Central dogma rates may explain why transcript measurements seems like such junk science!

Okay -- this new Nature Communications study might just be totally awesome.

I'm going to go with "might" for the following obvious reasons:

1) The terminology is outside of my wheelhouse
2) The math is so far away from the house where I store my wheels that it...(wait...what the hell is wheelhouse anyway..?...) that it could be complete and total gibberish and I would never ever know. My eyes stopped focusing as soon as I got to the page with the formula things.

HOWEVER:

1) A world class expert in these transcript words sent it to me with exclamation points on it.

2) Every person in proteomics has tried, at some point, to correlate transcript signal to real protein measurements and has walked away thinking "the transcript people are clearly just making numbers up" or "the transcript people are clearly just making everything up and one day we'll laugh about the trillions of dollars science spent on this generation of sequencing technology the same way we look back on global microarrays -- as expensive technology that yielded little or nothing"  Which, hopefully isn't true. I'm not saying it is! That's just what I might think for just one second when someone says "this mi-Seq says there are tons of transcipts so there will be tons of protein" and 15 SILAC peptides say "....NOPE! This is 1:1, yo" Maybe this awesome paper explains what's up!

3) This study might be the great unifier (which, apparently, isn't a word). This group looks at loads of transcription/translation rates in rapidly growing cells from bacteria, yeast, my favorite model organism for human disease, and humans -- and finds there are mechanistic reasons that genes would have high transcription but also low translation. And this might be gene specific. And it might be possible using these techniques to develop a metric for each gene, as in, this a gene that will correleate in transcript abundance and protein abundance -- and this one will not. What a useful database that could be, right?

I guess you could just measure the protein level directly using some sort of comprehensive -omics based measurement of protein levels, if such a thing existed (and cost a tiny fraction of the price to measure transcript abundance), but I'm sure there are diseases and models and all sorts of things where it is still important to understand the indirect things that lead to protein expression as well. It might seem simpler to us to move 1% of what is spent on transcriptomics per year over to direct global protein measurements, but whenever a solution seems that simple and direct to me it's generally that I don't fully understand the problem.

I'll be thinking about this paper all day. Maybe I'll even ask someone who understands the finer points to talk me through more of it!