Practical Pattern Matching

No new genes? At the University of Toronto, Brendan Frey is leading a group of scientists who are using AI techniques to analyze molecularbiology data. One of their projects involves using a factor graph they developed called GenRate to discover and evaluate genes in mouse tissues. Factor graphs let researchers describe a system with complex variables, such as gene location in DNA as well as gene length and function. “What a factor graph is useful for,” says Frey, “is describing a scoring function that tells you how good each setting of the variables is.” Using samples from over 1 million probes along DNA in 37 different mouse tissues, the scientists used their factor graph to determine which bits of DNA are expressed, or activated to read protein. In some tissues, the DNA is expressed; in others, it might not be. DNA parts that have no function are never activated. In the factor graph, each variable is a node. The scoring function comprises many local scoring functions that look for a small number of variables. For that small set of variables, it finds a score for each configuration of those variables. The local scores’ sum is the total score. “It’s a nice way to decompose a very complex problem into a whole bunch of simpler problems,” Frey says. The scientists then compare the factor graph data to known gene patterns. Because the factor graph provides a computational framework for vetting the best configuration of variables as well as discovering them, the team came up with surprising results that led to a major revision of the view of the mammalian genome. Although some research claims many genes are left to discover, Frey’s team has shown that might not be true. “Beyond the genes we found,” Frey says, “we don’t believe there exists many new protein-coding genes.”