text: user9373: In the beginning of my pipeline, I just fed the paired reads (2 files) into fastp, with the default options, and assumed it would do a good job preparing the reads for the next step:  alignment
  But I now checked the files in FastQC and it doesn't show much difference from before the fastp processing: Some extra yellow and red signals appear. I think the end result is even worse.
  fastqc doesn't analyse paired reads files together. So it might be wrong regarding the paired quality, taken as a whole.
  Anything else I should use together with FastQC, to verify the output quality?
  I'm aligning a bunch of poolseqs and then comparing them to find consistency in the change of snp frequencies
  As requested here is one of the examples that made me ask this:
  Before fastp. only one problem noted by fastqc
  
  After fastp. overall quality seems to have decreased
  
  The raw data is a pool seq from illumina.
  
  user475: On a regular basis, using FastQC is quite enough as to assess the quality of FastQ-formatted data. It gives you plenty of details as can be seen in the screenshots you shared.
  As others have commented  or suggested, there is  nothing wrong with having more 'yellow' warnings after processing the FASTQ files using fastp. At lest not as long as what you observe in the data looks fine and the warning has some explanation.
  The sequence length distribution warning is very often present post-processing since at the begging all your reads are of the same length but this is not the case any more after you trim the adapters and the low quality bases at the end of the read for example. You have to go to the plots specific to that section and make sure all looks fine. It should be the case.
  The GC sequence content warning. It depends a lot what organism you are sequencing, which regions of the genomes, etc. Importantly, what is the expected GC content of those regions. So you can compare to these observed numbers. Once again the warning can be easily triggered after the processing, since for a start Illumina sequencing has known GC bias
  Finally, I think actually your reads look better  after the processing with fastp. You are showing us the per base sequence quality and before, there are many reads with bases on the yellow and even the red sections of the quality scale. But this is not the case any more after the processing !
  
original_text: 9373: <p>In the beginning of my pipeline, I just fed the paired reads (2 files) into fastp, with the default options, and assumed it would do a good job preparing the reads for the next step:  alignment</p>
  <p>But I now checked the files in FastQC and it doesn't show much difference from before the fastp processing: Some extra yellow and red signals appear. I think the end result is even worse.</p>
  <p>fastqc doesn't analyse paired reads files together. So it might be wrong regarding the paired quality, taken as a whole.</p>
  <p>Anything else I should use together with FastQC, to verify the output quality?</p>
  <p>I'm aligning a bunch of poolseqs and then comparing them to find consistency in the change of snp frequencies</p>
  <p>As requested here is one of the examples that made me ask this:</p>
  <p><strong><em>Before fastp. only one problem noted by fastqc</em></strong></p>
  <p><a href="https://i.stack.imgur.com/8Fl4c.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/8Fl4c.png" alt="before fastp. only one problem noted by fastqc" /></a></p>
  <p><strong><em>After fastp. overall quality seems to have decreased</em></strong></p>
  <p><a href="https://i.stack.imgur.com/LPxDf.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/LPxDf.png" alt="after fastp. overall quality seems to have decreased" /></a></p>
  <p>The raw data is a pool seq from illumina.</p>
   475: <p>On a regular basis, using FastQC is quite enough as to assess the quality of FastQ-formatted data. It gives you plenty of details as can be seen in the screenshots you shared.</p>
  <p>As others have commented  or suggested, there is  nothing wrong with having more 'yellow' warnings after processing the FASTQ files using fastp. At lest not as long as what you observe in the data looks fine and the warning has some explanation.</p>
  <p>The sequence length distribution warning is very often present post-processing since at the begging all your reads are of the same length but this is not the case any more after you trim the adapters and the low quality bases at the end of the read for example. You have to go to the plots specific to that section and make sure all looks fine. It should be the case.</p>
  <p>The GC sequence content warning. It depends a lot what organism you are sequencing, which regions of the genomes, etc. Importantly, what is the expected GC content of those regions. So you can compare to these observed numbers. Once again the warning can be easily triggered after the processing, since for a start Illumina sequencing has known GC bias</p>
  <p>Finally, I think actually your reads look better  after the processing with fastp. You are showing us the per base sequence quality and before, there are many reads with bases on the yellow and even the red sections of the quality scale. But this is not the case any more after the processing !</p>
  