Steinbuch Centre for Computing, Eggenstein-Leopoldshafen 76344.
Department of Physics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen 76344.
Bioinformatics. 2020 Apr 1;36(7):2264-2265. doi: 10.1093/bioinformatics/btz892.
The ongoing advances in sequencing technologies have provided a massive increase in the availability of sequence data. This made it possible to study the patterns of correlated substitution between residues in families of homologous proteins or RNAs and to retrieve structural and stability information. Direct coupling analysis (DCA) infers coevolutionary couplings between pairs of residues indicating their spatial proximity, making such information a valuable input for subsequent structure prediction.
Here, we present pydca, a standalone Python-based software package for the DCA of protein- and RNA-homologous families. It is based on two popular inverse statistical approaches, namely, the mean-field and the pseudo-likelihood maximization and is equipped with a series of functionalities that range from multiple sequence alignment trimming to contact map visualization. Thanks to its efficient implementation, features and user-friendly command line interface, pydca is a modular and easy-to-use tool that can be used by researchers with a wide range of backgrounds.
pydca can be obtained from https://github.com/KIT-MBS/pydca or from the Python Package Index under the MIT License.
Supplementary data are available at Bioinformatics online.
测序技术的不断进步提供了大量可用的序列数据。这使得研究同源蛋白或 RNA 家族中残基之间相关替换的模式以及检索结构和稳定性信息成为可能。直接耦合分析（DCA）推断出残基对之间的共进化耦合，表明它们的空间接近性，从而使这些信息成为后续结构预测的有价值输入。
在这里，我们提出了 pydca，这是一个基于 Python 的独立软件包，用于 DCA 的蛋白质和 RNA 同源家族。它基于两种流行的逆统计方法，即平均场和伪似然最大化，并配备了一系列功能，从多重序列比对修剪到接触图可视化。由于其高效的实现、功能和用户友好的命令行界面，pydca 是一个模块化的、易于使用的工具，可以供具有广泛背景的研究人员使用。
pydca 可从 https://github.com/KIT-MBS/pydca 或 Python 包索引获得，许可证为 MIT 许可证。
补充数据可在 Bioinformatics 在线获得。
