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I am building a De Novo transcriptome reference assembly for an eukaryotic organism for which I have a genome.

I've created several assemblies with rnaSpades using different kmer sizes (19 to 69 with step 10). Now I would like to merge them into one final transcriptome.

How could I do that?

Is using a genome assembly reconciliation tool such as metassembler a good idea?

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  • $\begingroup$ Do you have a genome for the species you have data for? $\endgroup$ – conchoecia Jul 16 '18 at 23:07
  • $\begingroup$ @conchoecia I do have a genome, although it's only a draft genome. I'm currently using it to compare the quality of my assemblies $\endgroup$ – Pitagoras Alves Jul 18 '18 at 0:49
  • $\begingroup$ Possible? Sure, but there is a danger in doing meta-assembling. BUSCO is an ever-increasing score - if you merge multiple assemblies it will always improve because it's not really telling you much about false positives. I am not sure what do you want to use the transcriptome assembly for, but those won't ever be perfect and loads of effort might lead to only a marginaly better transciptome. $\endgroup$ – Kamil S Jaron Aug 20 '20 at 12:43
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You may combine all your transcriptomes into a single file and then apply a clustering method to group very similar transcripts into a single one. For that purpose, you may try CD-HIT-EST or MMseqs2. For each identity threshold you are going to test, you may assess the final quality with BUSCO or by blasting against reference sequences.

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A simple option would be to simply supply SPAdes with some contigs that you like with the --trusted-contigs option.

There exist tools specifically addressing the problem of merging transcriptome assemblies. I am unsure whether this is notably different from genome assemblies sufficiently that they are better than the genome assembly mergers. Here is a partial list:

  1. transfuse
  2. this paper
  3. this paper
  4. this paper
  5. DRAP

This paper seems to have some more information on comparing tools, though it is not focused specifically on merging.

See also the SeqAnswers on this topic, and this discussion too.

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