>I went to see a talk by Dr. Irmtraud Meyer yesterday afternoon, over on the UBC campus. I haven’t been down that way for a long time – and it was just as gloomy in the rain as it was when I did my masters there. (The bright red trees do make a nice contrast in the fall, however.)
The title of the talk was “Investigating the Transcriptome of Higher Eukaryotes”, which had me fooled into thinking it would be directly relevant to the work I’m doing and transcriptomes of human beings. Alas, I was wrong. However, it was related to some work in which I was involved during my masters degree. Oddly enough, that was a course project that just turned out well, and provided the group with a publication on inverse RNA folding, and sent one of the brighter grad students down the path of RNA work.
As I said, Dr. Meyer’s work was quite interesting, and – in a strange way – turned out to be relevant after all. As a person working on transcriptomes, I tend to have the view that RNA are linear bits of sequence, which cells produce as part of the pathway of producing proteins.–
That is the classical view of mRNA – and we tend not to stop and re-think it. However, that’s exactly what I ended up doing yesterday.
Two interesting bits of information came up that I knew in general, but hadn’t really processed:
- 80% of transcribed sequence corresponds to unannotated regions (Science, 2005, 308:1149-1154)
- 40-65% of known mammalian genes are alternately spliced (Science, 2005, 309:1559-1563)
And then, there’s ample evidence that RNA folding is involved in alternate splicing… well. Suddenly it’s hard to think of those little RNA sequences as linear strings – it’s hard even to think of them outside of the normal context of transcription and translation. Yet, we have tRNA, mRNA and even miRNA! Clearly transcriptomes aren’t the simple model that we perceive them to be in genomics.
While it’s nice to have a clearer picture on what’s going on at the molecular level, I don’t really know how to apply this information. I can’t use it to analyze the transcriptomes I work with, and I can’t use it to deal with alternative splicing that I see. I can’t even figure out what all those splice sites are, yet, but eventually, this information will have to be integrated into our annotations. miRNA and “junk” RNA all probably have meaning, which we just don’t understand at that level, yet.
Just a few more things to work on in the future, I suppose.