Most of us would be lost without Google maps or similar route-guidance technologies. And when those mapping tools include additional data about traffic or weather, we can navigate even more effectively. For scientists who navigate the mammalian genome to better understand genetic causes of disease, combining various types of data sets makes finding their way easier, too.
A team at the Salk Institute has developed a computational algorithm that integrates two different data types to make locating key regions within the genome more precise and accurate than other tools. The method, detailed during the week of February 13, 2017, in Proceedings of the National Academy of Sciences, could help researchers conduct vastly more targeted searches for disease-causing genetic variants in the human genome, such as ones that promote cancer or cause metabolic disorders.
"Most of the variation between individuals is in noncoding regions of the genome," says senior author Joseph Ecker, a Howard Hughes Medical Institute investigator and director of Salks Genomic Analysis Laboratory. "These regions dont code for proteins, but they still contain genetic variants that cause disease. We just havent had very effective tools to locate these areas in a variety of tissues and cell types until now."
Only about two percent of our DNA is made up of genes, which code for proteins that keep us healthy and functional. For many years, the other 98 percent was thought to be extraneous "junk." But, as science has developed ever more sophisticated tools to probe the genome, it has become clear that much of that so-called junk has vital regulatory roles. For example, sections of DNA called "enhancers" dictate where and when the gene information is read out.
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