Collaboration is, unfortunately, not as common as hoped for in the scientific community. Some researchers are quick to point out the detrimental effects, such as the difficulty young authors face in highlighting their work when they are four pages down on the author list, or the funding eligibility issues that may arise in such a partnership. The increase of scientific capability in non-traditional powerhouse countries, like China and Russia, raises additional concerns when it comes to data sharing. But like most things, there is a time and place for collaboration—and that’s in genomics.
We’ve lived in an “omics” age ever since the Human Genome Project (HGP) yielded the first human sequencing data more than a decade ago. In 2004, the last piece of the HGP puzzle was released, and that’s when our scientific landscape started shifting. With the advent of increasingly powerful computers, better software and enhanced methods, the “omics age” has given way to what is now being called the “information age” of genetics.
In all its glory and accomplishment, the HGP yielded more data at one time than the scientific community had ever seen. Here we are more than 10 years later and we have barely sifted through 1% of it. That’s not to take away from the fact that projects that used to take years to complete now take months or even weeks, and a considerable number of studies have benefited greatly from the sequencing—but there is still a lot of work to be done. This is where collaboration comes in. The wealth of data is just too large for one field of scientists, one academic department or even one university/institution.
Stanford Univ., Stanford, Calif., has long been recognized for its culture of interdisciplinary collaboration, as well as its status as a forerunner in computational genomics. In fact, earlier this year, a joint effort between Stanford and the J. Craig Venter Institute yielded the first software simulation of an entire organism. The smallest free-living organism, Mycoplasma genitalium, has 525 genes, which are now fully mapped, thanks to data from more than 900 scientific papers and a newly developed software model.
The school took another step in the right direction this month when it launched its new research center, the Stanford Center for Computational, Evolutionary and Human Genomics. The center is an effort to harness the vast amounts of genetic data that can, and will, benefit human well-being. The center plans to attract faculty and students from Stanford's seven schools (humanities and sciences, law, medicine, business, earth sciences, engineering and education) to engage in interdisciplinary collaborations that will catalyze discovery in emerging fields of genetic research. Expanding interaction between the schools, especially humanities and social sciences, was a key motivation in the center’s development.
"We really can't make effective interpretations unless we take the history of human behavior into account," says Center Co-Director Marcus Feldman, a biology professor. "That's why it's not just mathematicians and geneticists working together at the center, but also archaeologists, anthropologists and historians."
Examples of research in the new center include disease risk factors and genomic variations in crops. Researchers will use computational analysis to gain a better understanding of the interactions of genes with the goal of better understanding risk factors for common diseases, like coronary heart disease.
Other researchers will turn to “big data” to analyze how climate change could affect genomic variation in crops. This information could lead to “designer crops”—or crops developed using naturally occurring genetic variations to make them less susceptible to atmospheric changes. This, in turn, has a variety of implications, ranging from silencing the controversy on genetically modified organisms to finding a successful biofuel alternative.
In another decade, I think we will start to see some truly amazing, human-implication findings come out of Stanford’s center, as well as many others like it, thanks to the wealth of HGP data and the computational advances needed to understand it. We will then be confronted with yet another shift in the scientific landscape—this one away from the information age and on to bigger and better things that are a mere dream today.