Answers.Body: <p>You're attempting pair-ends assembly without complete coverage, e.g. short-read Illumina.</p>
  <p>Switch to fastp for QC and follow this up with Megahit or metaSpades. You will lose sequences maybe by as much as 10-fold loss, but you'll recover quality data.</p>
  <p>If this fails there are alternative approaches.</p>
  
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Answers.Text: Thanks for your response. My understanding was that these methods (qiime2) were designed for short-reads though, were they specifically designed for long reads ? I will look into these other methods, though !
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Answers.Text: qlime2 claims are clearly not correct.
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Answers.Text: Noted ! Thanks for your input.
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Body: <p>I am currently analysing some 16S (V4 region) rRNA data, using the qiime2 command-line software. Everything is working fine for the most part, until we are to merge the forward and reverse sequences - this is where the problem arises.</p>
  <p>The command used to do this, is the following:</p>
  <pre><code>qiime vsearch merge-pairs \
    --i-demultiplexed-seqs reads_qza/reads_trimmed.qza \
    --output-dir reads_qza/reads_joined
  </code></pre>
  <p>Where <code>reads_trimmed.qza</code>, is a qiime2 'artifact' i.e. object, which contains all of our samples which have been run through <code>cutadapt</code>, in order to trim primers/adaptors.</p>
  <p>As the title says, when merging the pair of sequences, we get almost no sequences merged, in the end, please refer to this image for a visual representation of what this looks like:<a href="https://i.sstatic.net/e8N69RVv.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/e8N69RVv.png" alt="enter image description here" /></a></p>
  <p>We tried to modify the above script to be more lenient with regards to the merging of sequences, by adding the argument <code>--p-maxdiffs 50</code>, which is defined as:
  Maximum number of mismatches in the area of overlap during merging.</p>
  <p>But to no avail, we still have basically no matches, with a median read number of less than 1.</p>
  <p>What could be causing this issue? Has anyone faced this before? Could this have to do with the sequencing technology? We used Illumina NextSeq, and because of it opted to use qiime and deblur rather than dada2, due to binned quality scores associated with this technology, there's an interesting discussion on this here:
  <a href="https://github.com/benjjneb/dada2/issues/1307" rel="nofollow noreferrer">https://github.com/benjjneb/dada2/issues/1307</a></p>
  <p>Thank you,</p>
  
Tags: <phylogenetics><ngs><metagenome><16rrna><qiime2>
Title: 16S merging sequences, removes ALL reads
ThreadText: # 16S merging sequences, removes ALL reads
  
  (Asked by: username_0 on 2024-09-06)
  
  I am currently analysing some 16S (V4 region) rRNA data, using the qiime2 command-line software. Everything is working fine for the most part, until we are to merge the forward and reverse sequences - this is where the problem arises.
  
  The command used to do this, is the following:
  
  ```
  qiime vsearch merge-pairs \
    --i-demultiplexed-seqs reads_qza/reads_trimmed.qza \
    --output-dir reads_qza/reads_joined
  
  ```
  
  Where `reads_trimmed.qza`, is a qiime2 'artifact' i.e. object, which contains all of our samples which have been run through `cutadapt`, in order to trim primers/adaptors.
  
  As the title says, when merging the pair of sequences, we get almost no sequences merged, in the end, please refer to this image for a visual representation of what this looks like:[![enter image description here](https://i.sstatic.net/e8N69RVv.png)](https://i.sstatic.net/e8N69RVv.png)
  
  We tried to modify the above script to be more lenient with regards to the merging of sequences, by adding the argument `--p-maxdiffs 50`, which is defined as:
  Maximum number of mismatches in the area of overlap during merging.
  
  But to no avail, we still have basically no matches, with a median read number of less than 1.
  
  What could be causing this issue? Has anyone faced this before? Could this have to do with the sequencing technology? We used Illumina NextSeq, and because of it opted to use qiime and deblur rather than dada2, due to binned quality scores associated with this technology, there's an interesting discussion on this here:
  <https://github.com/benjjneb/dada2/issues/1307>
  
  Thank you,
  
  **Tags:** phylogenetics, ngs, metagenome, 16rrna, qiime2
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2024-09-06. Score: 1 | Accepted answer
  
  You're attempting pair-ends assembly without complete coverage, e.g. short-read Illumina.
  
  Switch to fastp for QC and follow this up with Megahit or metaSpades. You will lose sequences maybe by as much as 10-fold loss, but you'll recover quality data.
  
  If this fails there are alternative approaches.
  
  > **Comments:**
  > **username_0**: Thanks for your response. My understanding was that these methods (qiime2) were designed for short-reads though, were they specifically designed for long reads ? I will look into these other methods, though !  
  > **username_1**: qlime2 claims are clearly not correct.  
  > **username_0**: Noted ! Thanks for your input.  
  
  ---
  
  