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Researchers at UMN and BioNano Genomics improving genome mapping

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Posted January 7, 2016

If you already have the sequenced map of an organism’s genome but want to look for structural oddities in a sample, you can check the genomic barcode – a series of distances between known, targeted sites – by cutting a DNA sequence at those sites and examining the distance between the cuts. However, if the original map – obtained through next-generation sequencing involving PCR – contains any amplification biases, there is room for systematic error across studies. To remedy this, researchers at the University of Minnesota and BioNano Genomics have improved a nanochannel-based form of mapping by using dynamic time-series data to measure the probability distribution, or how much genetic material separates two labels, based on whether the strands are stretched or compressed.

DNA molecules extended in nano channels. The backbone has been stained in green, and the sequence-specific sites have been labeled in red. Image credit: Julian Sheats, UMN

DNA molecules extended in nano channels. The backbone has been stained in green, and the sequence-specific sites have been labeled in red. Image credit: Julian Sheats, UMN

“Imagine that two labels on the DNA backbone are connected together by a spring that models the configurational entropy of the DNA between them,” said Kevin Dorfman, a professor in the University of Minnesota’s College of Science & Engineering. “If this was a harmonic spring … then we would expect to see an equal probability of positive and negative displacements about the rest of the length of the spring.”

Rather than this normal curve, however, Dorfman and his colleagues observed greater compression than extension between the labels, and found that the the majority of thermal fluctuations between the labels are short-lived events – information that could help improve the accuracy of genome mapping. “Such improvements are especially important for complicated samples like cancer, where the cells are heterogeneous, so we need high accuracy to find rare events,” Dorfman said.

Source: NSFUniversity of Minnesota

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