While metatranscriptomics reveals information about the expression of genes and their functions too, Functional metatranscriptomics (https://www.nature.com/articles/ismej201167) allows the characterization of genes expressed by different eukaryotic microorganisms. My question is can we use both of those techniques? I couldn't find out if they will bring the same results or each one of them will give its own.
I would consider these to be complementary approaches. They don't really provide the same information, or even the same kind of information.
A big challenge with sequence-based genomics and transcriptomics is that they rely on sequence homology for functional annotations. In other words, they only provide indirect information about gene function, which can be incorrect or, in many cases, completely absent (this is why you often see annotations like "hypothetical protein xxx" in genomics papers). So what can happen is, you may identify a great number of genes with no functional annotation, so you have no basis for determining what they are doing, or worse, they are incorrectly annotated and you publish misleading reports. This is especially challenging in complex and diverse environments like soil.
The advantage of the functional approach is that you don't have to rely on sequence homology for functional annotation, since you're characterizing the phenotype directly. The disadvantage is that you can only fined the specific functions you are looking for. To bring it back the example you linked, if you're only screening for dipeptide transporters, you would not be able identify anything that doesn't directly influence that phenotype.
So, it really depends on what information your are after in your study. If you wanted to completely replace the functional genomics approach with sequencing approaches, it would only work if your genes of interest already have reliable functional annotations and if your sequencing depth is sufficient to identify them. That said, sequences from a functional study like the one you shared could be used to inform your sequencing approach, or to mine existing metagenomic or metatranscriptomic datasets for genes with share homology. Heck, if you only have a few target genes and they have some well conserved regions, you could probably come up with an acceptable RT-PCR approach too.
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