text: user1637: I have a de novo assembly using both multiple SRA and locally sequenced transcriptomes. I started with 270M PE reads from 9 tissues. Here are the assembly stats generated with TrinityStats.pl:
  ################################
  ## Counts of transcripts, etc.
  ################################
  Total trinity 'genes':  543668
  Total trinity transcripts:      1111089
  Percent GC: 33.77
  
  ########################################
  Stats based on ALL transcript contigs:
  ########################################
  
          Contig N10: 2117
          Contig N20: 1324
          Contig N30: 945
          Contig N40: 714
          Contig N50: 561
  
          Median contig length: 344
          Average contig: 496.17
          Total assembled bases: 551293315
  
  
  #####################################################
  ## Stats based on ONLY LONGEST ISOFORM per 'GENE':
  #####################################################
  
          Contig N10: 2172
          Contig N20: 1333
          Contig N30: 925
          Contig N40: 681
          Contig N50: 523
  
          Median contig length: 323
          Average contig: 473.63
          Total assembled bases: 257499729
  
  As you can see, the total Trinity 'genes' number is very high. I suspect that this is due to a high biological variability of the transcriptomes (coming from organisms sampled in Italy, China, Spain...). 
  
  How can I assess whether the problem is really the biological variability?
  How can I reduce this number? I'd rather not downsample the reads. It looks like CD-Hit might do what I'm looking for...
  
  
  user73: As you've suggested, CD-HIT works for reducing transcript numbers. We used a mixture of expression-based filtering and CD-HIT for reducing transcript counts for our genome-guided transcriptome assembly. This reduced numbers by a lot, without much change in BUSCO scores:
  
  Map RNA-seq reads to Trinity-generated transcripts using Salmon
  Use the expression of BUSCO genes to set a credible signal cutoff
  Subset transcripts based on this threshold [for us it was 50 counts]
  Identify the longest Met to Stop open reading frame for each transcript (may not be a good idea in all cases)
  Run cdhit to collapse similar transcripts
  
  Here's our CD-HIT command line:
  cdhit -T 10 -c 0.98 -i longest_MetStopORF_HC50_TBNOCFED.fasta -o cdhit_0.98_LMOHC50_TBNOCFED.prot.fasta
  
  More details here:
  https://bmcbiol.biomedcentral.com/articles/10.1186/s12915-017-0473-4#Sec9
  
original_text: 1637: <p>I have a <em>de novo</em> assembly using both multiple SRA and locally sequenced transcriptomes. I started with 270M PE reads from 9 tissues. Here are the assembly stats generated with <code>TrinityStats.pl</code>:</p>
  
  <pre><code>################################
  ## Counts of transcripts, etc.
  ################################
  Total trinity 'genes':  543668
  Total trinity transcripts:      1111089
  Percent GC: 33.77
  
  ########################################
  Stats based on ALL transcript contigs:
  ########################################
  
          Contig N10: 2117
          Contig N20: 1324
          Contig N30: 945
          Contig N40: 714
          Contig N50: 561
  
          Median contig length: 344
          Average contig: 496.17
          Total assembled bases: 551293315
  
  
  #####################################################
  ## Stats based on ONLY LONGEST ISOFORM per 'GENE':
  #####################################################
  
          Contig N10: 2172
          Contig N20: 1333
          Contig N30: 925
          Contig N40: 681
          Contig N50: 523
  
          Median contig length: 323
          Average contig: 473.63
          Total assembled bases: 257499729
  </code></pre>
  
  <p>As you can see, the total Trinity 'genes' number is very high. I suspect that this is due to a high biological variability of the transcriptomes (coming from organisms sampled in Italy, China, Spain...). </p>
  
  <ul>
  <li>How can I assess whether the problem is really the biological variability?</li>
  <li>How can I reduce this number? I'd rather not downsample the reads. It looks like <a href="https://github.com/weizhongli/cdhit/wiki" rel="nofollow noreferrer">CD-Hit</a> might do what I'm looking for...</li>
  </ul>
   73: <p>As you've suggested, CD-HIT works for reducing transcript numbers. We used a mixture of expression-based filtering and CD-HIT for reducing transcript counts for our genome-guided transcriptome assembly. This reduced numbers by a lot, without much change in BUSCO scores:</p>
  
  <ol>
  <li>Map RNA-seq reads to Trinity-generated transcripts using Salmon</li>
  <li>Use the expression of BUSCO genes to set a credible signal cutoff</li>
  <li>Subset transcripts based on this threshold [for us it was 50 counts]</li>
  <li>Identify the longest Met to Stop open reading frame for each transcript (may not be a good idea in all cases)</li>
  <li>Run cdhit to collapse similar transcripts</li>
  </ol>
  
  <p>Here's our CD-HIT command line:</p>
  
  <pre><code>cdhit -T 10 -c 0.98 -i longest_MetStopORF_HC50_TBNOCFED.fasta -o cdhit_0.98_LMOHC50_TBNOCFED.prot.fasta
  </code></pre>
  
  <p>More details here:</p>
  
  <p><a href="https://bmcbiol.biomedcentral.com/articles/10.1186/s12915-017-0473-4#Sec9" rel="nofollow noreferrer">https://bmcbiol.biomedcentral.com/articles/10.1186/s12915-017-0473-4#Sec9</a></p>
  