miniMDS：从高分辨率 Hi-C 数据推断 3D 结构。
miniMDS: 3D structural inference from high-resolution Hi-C data.
机构信息
Department of Biochemistry and Molecular Biology and Center for Eukaryotic Gene Regulation, The Pennsylvania State University, University Park, PA, USA.
出版信息
Bioinformatics. 2017 Jul 15;33(14):i261-i266. doi: 10.1093/bioinformatics/btx271.
MOTIVATION
Recent experiments have provided Hi-C data at resolution as high as 1 kbp. However, 3D structural inference from high-resolution Hi-C datasets is often computationally unfeasible using existing methods.
RESULTS
We have developed miniMDS, an approximation of multidimensional scaling (MDS) that partitions a Hi-C dataset, performs high-resolution MDS separately on each partition, and then reassembles the partitions using low-resolution MDS. miniMDS is faster, more accurate, and uses less memory than existing methods for inferring the human genome at high resolution (10 kbp).
AVAILABILITY AND IMPLEMENTATION
A Python implementation of miniMDS is available on GitHub: https://github.com/seqcode/miniMDS .
CONTACT
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
动机
最近的实验提供了分辨率高达 1 kbp 的 Hi-C 数据。然而，使用现有方法，从高分辨率 Hi-C 数据集进行 3D 结构推断在计算上通常是不可行的。
结果
我们开发了 miniMDS，它是多维尺度（MDS）的一种近似，可对 Hi-C 数据集进行分区，在每个分区上分别进行高分辨率 MDS，然后使用低分辨率 MDS 重新组装分区。与现有方法相比，miniMDS 用于推断人类基因组的高分辨率（10 kbp）时速度更快、更准确且使用的内存更少。
可用性和实现
miniMDS 的 Python 实现可在 GitHub 上获得：https://github.com/seqcode/miniMDS。
联系方式
补充信息
补充数据可在“Bioinformatics”在线获得。
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