It’s often difficult to see a phenomenon when you are in the middle of it. Distance may be necessary to have the perspective to take in the entire thing. This can be true of physical manifestations, such as weather patterns, but also of events that take place over time. In medicine there are many “revolutions” that play out over decades, and it can be hard to see where they are going when we are in the middle of them. In the 1990s it seemed as if we might be in the middle of a gene therapy revolution, but it turns out that was probably three decades premature. It seemed that anti-oxidant therapy was going to transform the management of neurodegenerative diseases, but they didn’t. A decade later everyone thought we were at the beginning of a stem-cell revolution – it turns out, not so much. These technologies are all important, but they did not produce the transformational change that was predicted (at least, not yet). We were, however, inside an information revolution, that has completely transformed how we access and communicate information.
Right now it certainly seems as if we are in the middle of a revolution in genetic research and medicine. While it didn’t start with the Human Genome Project (completed in 2003), that was a milestone that triggered much of the public awareness of just how fast genetic technology is advancing. More recently the advent of CRISPR technology has renewed the promise of a genetic revolution. CRISPR is definitely a game-changer, but is embedded in steady progress in the ability to research, understand, modify, replace, and engineer genes. CRISPR technology is already showing promise from treating cancer to curing genetic diseases, like sickle cell anemia. Working within medical academia, the number of lectures I am exposed to in which CRISPR comes up as a potential therapeutic or diagnostic tool has just exploded.
The genetics revolution is partly as dramatic as it is because it is feeding off a parallel technological revolution – digital technology. Genes, in fact, are a form of digital technology, and so it meshes well with computer technology and directly benefits from it. In many ways Moore’s law has bled over to genetic technology, leading to geometric, not linear, advances in technology.
A recent software advance is a great example of the power of this relationship between genes and computers. Here is the summary:
Here, we define an algorithmic approach, mdBG, that makes use of minimizer-space de Bruijn graphs to enable long-read genome assembly. mdBG achieves orders-of-magnitude improvement in both speed and memory usage over existing methods without compromising accuracy. A human genome is assembled in under 10 min using 8 cores and 10 GB RAM, and 60 Gbp of metagenome reads are assembled in 4 min using 1 GB RAM.
What this functionally means is powerful desktop genetics. How much of an advance is this?
Their software performed genome assembly for the HiFi human data 81 times faster with 18 times less memory usage than the Peregrine assembler and 338 times faster with 19 times less memory usage than the hifiasm assembler.
A computer with 8 cores and 10GB of RAM is a typical desktop computer. I have more power in the computer I am using to write this blog post. To illustrate the power of this approach the authors then did this:
In addition, we constructed a minimizer-space de Bruijn graph-based representation of 661,405 bacterial genomes, comprising 16 million nodes and 45 million edges, and successfully search it for anti-microbial resistance (AMR) genes in 12 min.
This is just one example of the kinds of advances that are being made today in the field of genetics. This one seems worth highlighting for the same reason that CRISPR became such a sensation – because it was such an advance in the speed, ease, and cost of genetic alteration it brought the technology to the point that suddenly almost any lab in the world could do this research. This was a clear tipping point in genetics research. What previously required expensive lab equipment, high levels of skill, and months could now be accomplished cheaply in days.
This new method of assembling genomes seems to be a similar leap in power, lowering the barrier to this kind of research so that any researcher can do meaningful genetics research on their laptop over lunch. Over 600 thousand bacterial genomes were searched for genes for antibiotic resistance in 12 minutes, with off-the-shelf computer technology. The researchers are also making the code for their assembler free for other researchers to download. This open source approach will allow other researcher to tweak the code and improve it further. We can combine all this with the rapid advancements in artificial intelligence technology, which can automate searching through massive amounts of data for meaningful patterns.
I have been following this story closely for the last few years because we seem to be at a confluence of technologies that is transforming genetic science before our eyes. Genetics is particularly well-suited to digital technology, and now we have accessible and powerful tools to examine genes, sequence them, assemble entire genomes or even pangenomes (covering all species within a group), and make targeted alterations. Researchers can cheaply accomplish in a day what would have taken hundreds of years and billions of dollars a few decades ago. This is not true of every technology, but it is of genetics research.
