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I would be grateful if someone could take a quick look at these FASTQC results. This is rna-seq paired-end data. From the FASTQC manual, an unusual distribution seems to be suggestive of contamination and a shift in the curve is suggestive of a systematic bias. GC content distribution both pre-alignment and post-alignment are strange.

Samples are paired end, strand specific and % of mapped reads is above 95% for all the samples. There is no adapter content also.

What could be the problem?

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    $\begingroup$ What are you trying to sequence? Something more specific than "poly-A amplified RNA" would be helpful to know if this is unexpected. $\endgroup$
    – gringer ♦
    Feb 18, 2018 at 23:38
  • $\begingroup$ In the line of gringer. What specie are you sequencing ? Is there some background about GC distribution for this type of sequencing? Did you check with other previous sequences to have such a problem? (It could be a termal microorganism and be totally normal or not...) $\endgroup$
    – llrs
    Feb 19, 2018 at 8:38
  • $\begingroup$ Its human species. except per sequence GC content and duplication levels of every sample other plots are totally fine. there is no adapter content also. samples are strand specific sequenced with poly-A selection. % of mapped reads is above 95% for all the samples. Paired end reads % average GC content is <50% or equal to 50%. % of duplicate reads is 65-70% $\endgroup$
    – beginner
    Feb 19, 2018 at 9:15
  • $\begingroup$ I see sequence bias in 5' and 3' end also in per base sequence plot. Is this GC content distribution due to bias selection? $\endgroup$
    – beginner
    Feb 19, 2018 at 10:11
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    $\begingroup$ Sorry, our mapping pipeline does all this automatically and I had to dig out the code to remind myself how its done. If you think you've got a very skewed counts distribution, i'd start by looking at the identities of the genes with the highest counts. Alternatively to look directly at rRNA genes, you can download their annotations from UCSC or biomart with the correct biotypes. For the ribosomal proteins use go term GO:0003735. Then quantify with whatever you quantify with. % reads mapping to protein coding genes is generally a pretty good QC metric. $\endgroup$
    – Ian Sudbery
    Apr 23, 2018 at 9:43

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Although generally I recommend people to be wary about the warnings that FastQC gives (it tends to be overly paranoid), the GC content graphs here do look odd. It's good that you've had a look at it with another program to confirm the observation. They should have a distribution that is fairly close to normal, a bit like this:

GC content from mouse cDNA

Without more information about your sample, I can't tell whether or not this is expected from your input. This looks like fairly substantial contamination, but could also be caused by biased selection during sample preparation (e.g. low quality sample, low yield, PCR amplification bias), or just looking at a multi-organism sample rather than a single-organism sample.

As a contamination check, I'd recommend running your RNASeq reads through a kmer-based classification tool (e.g. OneCodex, Centrifuge, BBSketch, or Sourmash). This should tell you the most likely place that contaminant sequences came from.

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