Tool:evorca: fast and minimal plmDCA in JAX

Question

[Shosuke]

evorca: fast and minimal plmDCA in JAX

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evorca is a lightweight, JAX-based implementation of plmDCA (pseudo-likelihood maximization DCA) to infer contact maps and statistical coupling matrices from multiple sequence alignments.

• Minimal & Extensible: Clear, compact Potts-model pipeline designed to be readable and easy to modify.
• Efficient & Hardware-aware: JAX + Optax enable CPU/GPU execution (GPU requires a matching JAX build); sparse I/O is intended to help with larger alignments.
• Practical Interface: Simple command-line interface and a NumPy-first Python API for downstream analysis and visualization.

Key Differentiators

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Established plmDCA tools (e.g., EVcouplings, pydca, CCMpred) are mature and widely used, but depending on environment they may involve broader dependencies or different GPU options.
evorca focuses on a smaller, modern Python/JAX stack and a concise code path—aiming to make the core plmDCA steps straightforward to read, adapt, and integrate into existing workflows.

GitHub: https://github.com/suzuki-2001/evorca