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Once you have assembled your RNA-seq reads into transcriptomes, you'll want to have some sense of assembly quality, or just a summary of your transcriptome in general (number of transcripts, number of predicted genes, estimated completeness, longest transcript etc.)

A common tool to evaluate genome assemblies is [[https://github.com/ablab/quast|QUAST]]. Thankfully the fine folks who created QUAST also created a special tool to evaluate transcriptome assemblies: [[https://github.com/ablab/rnaquast|rnaQUAST]]. This is the tool that we will be using.

Here is an example perun submission script:
<code>
#!/bin/bash
#$ -S /bin/bash
. /etc/profile
#$ -cwd
#$ -m bea
#$ -pe threaded 8
#$ -q 144G-batch,256G-batch,768G-batch

source activate rnaquast-2.2.0

rnaQUAST.py \
    -o 6_rnaquast-2.2.0 \
    -c 4_trinity-2.11-with-work-around/Trinity.fasta 5_rnaspades-3.14.1/transcripts.fasta \
    -t 8 \
    -l trinity rnaspades \
    -ss \
    --gene_mark \
    --busco /home/jmartijn/db/busco/eukaryota_odb10
</code>

The script activates the rnaquast-2.2.0 conda environment. 2.2.0 is the latest version as of January 2021. This environment contains all the necessary software to run the job smoothly.

Quick explanation on the options:
<code>
-o            output_directory
-c            transcriptome1.fasta transcriptome2.fasta ... transcriptomeN.fasta
-t            number_of_threads
-l            transcriptome_label1 transcriptome_label2 ... transcriptome_labelN
-ss           invoke if you used a strand specific mRNA library
--gene_mark   to activate gene prediction with GeneMark
--busco       to activate completeness evaluation with BUSCO
</code>

The labels are used to rename your transcriptomes in the context of the rnaQUAST analysis. ''transcriptome1.fasta'' will be renamed to ''transcriptome_label1'', etc etc.

Note that the submission script is specifically asking to run on ''144G-batch'', ''256G-batch'' or ''768G-batch'' nodes. For some unclear reason only these nodes will run the software smoothly without fail. It may work on some specific ''16G-batch'' nodes as well but its a gamble.

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