Document - ChromStruct 4: a Python code to estimate the chromatin structure from Hi-C data

2019

Journal article Open Access

ChromStruct 4: a Python code to estimate the chromatin structure from Hi-C data

Caudai C., Salerno E., Zoppè M., Merelli I., Tonazzini A.

Biotechnology Chromatin configuration Bayesian estimation Chromosome conformation capture Genetics Applied Mathematics

A method and a stand-alone Python(TM) code to estimate the 3D chromatin structure from chromosome conformation capture data are presented. The method is based on a multiresolution, modified-bead-chain chromatin model, evolved through quaternion operators in a Monte Carlo sampling. The solution space to be sampled is generated by a score function with a data-fit part and a constraint part where the available prior knowledge is implicitly coded. The final solution is a set of 3D configurations that are compatible with both the data and the prior knowledge. The iterative code, provided here as additional material, is equipped with a graphical user interface and stores its results in standard-format files for 3D visualization. We describe the mathematical-computational aspects of the method and explain the details of the code. Some experimental results are reported, with a demonstration of their fit to the data.

Source: IEEE/ACM transactions on computational biology and bioinformatics (Online) 16 (2019): 1867–1878. doi:10.1109/TCBB.2018.2838669

Publisher: IEEE Computer Society,, New York, NY , Stati Uniti d'America

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@article{oai:it.cnr:prodotti:387460,
	title = {ChromStruct 4: a Python code to estimate the chromatin structure from Hi-C data},
	author = {Caudai C. and Salerno E. and Zoppè M. and Merelli I. and Tonazzini A.},
	publisher = {IEEE Computer Society,, New York, NY , Stati Uniti d'America},
	doi = {10.1109/tcbb.2018.2838669},
	journal = {IEEE/ACM transactions on computational biology and bioinformatics (Online)},
	volume = {16},
	pages = {1867–1878},
	year = {2019}
}