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bioRxiv · 10.1101/2022.05.06.490994

PHi-C2: interpreting Hi-C data as the dynamic 3D genome state

Shinkai, S.·Itoga, H.·Kyoda, K.·Onami, S.
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Abstract

Hi-C is a widely used assay for studying three-dimensional (3D) genome organization across the whole genome. Here, we present PHi-C2, a Python package supported by mathematical and biophysical polymer modeling, that converts an input Hi-C matrix data into the polymer models dynamics, structural conformations, and rheological features. The updated optimization algorithm to regenerate a highly similar Hi-C matrix provides a fast and accurate optimal solution compared to the previous version by eliminating a computational bottleneck in the iterative optimization process. Besides, we newly set up the availability on Google Colab workflow to run, easily change parameters and check the results in the notebook. Overall, PHi-C2 can be a valuable tool to mine the dynamic 3D genome state embedded in Hi-C data. Availability and ImplementationPHi-C2 as the phic Python package is freely available under the GPL license and can be installed from the Python package index. The source code is available from GitHub at https://github.com/soyashinkai/PHi-C2. Without preparing a Python environment, PHi-C2 can run on Google Colab (https://bit.ly/3rlptGI). Contactsoya.shinkai@riken.jp or sonami@riken.jp

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Shinkai, S., Itoga, H., Kyoda, K., Onami, S.. 2022-05-08. PHi-C2: interpreting Hi-C data as the dynamic 3D genome state. https://doi.org/10.1101/2022.05.06.490994

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