{"id":25557784,"url":"https://github.com/spqb/adabmdca","last_synced_at":"2025-04-12T02:36:36.780Z","repository":{"id":257847733,"uuid":"865182055","full_name":"spqb/adabmDCA","owner":"spqb","description":"adabmDCA 2.0 – a flexible but easy-to-use package for Direct Coupling Analysis","archived":false,"fork":false,"pushed_at":"2025-01-23T15:31:05.000Z","size":27,"stargazers_count":6,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-04-12T02:36:35.419Z","etag":null,"topics":["bioinformatics","cpp","dca","direct-coupling-analysis","generative-ai","julia","python"],"latest_commit_sha":null,"homepage":"https://spqb.github.io/adabmDCApy/","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","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,"zenodo":null}},"created_at":"2024-09-30T05:53:35.000Z","updated_at":"2025-02-23T18:57:32.000Z","dependencies_parsed_at":"2024-10-16T18:26:21.718Z","dependency_job_id":"8b7fa4a4-b3f2-4aa7-91a8-64c873d9680a","html_url":"https://github.com/spqb/adabmDCA","commit_stats":null,"previous_names":["spqb/adabmdca"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/spqb%2FadabmDCA","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/spqb%2FadabmDCA/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/spqb%2FadabmDCA/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/spqb%2FadabmDCA/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/spqb","download_url":"https://codeload.github.com/spqb/adabmDCA/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248506993,"owners_count":21115519,"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":["bioinformatics","cpp","dca","direct-coupling-analysis","generative-ai","julia","python"],"created_at":"2025-02-20T15:19:09.570Z","updated_at":"2025-04-12T02:36:36.763Z","avatar_url":"https://github.com/spqb.png","language":null,"readme":"# adabmDCA 2.0\n`adabmDCA 2.0` – a flexible but easy-to-use package for Direct Coupling Analysis.\n\n## Installation\n\n`adabmDCA` is available in three languages: C++ (single-core CPU), Julia (multi-core CPU) and Python (GPU-oriented). Follow the instructions for installing the desired implementation.\n\n### Python implementation :snake:\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 https://github.com/spqb/adabmDCApy.git\ncd adabmDCApy\npip install .\n```\n\nThe main repository of the implementation can be found at [adabmDCApy](https://github.com/spqb/adabmDCApy.git).\n\n### Julia implementation\nAfter installing [Julia](https://julialang.org/downloads/) on your system, you can install the package in one of the following ways:\n\n#### Option 1: Using bash command\nOpen a terminal in the desired folder, and run the following commands:\n\n```{bash}\n# Download scripts from Github\nwget -O adabmDCA.sh https://raw.githubusercontent.com/spqb/adabmDCA.jl/refs/heads/main/adabmDCA.sh\nwget -O execute.jl https://raw.githubusercontent.com/spqb/adabmDCA.jl/refs/heads/main/execute.jl\nchmod +x adabmDCA.sh\n\n# Install ArgParse and adabmDCA.jl from the GitHub repo\njulia --eval 'using Pkg; Pkg.add(\"ArgParse\"); Pkg.add(PackageSpec(url=\"https://github.com/spqb/adabmDCA.jl\"))'\n```\nThis will install all necessary dependencies and set up the package.\n\n#### Option 2: Manual Installation via Julia\n\n1.  Open Julia and install the package by running:\n    ```{Julia}\n    using Pkg\n    Pkg.add(url=\"https://github.com/spqb/adabmDCA.jl\")\n    Pkg.add(\"ArgParse\")\n    ```\n    \n2.  Download the files `adabmDCA.sh` and `execute.jl` into the same folder\n    ```{bash}\n    wget https://github.com/spqb/adabmDCA.jl/blob/main/install.sh\n    wget https://github.com/spqb/adabmDCA.jl/blob/main/execute.jl\n    ```\n\n3.  Make the script executable by opening a terminal in the folder and running:\n    ```{bash}\n    chmod +x adabmDCA.sh\n    ```\nThis will set up the package for use.\n\nThe main repository of the implementation can be found at [adabmDCA.jl](https://github.com/spqb/adabmDCA.jl.git).\n\n### C/C++ implementation\n1.    Clone the repository\n      ```{bash}\n      git clone https://github.com/spqb/adabmDCAc.git\n      ```\n2.    In the __src__ folder run\n      ```{bash}\n      make\n      ```\n3.    It will generate the executable file __adabmDCA__. In the main folder run also `chmod +x adabmDCA.sh` to use the main script file. See\n      ```{bash}\n      ./adabmDCA --help\n      ```\n      for a complete list of features.\n\nThe main repository of the implementation can be found at [adabmDCAc](https://github.com/spqb/adabmDCAc.git).\n\n\n## Introducton\nThis package presents a new version of `adabmDCA`. The package comes in three different languages: C++ (single-core CPU), Julia (multi-core CPU), and Python (GPU-oriented). They share the same front-end interface from the terminal allowing the user to install and use one of the three equivalent versions based on hardware or software constraints.\n\nWe provide three different training routines:\n- **bmDCA**: Trains a fully-connected DCA model;\n- **eaDCA**: Trains a DCA model on a sparse coupling network by progressively adding couplings during the training;\n- **edDCA**: Starts from a trained bmDCA model and iteratively removes the less informative couplings until the target sparsity is reached.\n\nAdditionally, we provide several routines for sampling and analyzing the generated sequences once a DCA model is trained, for constructing and evaluating - according to a DCA model - a single mutant library from a given wild type, and finally, for computing the pairwise contact scores, in terms of average-product corrected Frobenius norms of the DCA couplings.\n\n## Usage\nCheck out the online [Documentation](https://spqb.github.io/adabmDCApy) :wink:\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","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fspqb%2Fadabmdca","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fspqb%2Fadabmdca","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fspqb%2Fadabmdca/lists"}