dcatoolkit


Namedcatoolkit JSON
Version 0.2.0 PyPI version JSON
download
home_pageNone
SummaryCollection of useful modules and representations for managing DCA output data.
upload_time2024-10-10 08:42:48
maintainerNone
docs_urlNone
authorNone
requires_python>=3.10
licenseMIT License Copyright (c) 2024 Raheel Syed Ahmed Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
keywords dca toolkit di coevolution
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            # dcatoolkit
 Collection of useful modules and representations for managing DCA output data.

## Major Sections
### Representations
  * Use Pairs to load lists, tuples, sets, and ndarrays with the correct orientation of elements. This will allow you to yield integer pairs that can be mirrored (where y becomes x and vice versa) and to subset various pairs.
  * Use DirectInformationData to create 3-column structured ndarrays that can be sorted by "DI", mapped to a protein with a ResidueAlignment, and used to generate output for other programs (including UCSF Chimera)
  * Use ResidueAlignment to generate a reference map. Indices of one sequence of characters can be linked to their corresponding indices of the other sequence of characters. The dictionaries produced, domain-to-protein and protein-to-domain, allow for forward mapping and backmapping.
  * Use StructureInformation to find contacts in a protein structure and find atomic information related to specific pairs of interest.
### Analytics
  * Use MSATools to load in Multiple Sequence Alignment (MSA) data and provide functionality including generating frequency statistics on "gappiness" in the MSA and filtering and cleaning MSAs.


## Diagram of Hidden Markov Machine & Direct Coupling Analysis Pipeline
<p align="center">
  <img src="https://github.com/user-attachments/assets/4768e08f-d513-4dbf-abc5-c80c1b3d42aa"/>
</p>

            

Raw data

            {
    "_id": null,
    "home_page": null,
    "name": "dcatoolkit",
    "maintainer": null,
    "docs_url": null,
    "requires_python": ">=3.10",
    "maintainer_email": "Raheel Syed Ahmed <raheelsyedahmed@gmail.com>",
    "keywords": "dca, toolkit, DI, coevolution",
    "author": null,
    "author_email": "Raheel Syed Ahmed <raheelsyedahmed@gmail.com>",
    "download_url": "https://files.pythonhosted.org/packages/b7/14/719cfbeb392b96558bd2a9ce4900a1ac90b6888275d07f62c59a0281a844/dcatoolkit-0.2.0.tar.gz",
    "platform": null,
    "description": "# dcatoolkit\r\n Collection of useful modules and representations for managing DCA output data.\r\n\r\n## Major Sections\r\n### Representations\r\n  * Use Pairs to load lists, tuples, sets, and ndarrays with the correct orientation of elements. This will allow you to yield integer pairs that can be mirrored (where y becomes x and vice versa) and to subset various pairs.\r\n  * Use DirectInformationData to create 3-column structured ndarrays that can be sorted by \"DI\", mapped to a protein with a ResidueAlignment, and used to generate output for other programs (including UCSF Chimera)\r\n  * Use ResidueAlignment to generate a reference map. Indices of one sequence of characters can be linked to their corresponding indices of the other sequence of characters. The dictionaries produced, domain-to-protein and protein-to-domain, allow for forward mapping and backmapping.\r\n  * Use StructureInformation to find contacts in a protein structure and find atomic information related to specific pairs of interest.\r\n### Analytics\r\n  * Use MSATools to load in Multiple Sequence Alignment (MSA) data and provide functionality including generating frequency statistics on \"gappiness\" in the MSA and filtering and cleaning MSAs.\r\n\r\n\r\n## Diagram of Hidden Markov Machine & Direct Coupling Analysis Pipeline\r\n<p align=\"center\">\r\n  <img src=\"https://github.com/user-attachments/assets/4768e08f-d513-4dbf-abc5-c80c1b3d42aa\"/>\r\n</p>\r\n",
    "bugtrack_url": null,
    "license": "MIT License  Copyright (c) 2024 Raheel Syed Ahmed  Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:  The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.  THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ",
    "summary": "Collection of useful modules and representations for managing DCA output data.",
    "version": "0.2.0",
    "project_urls": null,
    "split_keywords": [
        "dca",
        " toolkit",
        " di",
        " coevolution"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "ff75a4e581f5f72655ab83f3e610c0e8f16227188e9d938a32a41013ad632842",
                "md5": "0bb6d0693bcfdce94b3d627d5d5dddc3",
                "sha256": "181b43affcd661b3d599efbb1357766e1c3ccfb613d3b8d9c2da636a43bff3e9"
            },
            "downloads": -1,
            "filename": "dcatoolkit-0.2.0-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "0bb6d0693bcfdce94b3d627d5d5dddc3",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": ">=3.10",
            "size": 17124,
            "upload_time": "2024-10-10T08:42:47",
            "upload_time_iso_8601": "2024-10-10T08:42:47.459739Z",
            "url": "https://files.pythonhosted.org/packages/ff/75/a4e581f5f72655ab83f3e610c0e8f16227188e9d938a32a41013ad632842/dcatoolkit-0.2.0-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "b714719cfbeb392b96558bd2a9ce4900a1ac90b6888275d07f62c59a0281a844",
                "md5": "2a1cf8052f64d5d8b788abd320370622",
                "sha256": "74bc25967899cdaf215702ded1608335c12450bc34e4beebc07893928f571872"
            },
            "downloads": -1,
            "filename": "dcatoolkit-0.2.0.tar.gz",
            "has_sig": false,
            "md5_digest": "2a1cf8052f64d5d8b788abd320370622",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.10",
            "size": 18751,
            "upload_time": "2024-10-10T08:42:48",
            "upload_time_iso_8601": "2024-10-10T08:42:48.876624Z",
            "url": "https://files.pythonhosted.org/packages/b7/14/719cfbeb392b96558bd2a9ce4900a1ac90b6888275d07f62c59a0281a844/dcatoolkit-0.2.0.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2024-10-10 08:42:48",
    "github": false,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "lcname": "dcatoolkit"
}
        
Elapsed time: 1.40718s