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  • Filtering by per base Phred Scores pre-methylation calling

    In the Quality Control Step of a typical NGS post-sequencing pipeline, we used Trim Galore! for phred score trimming of reads. As I understand it, Trim Galore! trims the ends of a read should they fall under the specified phred score threshold but does not remove bases which may fail the threshold should they be within the read (from Trim Galore! –help: The algorithm is the same as the one used by BWA (Subtract INT from all qualities; compute partial sums from all indices to the end of the sequence; cut sequence at the index at which the sum is minimal)). As a result, reads potentially contain bases with phred scores under the threshold (tested and shown in our data).

    During methylation calling, we note that some softwares further filter for base phred score before methylation calling (Strand NGS, MethylExtractor), whereas others recommended for deduplication before methylation calling (Picard, samtools) do not. Filtering per base phred scores can potentially heavily influence the outcome methylation call (also tested and shown in our data). Agreeably should a significant quantity of reads contain low phred scores in the middle of the read, it would be reflected in various modules of FastQC – however, our question is, is it necessary to further filter by per base phred scores after an initial read phred filter by Trim Galore! and no major flags by FastQC?

  • #2
    Yes, you should always filter by phred score. If a base has a score of 0 or 1, then you wouldn't want to rely on it. SNP callers (e.g., samtools mpileup) can handle low quality bases in their calculations, but that's not possible with methylation extraction.

    Also, you should make a methylation bias plot and exclude bases based on position within reads if needed. You can use PileOMeth for all of this.

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    • #3
      I agree that very poor basecalls should be excluded, which is why Trim Galore aims to remove qualities lower than 20. This is not an absolute cutoff but more of a sliding window approach, and as you correctly found this leaves the occasional basecall with a quality below 20 in. I can’t give you an exact number for this but my gut feeling is that the total number of these calls is almost negligibly small (almost certainly much smaller than 0.1% of all calls).

      For these there is a then a chance that the position involved is not a C (~75% of cases), and if it affected a cytosine position there is then the chance that the basecall itself was correct even though the technical basecall quality was a little low (very poor qualities would have been trimmed anyway). This would reduce the potential source of error by another order of magnitude. Finally, these rare errors would then most likely be spread randomly over a very large genome so in my opinion filtering on Phred scores in addition to quality trimming is unlikely to change the outcome of your results noticeably.

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