Laboratory for Developmental Dynamics, RIKEN Center for Biosystems Dynamics Research, Kobe 650-0047, Japan.
Life Science Data Sharing Unit, Infrastructure Research and Development Division, RIKEN Information R&D and Strategy Headquarters, Kobe 650-0047, Japan.
Bioinformatics. 2022 Oct 31;38(21):4984-4986. doi: 10.1093/bioinformatics/btac613.
High-throughput chromosome conformation capture (Hi-C) is a widely used assay for studying the 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 input Hi-C matrix data into the polymer model's dynamics, structural conformations and rheological features. The updated optimization algorithm for regenerating a highly similar Hi-C matrix provides a fast and accurate optimal solution compared to the previous version by eliminating the factors underlying the inefficiency of the optimization algorithm in the iterative optimization process. In addition, we have enabled a Google Colab workflow to run the algorithm, wherein users can easily change the parameters and check the results in the notebook. Overall, PHi-C2 represents a valuable tool for mining the dynamic 3D genome state embedded in Hi-C data.
PHi-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. Moreover, users do not have to prepare a Python environment because PHi-C2 can run on Google Colab (https://bit.ly/3rlptGI).
Supplementary data are available at Bioinformatics online.
高通量染色体构象捕获（Hi-C）是一种广泛用于研究整个基因组中三维（3D）基因组组织的检测方法。在这里，我们展示了 PHi-C2，这是一个支持数学和生物物理聚合物建模的 Python 包，它将输入的 Hi-C 矩阵数据转换为聚合物模型的动力学、结构构象和流变学特征。与之前的版本相比，更新的用于生成高度相似的 Hi-C 矩阵的优化算法通过消除迭代优化过程中优化算法效率低下的因素，提供了更快、更准确的最优解决方案。此外，我们已经启用了 Google Colab 工作流程来运行该算法，用户可以在笔记本中轻松更改参数并检查结果。总的来说，PHi-C2 是挖掘嵌入在 Hi-C 数据中的动态 3D 基因组状态的有价值的工具。
PHi-C2 作为 phic Python 包，根据 GPL 许可证免费提供，并可以从 Python 包索引中安装。源代码可在 GitHub 上获取（网址：https://github.com/soyashinkai/PHi-C2）。此外，用户不必准备 Python 环境，因为 PHi-C2 可以在 Google Colab 上运行（网址：https://bit.ly/3rlptGI）。
补充数据可在生物信息学在线获取。
