cudaMMC：GPU 增强的多尺度蒙特卡罗染色质 3D 建模。
cudaMMC: GPU-enhanced multiscale Monte Carlo chromatin 3D modelling.
机构信息
Laboratory of Bioinformatics and Computational Genomics, Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw 00-662, Poland.
Laboratory of Functional and Structural Genomics, Centre of New Technologies, University of Warsaw, Warsaw 02-097, Poland.
出版信息
Bioinformatics. 2023 Oct 3;39(10). doi: 10.1093/bioinformatics/btad588.
MOTIVATION
Investigating the 3D structure of chromatin provides new insights into transcriptional regulation. With the evolution of 3C next-generation sequencing methods like ChiA-PET and Hi-C, the surge in data volume has highlighted the need for more efficient chromatin spatial modelling algorithms. This study introduces the cudaMMC method, based on the Simulated Annealing Monte Carlo approach and enhanced by GPU-accelerated computing, to efficiently generate ensembles of chromatin 3D structures.
RESULTS
The cudaMMC calculations demonstrate significantly faster performance with better stability compared to our previous method on the same workstation. cudaMMC also substantially reduces the computation time required for generating ensembles of large chromatin models, making it an invaluable tool for studying chromatin spatial conformation.
AVAILABILITY AND IMPLEMENTATION
Open-source software and manual and sample data are freely available on https://github.com/SFGLab/cudaMMC.
动机
研究染色质的 3D 结构为转录调控提供了新的见解。随着 ChiA-PET 和 Hi-C 等 3C 新一代测序方法的发展，数据量的激增凸显了对更高效的染色质空间建模算法的需求。本研究引入了 cudaMMC 方法，该方法基于模拟退火蒙特卡罗方法，并通过 GPU 加速计算得到增强，可有效地生成染色质 3D 结构的集合。
结果
与我们之前在同一工作站上的方法相比，cudaMMC 的计算速度更快，稳定性更好。cudaMMC 还大大减少了生成大型染色质模型集合所需的计算时间，使其成为研究染色质空间构象的宝贵工具。
可用性和实现
可在 https://github.com/SFGLab/cudaMMC 上免费获得开源软件以及手册和示例数据。
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