text: user14961: Recently I have ran a human WGS on the BGI DNBSEQ system, and their FASTQ quality scores seem to be quite impressive, where the Phred scores barely deteriorate along the read length when checked on FastQC.
  
  Since the DNBSEQ technology is relatively new to me, I wonder if there's any bioinformatic approach to test whether the Phred scores listed in these FASTQ files are authentic or not? I'm kind of worried that their sequencer would just falsely assign high Phred scores to all bases.
  
  user73: Yes, reported accuracy can be tested and compared with actual accuracy by mapping reads from a reference sample to a known-perfect reference sequence. It's usually done in aggregate, at a read level rather than at single-base level.
  I like using Heng Li's gap-compressed identity statistic for this:
  
  Alternatively, fish out a few reads at random, and use BLAST, which reports an identity score.
  If the sample is not a reference sample with a precisely known sequence, then this approach will not work, because introduced variation will confound the comparison.
  While it might be possible to create a new reference sequence from the reads, this introduces the possibility of systematic error in the sequencer that would be hidden by the new reference sequence, and when you're looking at error rates in the realm of q30 and above (as in your plot), those small differences are going to matter a lot.
  
original_text: 14961: <p>Recently I have ran a human WGS on the BGI DNBSEQ system, and their FASTQ quality scores seem to be quite impressive, where the Phred scores barely deteriorate along the read length when checked on FastQC.</p>
  <p><a href="https://i.stack.imgur.com/okf48.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/okf48.png" alt="DNBSEQ_fastqc" /></a></p>
  <p>Since the DNBSEQ technology is relatively new to me, I wonder if there's any bioinformatic approach to test whether the Phred scores listed in these FASTQ files are authentic or not? I'm kind of worried that their sequencer would just falsely assign high Phred scores to all bases.</p>
   73: <p>Yes, reported accuracy can be tested and compared with actual accuracy by mapping reads from a reference sample to a known-perfect reference sequence. It's usually done in aggregate, at a read level rather than at single-base level.</p>
  <p>I like using Heng Li's <a href="http://lh3.github.io/2018/11/25/on-the-definition-of-sequence-identity" rel="nofollow noreferrer">gap-compressed identity statistic</a> for this:</p>
  <p><a href="https://i.stack.imgur.com/Y1V6J.jpg" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/Y1V6J.jpg" alt="mean base quality vs gap-compressed identity for Nanopore reads" /></a></p>
  <p>Alternatively, fish out a few reads at random, and use BLAST, which reports an identity score.</p>
  <p>If the sample is not a reference sample with a precisely known sequence, then this approach will not work, because introduced variation will confound the comparison.</p>
  <p>While it might be possible to create a new reference sequence from the reads, this introduces the possibility of systematic error in the sequencer that would be hidden by the new reference sequence, and when you're looking at error rates in the realm of q30 and above (as in your plot), those small differences are going to matter a lot.</p>
  