Steinbuch Centre for Computing, Karlsruhe Institute of Technology (KIT), Eggenstein-Leopoldshafen, Germany.
Department of Computer Science, Humbold-Universität zu Berlin, Berlin, Germany.
Bioinformatics. 2019 Dec 15;35(24):5337-5338. doi: 10.1093/bioinformatics/btz578.
The distance geometry problem is often encountered in molecular biology and the life sciences at large, as a host of experimental methods produce ambiguous and noisy distance data. In this note, we present diSTruct; an adaptation of the generic MaxEnt-Stress graph drawing algorithm to the domain of biological macromolecules. diSTruct is fast, provides reliable structural models even from incomplete or noisy distance data and integrates access to graph analysis tools.
diSTruct is written in C++, Cython and Python 3. It is available from https://github.com/KIT-MBS/distruct.git or in the Python package index under the MIT license.
Supplementary data are available at Bioinformatics online.
距离几何问题在分子生物学和生命科学中经常遇到，因为许多实验方法都会产生模糊和嘈杂的距离数据。在本说明中，我们提出了 diSTruct；一种通用的最大熵-应力图绘制算法在生物大分子领域的应用。diSTruct 速度很快，即使从不完整或嘈杂的距离数据中也能提供可靠的结构模型，并集成了对图分析工具的访问。
diSTruct 是用 C++、Cython 和 Python 3 编写的。它可以从 https://github.com/KIT-MBS/distruct.git 获得，也可以在 Python 包索引中以 MIT 许可证获得。
补充数据可在 Bioinformatics 在线获得。
