic. I obtained two pair end files, and two files for orphan reads (reads that lost their pair).

    Now, I'm interested in looking for novel genes and splice sites in Canola (plant;large repetitive genome; poorly characterized; non-model organism).

    When I align my PE reads and my orphans, my data looks like this:


    Left reads:
    Input: 42041591
    Mapped: 38698515 (92.0% of input)
    of these: 20610206 (53.3%) have multiple alignments (9400 have >20)
    Right reads:
    Input: 37408393
    Mapped: 34499559 (92.2% of input)
    of these: 18350802 (53.2%) have multiple alignments (6973 have >20)
    92.1% overall read alignment rate.

    Aligned pairs: 33238603
    of these: 12676525 (38.1%) have multiple alignments
    and: 817033 ( 2.5%) are discordant alignments
    86.7% concordant pair alignment rate.


    Now I have several questions:

    First, is it recommended to use only concordant aligned pairs for downstream analyses?

    Second, should I only use uniquely mapping reads? The paper I am modelling this after only uses uniquely mapping... but that sounds counterintuitive, as this is a very repetitive genome and even though multiple alignments occur it still chooses the best one right? I was thinking I would use SAMtools to remove PCR duplicates or something (looks like there may be a few highly repetitive elements, duplicates? rRNA?)

    Why do concordant pairs (86%) and discordant alignments (2.5%) not add to be 100%? What else is there?

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