The philosophy of -omics studies

As a physcist, I'm a relative newcomer to the biological sciences. As a result, many approaches to doing science within these fields are new and really interesting to me.

One such idea is the -omics approach to science. Sonja Prohaska and Peter Stadler provide an insightful and sometimes amusing intrepetation of -omics studies in an article in the book Bioinformatics for Omics Data. (The PubMed link to the article is here, but you can find a pdf by Googling "The use and abuse of -omes.") According to them, -omics refers to the research field of digesting the pile of data coming from measurements of a particular -ome, such as the "genome" or "transcriptome." The goal is to relate the collection of parts within the -ome to biological function, or at least to determine function by comparing -omes of two different organisms.

The authors explain that -omics approaches have three components, which I quote from their article:

  1. A suite of (typically high-throughput) technologies address a well-defined collection of biological objects, the pertinent -ome.
  2. Both technologically and conceptually there is a cataloging effort to enumerate and characterize the individual objects–hopefully coupled with an initiative to make this catalog available as a database.
  3. Beyond the enumeration of objects, one strives to cover the quantitative aspects of the -ome at hand.

While grand in scope, these approaches carry difficulties that to me appear unique. As stated above, -omic information is acquired through high-throughput techniques, which means that they generate very large amounts of data. Of major concern is actually linking this data to biological function. In other words, scientists must answer whether the correlations in the data actually allow us to predict the behavior of a cell or tissue. As might be expected, generating a complex data set such as is found in -omics studies can be quite impressive at first glance. But this complexity may hide the fact that no biologically relevant conclusions can be drawn from it.

The authors specifically enumerate four limitations that could adversely affect -omics studies:

  1. Technical limitations
  2. Limitations in the experimental design (such as heavy reliance on assumptions)
  3. Conceptual limitations
  4. Limitations in the analysis

Particularly, the conceptual limitations struck me as intriguing. The authors offered the example of genomics studies in which the notion of a "gene" is not currently a well-defined concept. When the concepts underpinning an entire collection of measurements is unclear, we should question whether the measured data has the meaning that we think it does.

Overall, I found that this commentary provided an interesting and sobering view of one particular approach to studying biology. It's interesting because I suspect that many important biological questions in the near future will come from taking an integrated and systems perspective. This perspective will require high-throughput techniques that carry the same limitations that -omics studies have.


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