Quantification of DNA cleavage specificity in Hi-C experiments.

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2016-01-08

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Abstract

Hi-C experiments produce large numbers of DNA sequence read pairs that are typically analyzed to deduce genomewide interactions between arbitrary loci. A key step in these experiments is the cleavage of cross-linked chromatin with a restriction endonuclease. Although this cleavage should happen specifically at the enzyme's recognition sequence, an unknown proportion of cleavage events may involve other sequences, owing to the enzyme's star activity or to random DNA breakage. A quantitative estimation of these non-specific cleavages may enable simulating realistic Hi-C read pairs for validation of downstream analyses, monitoring the reproducibility of experimental conditions and investigating biophysical properties that correlate with DNA cleavage patterns. Here we describe a computational method for analyzing Hi-C read pairs to estimate the fractions of cleavages at different possible targets. The method relies on expressing an observed local target distribution downstream of aligned reads as a linear combination of known conditional local target distributions. We validated this method using Hi-C read pairs obtained by computer simulation. Application of the method to experimental Hi-C datasets from murine cells revealed interesting similarities and differences in patterns of cleavage across the various experiments considered.

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10.1093/nar/gkv820

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Meluzzi, Dario, and Gaurav Arya (2016). Quantification of DNA cleavage specificity in Hi-C experiments. Nucleic Acids Res, 44(1). p. e4. 10.1093/nar/gkv820 Retrieved from https://hdl.handle.net/10161/15625.

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Scholars@Duke

Arya

Gaurav Arya

Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science

My research laboratory uses physics-based computational tools to provide fundamental, molecular-level understanding of a diverse range of biological and soft-material systems, with the aim of discovering new phenomena and developing new technologies. The methods we use or develop are largely based on statistical mechanics, molecular modeling and simulations, stochastic dynamics, coarse-graining, bioinformatics, machine learning, and polymer/colloidal physics. Our current research interests fall within four main themes: genome organization and regulation; polymer-nanoparticle composites; viral-DNA-packaging; and DNA nanotechnology. Please visit our website for more details about each of these research projects.


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