Quantification of DNA cleavage specificity in Hi-C experiments.
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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.
DNA Restriction Enzymes
Datasets as Topic
Nucleic Acid Conformation
Reproducibility of Results
Published Version (Please cite this version)10.1093/nar/gkv820
Publication InfoMeluzzi, D; & Arya, Gaurav (2016). Quantification of DNA cleavage specificity in Hi-C experiments. Nucleic Acids Res, 44(1). pp. e4. 10.1093/nar/gkv820. Retrieved from http://hdl.handle.net/10161/15625.
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Associate Professor 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 resear