adabmDCA 2.0 -- a flexible but easy-to-use package for Direct Coupling Analysis
This provides a practical tool for researchers in computational biology to perform DCA tasks more efficiently, though it is incremental as it builds on existing methods.
The authors introduced adabmDCA 2.0, a flexible and easy-to-use package for Direct Coupling Analysis based on Boltzmann machine learning, which supports multiple programming languages and architectures and addresses tasks like contact prediction and sequence design for protein and RNA data.
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.