What does OMICS really mean?

Omics is a popular buzzword in the science world. Thought leaders are always discussing the importance of using an omics approach to study everything from cancer to coconuts. So what in the world is an omic and how is it useful?

Genomics

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.

Transcriptomics

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.

Proteomics

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.

Metabolomics

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.

Challenges in omics studies

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….

Omics and big data

That’s why omics is often accompanied by another buzz word: big data. Unravelling the data pertaining to all of the genes in a genome and all of the transcripts in a transcriptome and all of the proteins in a proteome and so on to determine what is meaningful is a massive challenge. Analyzing big data requires advanced computational approaches and often the help of artificial intelligence. Omics has thus inspired a whole new field of science that combines computational know-how and mathematical modeling with biology.