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3-D maps reveal genes fold like 'origami'
A central goal of the five-year project was to identify the loops in the human genome. The research was carried out at Baylor College of Medicine, Rice University, the Broad Institute, and Harvard University.
The research appears online in Cell.
Researchers used a technology called the"Hi-C" method to collect billions of snips of DNA. These were later analyzed for signs of loops. The team found that loops, and other genome folding patterns, are an essential part of gene regulation. Researchers sifted through billions of DNA pairs to catalog 10,000 loops.
Senior author Erez Lieberman Aiden, assistant professor of genetics at Baylor and of computer science and computational and applied mathematics at Rice, said work began five years ago, shortly after he and his colleagues at the Broad Institute published a groundbreaking study introducing the Hi-C 3-D method for sequencing genomes.
The work to refine "Hi-C" and produce full-genome maps with gene-level resolution continued when Aiden moved to Houston in 2013, established the Center for Genome Architecture at Baylor and joined the Center for Theoretical Biological Physics at Rice. Aiden credited Rao and Huntley with leading the effort, which involved a team of 11 researchers at Rice, Baylor, Broad and Harvard.
Identifying the loops themselves was yet another challenge. Fortunately, the group benefited from resources provided by NVIDIA, which named Aiden's lab a GPU Research Center in 2013 and provided essential hardware for the project. Huntley said new methods were also developed to speed the data processing and reduce experimental "noise," irregular fluctuations that tend to obscure weak signals in the data.
Huntley: "We faced a real challenge because we were asking, 'How do each of the millions of pieces of DNA in the database interact with each of the other millions of pieces?' Most of the tools that we used for this paper we had to create from scratch because the scale at which these experiments are performed is so unusual."
The big-data tools created for the study included parallelized pipelines for high-performance computer clusters, dynamic programming algorithms and custom data structures.
Rao said the group also relied heavily on data-visualization tools created by co-authors Neva Durand and James Robinson.
Rao: "When studying big data, there can be a tendency to try to solve problems by relying purely on statistical analyses to see what comes out, but our group has a different mentality. Even though there was so much data, we still wanted to be able to look at it, visualize it and make sense of it. I would say that almost every phenomenon we observed was first seen with the naked eye."
Additional co-authors include the Broad Institute's Eric Lander and Baylor's Elena Stamenova, Ivan Bochkov, Adrian Sanborn, Ido Machol and Arina Omer.
The research was supported by the National Science Foundation, the National Institutes of Health, the National Human Genome Research Institute, NVIDIA, IBM, Google, the Cancer Prevention and Research Institute of Texas and the McNair Medical Institute.