Blogging My Genome, episode 2: scratching the surface

After learning about Illumina's Understand Your Genome (UYG) program at ASHG 2013, I decided to sign up to get my genome sequenced. This is the second in a series of blog posts I'll write about my own adventure in very personal genomics!

Three months after shipping my blood sample off to the lab for whole-genome sequencing (WGS), I got the long-awaited message to come in and go over my results. And so on a rainy Friday afternoon I biked over to Stanford for genetic counseling. I was very excited, and yet not without awareness of the ~1% chance I could see one of the known pathogenic findings on the American College of Medical Genetics list for genome sequencing reports, and perhaps up to ~5% chance of some other medically actionable finding.

Fortunately, nothing like that came up. In fact, my report is quite unremarkable, which is of course a good thing:

Blogging My Genome, episode 1: parting with my blood (and treasure)

After learning about Illumina's Understand Your Genome (UYG) program at ASHG 2013, I decided to go ahead and sign up to get my genome sequenced. This is the first in a series of blog posts I'll write about my own adventure in very personal genomics!

UYG gets you:

• "Deep" whole-genome squencing (WGS) from a blood sample
• Bioinformatics and clinical interpretation through Illumina's CLIA lab
• Report sent to your clinician
• Raw data on a portable hard drive
• Day-long workshop with other participants
• iPad with the MyGenome app

...for $5,000, which isn't too bad given what's included. The pricing probably reflects that the program is mainly an outreach effort aimed at subject matter experts.

Ribosome profiling confirms widespread stop codon readthrough in flies

A recent eLife paper from the Weissman lab at UCSF uses high-throughput ribosome profiling to show that many Drosophila melanogaster (fruitfly) genes undergo an unusual translation process called stop codon readthrough - confirming many of, and expanding on, our earlier predictions based on computational comparative genomics. Stop codon readthrough occurs when a translating ribosome reaches an in-frame stop codon in an mRNA but, instead of terminating as usual, continues on with translation - as if the stop codon were a sense codon. This gives rise to a protein isoform with an extended C-terminal region, potentially modifying its function or localization.

Stop codon readthrough is a long-known but fairly obscure "recoding" mechanism, which wasn't believed to play a widespread role in metazoan gene expression, save for selenoproteins and a few other intriguing but isolated examples. Now we know that it actually affects hundreds of fly genes - and moreover that, in many cases, the products confer biological functions conserved throughout many millions of years of evolution.

Building OCaml programs in Cloud9 IDE

Cloud9 IDE is one of several new cloud-based products providing the ability to edit, build, test, and deploy code through a collaborative web application. Cloud9 IDE has one especially powerful feature: each workspace (i.e. project or repo) has a Linux home directory persisted along with the code and other settings, and you get a full bash terminal in this directory with modest but usable resource limits. This means it's possible to install and use a full OCaml toolchain inside, much like my previous effort on Travis CI.

I prepared a script to automate the process of installing OCaml and OPAM inside a Cloud9 IDE workspace. Enter into the terminal in any workspace:

curl -L https://raw.github.com/mlin/c9-ocaml/master/c9-ocaml.sh | bash -ex
eval $(opam config env)

The OCaml toolchain and OPAM are then ready to go. Here's a screenshot of compiling and running 'Hello, world!':

The human population harbors 172 mutations per non-lethal genome position. What'll happen to them?

A recent Panda's Thumb post highlighted that, given the size of the human genome, the rate of de novo point mutations, and the total size of the population, every non-lethal position can be expected to vary - meaning that, for every genome position or site, there's very likely at least one person (and usually dozens or more) with a new mutation there, so long as it's non-lethal. It's a trivial calculation and, while we could refine it in various ways, the essential point is clear.

"We are all, regardless of race,
genetically 99.9% the same."
Right or wrong?

Still, let's try to understand this a bit further. First, an equally simple, entirely compatible fact which might attenuate our surprise: the existence of a couple hundred people with new mutations in a certain site leaves about seven billion without a new mutation there. Indeed, at the vast majority of sites, almost all people are homozygous for the same allele - identical by descent from the hominid lineage.

In that light, here's a deep question one can ask about all those hundreds of billions of de novo mutations: what will be their ultimate fate? Will they all shuffle through the future human population, making our genome's future evolution look like the reels on a slot machine? Or is it going to be rather more like the pitch drop experiment?