Johns Hopkins researchers have succeeded in teaching computers how to identify commonalities in DNA sequences known to regulate gene activity, and to then use those commonalities to predict other regulatory regions throughout the genome. The tool is expected to help scientists better understand disease risk and cell development.
The work was reported in two recent papers in Genome Research, published online on July 3 and Sept. 27.
"Our goal is to understand how regulatory information is encrypted and to learn which sequence variations contribute to medical risks," says Andrew McCallion, Ph.D., associate professor of molecular and comparative pathobiology in the McKusick-Nathans Institute of Genetic Medicine at Hopkins. "We give data to a computer and 'teach it' to distinguish between data that has no biological value versus data that has this or that biological value. It then establishes a set of rules, which allows it to look at new sets of data and apply what it learned. We're basically sending our computers to school."
These state-of-the-art "machine learning" techniques were developed by Michael Beer, Ph.D., assistant professor of biomedical engineering at the Johns Hopkins School of Medicine, and by Ivan Ovcharenko, Ph.D., at the National Center for Biotechnology Information. The researchers began both studies by creating "training sets" for their computers to "learn" from. These training sets were lists of DNA sequences taken from regions of the genome, called enhancers, that are known to increase the activity of particular genes in particular cells.
For the first of their studies, McCallion's team created a training set of enhancer sequences specific to a particular region of the brain by compiling a list of 211 published sequences that had been shown, by various studies in mice and zebrafish, to be active in the development or function of that part of the brain.
For a second study, the team gen
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Johns Hopkins Medicine