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What are the major differences between the use cases of a shot-gun genome and an mRNA capture transcriptome? Especially when it comes to downstream analyses such as looking for orthology, and selection analysis.

From what I can visualize in my head, I can come up with the following points:

  1. The genome has the structural data, information on chromosomes, and haplotypes while the transcriptome lacks this information.

  2. The genome records the SNPs more directly than transcriptomes. I might be thinking of this wrong but the genomes would have the actual bases and we can then compare the bases between the genomes but for transcriptomes differences in expression would show up as presence/absence variations that have no way of being traced back?

  3. The transcriptomes should be easier to work with when it comes to computational requirements since they tend to be 25 to 50 times smaller than genomes.

I could be completely wrong about the points I listed and could be missing something major. What do you guys think?

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It really depends on your use case. If this is human data, where we already have a fairly polished reference genome, then genome sequencing adds value for the genetic variation, e.g. rare disease genetics, GWAS or cancer genomics. Transcriptome sequencing allow exploration of tissue (or cell type) specific patterns and RNA biomarkers etc. Calling variants from RNA can be interesting in the context of splicing and RNA editing.

For a non-model organism where there isn't currently a reference genome, then the genome creates a reference for future studies to map genomic/transcriptomic reads to, so it's usual to do the genome first.

Since you mention ortholgy and selection studies, well traditional comparative genomics studies have done these analyses from the genome only, but the transcriptome can add value depending on your questions.

Finally, I wouldn't consider computational requirements as you stated: it depends on sequencing depth/coverage and other factors, and is likely not to drive the choice between the two assays

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  • $\begingroup$ Thanks for responding. So, when I mention comparative genomics what I am trying to say is that getting a good genome for a non-model organism is not easy but it seems RNA data is more abundant. I am trying to see if I can stockpile information on plant evolution from RNA data only since genomes are so rare. As for the computational part, when you feed multiple whole genomes to off-the-shelf software they usually just end up with segfaults (since whole genomes can easily be significantly larger than transcriptomes). I am sure that's not an issue in targeted research based on individual samples. $\endgroup$
    – Tangent Runner
    Commented Dec 29, 2021 at 22:50

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