In silico prediction of high-resolution Hi-C interaction matrices (part II)

The uploaded files are source datasets for the HiC-Reg approach. HiC-Reg is a regression based method that predict contact counts from one-dimensional regulatory signals such as epigenetic marks and regulatory protein binding. See more details here (https://github.com/Roy-lab/HiC-Reg). There are a total of six files in this dataset: Data.tgz, Gm12878.tgz, Hmec.tgz, K562.tgz, Huvec.tgz and Nhek.tgz. The Data.tgz include predictions and other downstream analysis such as feature importance analysis, significant interaction calling, and data files for select figures. The Gm12878.tgz, K562.tgz, Huvec.tgz, Hmec.tgz and Nhek.tgz contain trained models, predictions, feature files for two chromosomes for in each cell line. This is part II of the dataset which contains K562.tgz and Huvec.tgz.

This work is supported by the National Institutes of Health (NIH), BD2K grant U54 AI117924 and NIH R01-HG010045-01.

Related Organizations

University of Wisconsin–Oshkosh
United States
Wisconsin Institutes for Discovery
United States

Keywords

machine learning, 3D genome configuration, Hi-C, genomics, gene regulation

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