The Three R’s of Genetics

monk scribe

Walk before you can run, read before you can write

The three ‘R’s of genetics are reading, writing, and arithmetic. Of course, only one of those skills starts with an actual letter ‘R’, but as the arithmetic portion of genetics is often met using a statistical program that happens to be called ‘R’, it’s only ‘writing’ that is the real outlier.  Writing stands out in another way, besides being spelled ‘rong. We read a lot of genetic sequence (almost 1.5 trillion bases of whole genome sequence has been read, to date). We use a lot of arithmetic in figuring out what those 1.5 trillion bases are doing. However we have yet to write out a whole animal genome. We change a word here and there in the genetic code, but writing isn’t a skill we’ve mastered in genetics. That could soon change.

Though reading usually follows writing, in genetics it’s the opposite. We can read well, but writing is hard.

In general, things are written before they are read. Even the world’s sacred texts, which you could imagine might arise via supernatural fiat and spontaneously burst into being, didn’t. They were written. God wrote the ten commandments on stone tablets with his finger (pen doesn’t write well on stone, and crayons just melts in his hands). Moses wrote down the first five books (except probably the last bit, where he dies). The Vedas began as an oral tradition, the Koran was transcribed by Muhammad’s disciples, and the entire Star Wars prequel series was simply adlibbed throughout. The closest inversion of the normal order of write-> read is in the book of John, where he says that, “In the beginning was the Word”. But this doesn’t seem to be a word that was actually written somewhere. It just Is. Which certainly saves on paper.

The human genome is an exception, we first read it (that was the Human Genome Project, completed in April of 2003), and we’re now just beginning to talk about writing it.

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In Defense Of Irreproducible Results

Kirk-Tribbles-3

The dangers of p-values and Klingons

Science is hard. Well, lots of things are hard. Baking a good baguette is hard. Remembering everyone’s birthday (before their birthday) is hard too, as are granite boulders. But science has a special trick up its sleeve that makes it quantifiably hard: the p-value. P-values are supposed to help us identify results that are statistically significant, but getting a low p-value is difficult. In the medical sciences, achieving statistical significance usually means having a good question, a lot of patient samples, and doing your assays and calculations well. The former requires cleverness, the middle requires resources, and the final entails diligence. Combining all these traits in one researcher, or even one research team, is hard. I, for one, still haven’t figured out what all the bins in my refrigerator are for, let alone be able to manage an entire clinical study workflow.

And to make it worse, the scientific community is now taking very close looks at everyone’s p-values to make sure that these low p-values mean what they’re supposed to mean. Not only should data be significant, but it should be reproducible. Unfortunately it seems that research results frequently are not reproducible. In fact, we are in the midst of a “reproducibility crisis”, according to some. Various studies have suggested that most published results, in medicine and the social sciences, are not repeatable, despite having nice p-values in the original study.

Does this reproducibility crisis merit pitchforks and torches, or better study design and a philosophical debate? Why not both?!

Why is this? Is the reproducibility crisis due to a mixture of fraud and lazy science, for which pitchforks, lighted torches, and storming the gates are the best response? Or is it more complicated, requiring more agreement on good study design and an understanding of just how reproducible scientific results really should be? Probably mostly the latter, but we can add in a little of the former too, just to keep in interesting.

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