adabmDCA 2.0 提供易用的直接耦合分析工具，支持蛋白与RNA序列分析。
adabmDCA 2.0 -- a flexible but easy-to-use package for Direct Coupling Analysis
- 基于玻尔兹曼机学习的灵活DCA实现，多语言多架构支持
- 可直接用于残基接触预测、突变效应评估等下游任务
- 适合生物序列分析研究者快速上手使用
本文介绍了一种基于玻尔兹曼机学习的直接耦合分析（DCA）灵活实现方法，配套提供使用教程。该工具包 adabmDCA 2.0 支持 C++、Julia、Python 多种编程语言，在单核、多核 CPU 和 GPU 架构上均可运行，拥有统一前端接口。除密集与稀疏生成型DCA模型的多种学习策略外，还能直接应用于残基-残基接触预测、突变效应预测、序列文库评分及人工序列生成等常见下游任务。适用于蛋白与RNA序列数据，具备良好的实用性与扩展性。
原文摘要 · Abstract (English)
In this methods article, we provide a flexible but easy-to-use implementation of Direct Coupling Analysis (DCA) based on Boltzmann machine learning, together with a tutorial on how to use it. The package \texttt{adabmDCA 2.0} is available in different programming languages (C++, Julia, Python) usable on different architectures (single-core and multi-core CPU, GPU) using a common front-end interface. In addition to several learning protocols for dense and sparse generative DCA models, it allows to directly address common downstream tasks like residue-residue contact prediction, mutational-effect prediction, scoring of sequence libraries and generation of artificial sequences for sequence design. It is readily applicable to protein and RNA sequence data.
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