Until recently, the wet lab has been a crucial component of every biologist. Today's advances in the production of massive amounts of data and the creation of machine-learning algorithms for processing that data are changing the face of biological science—making it possible to do real science without a wet lab. David Heckerman shares several examples of how this transformation in the area of genomics is changing the pace of scientific breakthroughs.
Today, genetics is the leading source of scientific promise. Since James D. Watson and Francis Crick uncovered DNA’s structure in 1953, a massive amount of available genetic data has been identified, and novel forms of scientific organization and modes of working have emerged. As a result, genetics has brought science to the brink of a new era of enlightenment, in which individuals are understood in terms of the relationships among their unique genomic data.
This movement – the latest incarnation of the endless quest for human advancement – poses new challenges to the relationship between science and society. As the American Museum of Natural History provocatively asked at the 2001 opening of its genetics exhibition: “The genomic revolution is here – are you ready?”
Our abilities in sequence-reading have been improving along an exponential curve since pretty much the day we published the molecule’s structure, and in that time we’ve made our reading about a hundred thousand times more efficient. It used to cost about $0.01 to sequence a base pair; now it costs about $0.0000001. Synthesis, on the other hand, used to cost about $3 per base pair; now it costs roughly $0.50. This week, though, a talk by Cambrian Genomics at a DARPA-funded event brought widespread attention to a technology that promises to make DNA synthesis thousands of times cheaper, potentially offering the first real price drop the process has ever seen.
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