In the National Academy of Science’s genetically engineered crop study report released a few days ago, they proposed a new regulatory framework for analyzing the safety of any crop variety that relies on a technology called “omics”? So what in the world is an omic and how is it useful?
There are many different types of “omics”. It all started with GENomics which involves figuring out and comparing the GENES between organisms. This has allowed scientists to identify different genetic “markers” associated with diseases and other traits. However, genomics are only part of the story. Just because a gene is present doesn’t mean it is active.
Active genes are used as a template to TRANSCRIBE molecular messengers called RNA. Analyses of the levels of RNA within an organism in a given tissue or in response to a certain stimulus are known as TRANSCRIPTomics, but it doesn’t stop there.
RNA transcripts serve as instructions that enable cells to build PROTEINS. Proteins are the real workhorses of the cell, and are largely responsible for an organism’s traits. Comparisons of the protein landscape in an organism are accomplished by PROTEomics.
But what about all the other cool stuff floating around in cells? Hormones, pigments, vitamins, ethanol? These molecules are assembled within cells and are called METABOLITES (and plants have way way way more of them than people: nicotine, caffeine, capsaicin…..). And guess what? Where there’s a molecule there’s an “omics”, so now we’ve got METABOLomics.
So omics includes the study of the genes, RNA, proteins, and metabolites in an organism (there are even more, but I won’t go there). Sound complicated? It gets worse. When scientists gather data for omics studies, they get it in pieces.
So you know the human genome project? It’s not like somebody scanned a human blood sample and printed out the entire genome. To put it into perspective, if the human genome was unravelled and blown up to the diameter of a strand of hair, that hair would be 30 miles long and form a wad the size of a volleyball.
The genome had to be sequences piece by piece (not particularly systematically) and then those pieces had to be reassembled. To put it into perspective, Dr. Keith Bradnom uses the following analogy: Take 100 identical jigsaw sets, mix all the pieces together, throw away 10% of the pieces, randomly mix in the contents of an unrelated puzzle, throw away the cover of the box, and assemble!
Some scientists on twitter even called this analogy an over-simplification! As you can imagine, this takes not only a lot of brain power, but also a lot of computing power. Then once you finally get it assembled….
So that’s why the National Academy of Sciences said that we need to further develop these technologies. The idea is that we could compare the “omics” between new crop varieties and existing varieties and analyze any significant differences for potential safety concerns. As you can see, this is no small project, and that’s why they call it “big data”.
If you are a non-scientist, I hope you found this explanation helpful. If you are a scientist, particularly a computational scientist, please let me know in the comments if I’ve made any mistakes or am missing important details.