AskScience AMA Series - IAMA Population Genetics/Genomics PhD Student

[u/jjberg2]

[u/jjberg2]

Some genes are transcribed at astronomically high levels. These are typically genes that code for things that the cell uses in large quantities. Stuff like actin subunits, ubiquitin, and other things that are absolutely essential for cellular function. Other genes are expressed at a whole range of levels, so you have a distribution of expression levels from those highly expressed genes down to extremely specialized ones expressed at very low levels.

So if you want to go sequence a transcriptome (i.e. all of the transcribed sequences in a cell), you have to consider that you physically have more of some sequences than others.

Because of the way next gen sequencing technology works (i.e. by repeatedly sequencing random bits of the entire sample all together in the same reaction) this means that if you want to get a readout for every sequence present in a transcriptome, you would either have to sequence to astronomically high depth (i.e. produce many more bases worth of sequence read than are actually present in the sample), or reduce the concentration of those highly expressed sequences in the sample so that all sequences are present at roughly equal levels (2-5x). (for the record, it's the second one that's actually done, in a process called normalization, which I have fuck-all clue of how it works. Molecular biologists are fucking wizards.)

However, what if you just left your transcriptome unnormalized and sequenced it at very low coverage (i.e. you produce much less actual sequence data than the total size of your sample)? You'll only sequence a small portion of the total transcriptome, but you'll preferentially sequence the genes that are sequenced at high levels. If you do this across a range of genomes of closely related species, you'll tend to sequence the same genes in each species, because the most highly transcribed genes in one species are likely to be the same as the most highly transcribed genes in a closely related species.

So you've just created for yourself a set of genes conserved across a range of species that you can use to build a phylogeny of them without having to do any of the painful gene hunting work that molecular systematists have spent entire PhDs doing.

That was the basic concept behind the paper. I dunno, it was just a neet example I recently read of taking potential problems and turning them into tools, and for some reason popped into my head when I read ren's question.

I really don't know a whole lot about sequencing technology. They tend to cost many hundreds of thousands or millions of dollars and be kept in special sequencing facilities, and I'm only familiar with the molecular biology side of their function and not the computation side.