In 2001, the Human Genome Project and Celera Genomics announced that after 10 years of work at a cost of some $400 million, they had completed a draft sequence of the human genome. Today, sequencing a human genome is something that a single researcher can do in a couple of weeks for less than $10,000.
Since 2002, the rate at which genomes can be sequenced has been doubling every four months or so, whereas computing power doubles only every 18 months. Without the advent of new analytic tools, biologists' ability to generate genomic data will soon outstrip their ability to do anything useful with it.
In the latest issue of Nature Biotechnology, MIT and Harvard University researchers describe a new algorithm that drastically reduces the time it takes to find a particular gene sequence in a database of genomes. Moreover, the more genomes it's searching, the greater the speedup it affords, so its advantages will only compound as more data is generated.
In some sense, this is a data-compression algorithm - like the one that allows computer users to compress data files into smaller zip files. "You have all this data, and clearly, if you want to store it, what people would naturally do is compress it," says Bonnie Berger, a professor of applied math and computer science at MIT and senior author on the paper. "The problem is that eventually you have to look at it, so you have to decompress it to look at it. But our insight is that if you compress the data in the right way, then you can do your analysis directly on the compressed data. And that increases the speed while maintaining the accuracy of the analyses."
Exploiting redundancy
The researchers' compression scheme exploits the fact that evolution is stingy with good designs. There's a great deal of overlap in the genomes of closely related species, and some overlap even in the genomes of distantly related species: That's why experiments performed on yeast cells can tell us something about human drug reactions.
Berger; her former grad student Michael Baym PhD '09, who's now a visiting scholar in the MIT math department and a postdoc in systems biology at Harvard Medical School; and her current grad student Po-Ru Loh developed a way to mathematically represent the genomes of different species - or of different individuals within a species - such that the overlapping data is stored only once. A search of multiple genomes can thus concentrate on their differences, saving time.
"If I want to run a computation on my genome, it takes a certain amount of time," Baym explains. "If I then want to run the same computation on your genome, the fact that we're so similar means that I've already done most of the work."