{"id":25806938,"url":"https://github.com/spqb/adabmdcapy","last_synced_at":"2025-02-27T20:55:29.812Z","repository":{"id":257841821,"uuid":"865865687","full_name":"spqb/adabmDCApy","owner":"spqb","description":"Pytorch implementation of adabmDCA","archived":false,"fork":false,"pushed_at":"2025-02-18T09:49:40.000Z","size":13679,"stargazers_count":5,"open_issues_count":2,"forks_count":1,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-02-18T10:41:02.909Z","etag":null,"topics":["dca","direct-coupling-analysis"],"latest_commit_sha":null,"homepage":"https://spqb.github.io/adabmDCApy/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/spqb.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-10-01T09:13:10.000Z","updated_at":"2025-02-06T10:42:22.000Z","dependencies_parsed_at":"2024-11-25T16:28:08.366Z","dependency_job_id":"429dd01e-e08a-45c4-9549-af4a83e96036","html_url":"https://github.com/spqb/adabmDCApy","commit_stats":null,"previous_names":["spqb/adabmdcapy"],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/spqb%2FadabmDCApy","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/spqb%2FadabmDCApy/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/spqb%2FadabmDCApy/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/spqb%2FadabmDCApy/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/spqb","download_url":"https://codeload.github.com/spqb/adabmDCApy/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241059249,"owners_count":19902348,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["dca","direct-coupling-analysis"],"created_at":"2025-02-27T20:55:29.177Z","updated_at":"2025-02-27T20:55:29.805Z","avatar_url":"https://github.com/spqb.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# adabmDCA 2.0 - Direct Coupling Analysis in Python\n\n**Authors:**  \n- **Lorenzo Rosset** (Ecole Normale Supérieure ENS, Sorbonne Université)\n- **Roberto Netti** (Sorbonne Université)\n- **Anna Paola Muntoni** (Politecnico di Torino)\n- **Martin Weigt** (Sorbonne Université)\n- **Francesco Zamponi** (Sapienza Università di Roma)\n  \n**Maintainer:** Lorenzo Rosset\n\n## Overview\n\n**adabmDCA 2.0** is a flexible yet easy-to-use implementation of Direct Coupling Analysis (DCA) based on Boltzmann machine learning. This package provides tools for analyzing residue-residue contacts, predicting mutational effects, scoring sequence libraries, and generating artificial sequences, applicable to both protein and RNA families. The package is designed for flexibility and performance, supporting multiple programming languages (C++, Julia, Python) and architectures (single-core/multi-core CPUs and GPUs).  \nThis repository contains the Python GPU version of adabmDCA, maintained by **Lorenzo Rosset**.\n\nThe project's main repository can be found at [adabmDCA 2.0](https://github.com/spqb/adabmDCA.git).\n\n## Features\n\n- **Direct Coupling Analysis (DCA)** based on Boltzmann machine learning.\n- Support for **dense** and **sparse** generative DCA models.\n- Available on multiple architectures: single-core and multi-core CPUs, GPUs.\n- Ready-to-use for **residue-residue contact prediction**, **mutational-effect prediction**, and **sequence design**.\n- Compatible with protein and RNA family analysis.\n\n## Installation\n\n### Option 1: Install from PyPI\nOpen a terminal and run\n```bash\npip install adabmDCA\n```\n\n### Option 2: Install from the GitHub repository\nClone the repository locally and then install the requirements and the package. In a terminal, run:\n\n```bash\ngit clone git@github.com:spqb/adabmDCApy.git\ncd adabmDCApy\npip install .\n```\n\n## Usage\n\nAfter installation, all the main routines can be launched through the command-line interface using the command `adabmDCA`.\n\nTo get started with adabmDCA in Python, please refer to the [Documentation](https://spqb.github.io/adabmDCApy/) or the [Colab notebook](https://colab.research.google.com/drive/1l5e1W8pk4cB92JAlBElLzpkEk6Hdjk7B?usp=sharing).\n\n## License\n\nThis package is open-sourced under the MIT License.\n\n## Citation\n\nIf you use this package in your research, please cite:\n\n\u003e Rosset, L., Netti, R., Muntoni, A.P., Weigt, M., \u0026 Zamponi, F. (2024). adabmDCA 2.0: A flexible but easy-to-use package for Direct Coupling Analysis.\n\n## Acknowledgments\n\nThis work was developed in collaboration with Sorbonne Université, Sapienza Università di Roma, and Politecnico di Torino.\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fspqb%2Fadabmdcapy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fspqb%2Fadabmdcapy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fspqb%2Fadabmdcapy/lists"}