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A Computer Scientist Takes on Transcription

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  • Title: Assistant Professor, Department of Computer Science, University of Maryland
  • Education: PhD, Princeton University, 2005
  • Recommended by: Steven Salzberg

Carl Kingsford has one foot planted firmly in the analysis of biological networks and the other in bacterial and viral genome organization and evolution. For biological network research, Kingsford is working on addressing the problem of obtaining protein-protein interaction data from cells in a high-throughput method. He says the challenge is that people want to be able to analyze these large networks to figure out how they're organized and how they can be used to learn what the proteins are doing in the cell. To this end, he is focusing on how to use mathematical programming to look at these networks and predict protein function.

And on the bacterial genome analysis side, he is developing software to look at bacteria and the influenza virus. There are various projects under that umbrella, one of which is a freely available program he and his colleagues developed called TransTermHP. This tool enables researchers to predict particular sequences of transcription terminators in bacteria. "Basically it's a fundamental feature of the organization of the bacterial genomes that divide up the genome into genes that get transcribed at the same time. … This is a computational way of finding those signals," Kingsford says. "The bacterial and viral genome is a grab bag of a few different things, but that's the main one that we've done."

Although the biological network research is a relatively new area for Kingsford, the genome organization research is a direct result of his earlier training as a computer scientist. In his graduate studies, he was mostly focused on computational biology and protein structure prediction that honed his algorithm and coding skills. This is the same skill set he uses to address the challenge of extremely noisy data from biological network studies. "It's a technical issue [that] we're thinking about mostly, and it's how we can deal with this extremely useful, but also very noisy, data," he says. "And that's where computer scientists can excel, because we've developed a lot of methods for dealing with uncertainty of data sets over the years."

Kingsford credits his PhD advisor Mona Singh, a professor of computer science at Princeton University, for teaching him the value of due diligence in research and in putting papers together. "She has a very high standard for something being publishable, and that's something that has influenced me greatly," he says. "You don't publish the smallest publishable unit. You work through all the possible bugs and different ways of looking at something until you're really sure you understand it." He also credits Steven Salzberg with encouraging him to keep a focus on practical, high-impact tools that are really useful for biologists looking to solve specific problems.

One thing that would help his studies would be technology that would allow for high-throughput, low-noise assays capable of identifying two proteins and tracking when and where they interact. "That would make a lot of the questions we try to answer about the evolution and organization of biological networks easier to answer," he says, "but it would take some of the fun out it because it would be like giving you the answer."

Publications of note

In a paper published in Genome Biology ("Rapid, accurate, computational discovery of Rho-independent transcription terminators illuminates their relationship to DNA uptake"), Kingsford and his colleagues describe TransTermHP and its ability to detect Rho-independent transcription terminators. Using the program, the team predicted the locations of terminators in 343 prokaryotic genomes, representing the largest collection of predictions available.

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