| Name | mokapot JSON |
| Version |
0.10.0
JSON |
| download |
| home_page | |
| Summary | Fast and flexible semi-supervised learning for peptide detection |
| upload_time | 2023-09-11 18:51:12 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.6 |
| license | Apache 2.0 |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<img src="https://raw.githubusercontent.com/wfondrie/mokapot/master/static/mokapot_logo_dark.svg" width=300>
---
[](http://bioconda.github.io/recipes/mokapot/README.html)
[](https://pypi.org/project/mokapot/)
[](https://github.com/wfondrie/mokapot/actions?query=workflow%3Atests)
[](https://mokapot.readthedocs.io/en/latest/?badge=latest)
Fast and flexible semi-supervised learning for peptide detection.
mokapot is fundamentally a Python implementation of the semi-supervised learning
algorithm first introduced by Percolator. We developed mokapot to add additional
flexibility to our analyses, whether to try something experimental---such as
swapping Percolator's linear support vector machine classifier for a non-linear,
gradient boosting classifier---or to train a joint model across experiments
while retaining valid, per-experiment confidence estimates. We designed mokapot
to be extensible and support the analysis of additional types of proteomics
data, such as cross-linked peptides from cross-linking mass spectrometry
experiments. mokapot offers basic functionality from the command line, but using
mokapot as a Python package unlocks maximum flexibility.
For more information, check out our
[documentation](https://mokapot.readthedocs.io).
## Citing
If you use mokapot in your work, please cite:
> Fondrie W. E. & Noble W. S. mokapot: Fast and Flexible Semisupervised
> Learning for Peptide Detection. J Proteome Res (2021) doi:
> 10.1021/acs.jproteome.0c01010. PMID: 33596079.
> [Link](https://doi.org/10.1021/acs.jproteome.0c01010)
## Installation
mokapot requires Python 3.6+ and can be installed with pip or conda.
Using conda:
```
$ conda install -c bioconda mokapot
```
Using pip:
```
$ pip3 install mokapot
```
Additionally, you can install the development version directly from GitHub:
```
$ pip3 install git+git://github.com/wfondrie/mokapot
```
## Basic Usage
Before you can use mokapot, you need PSMs assigned by a search engine available
in the [Percolator tab-delimited file
format](https://github.com/percolator/percolator/wiki/Interface#tab-delimited-file-format)
(often referred to as the Percolator input, or "PIN", file format) or as a
PepXML file.
Simple mokapot analyses can be performed at the command line:
```Bash
$ mokapot psms.pin
```
Alternatively, the Python API can be used to perform analyses in the Python
interpreter and affords greater flexibility:
```Python
>>> import mokapot
>>> psms = mokapot.read_pin("psms.pin")
>>> results, models = mokapot.brew(psms)
>>> results.to_txt()
```
Check out our [documentation](https://mokapot.readthedocs.io) for more details
and examples of mokapot in action.
Raw data
{
"_id": null,
"home_page": "",
"name": "mokapot",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.6",
"maintainer_email": "",
"keywords": "",
"author": "",
"author_email": "\"William E. Fondrie\" <fondriew@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/29/80/07b90c51194720b859efa64ca23d87e0f1833c03dd0d4f03c07622ffab92/mokapot-0.10.0.tar.gz",
"platform": null,
"description": "<img src=\"https://raw.githubusercontent.com/wfondrie/mokapot/master/static/mokapot_logo_dark.svg\" width=300> \n\n--- \n[](http://bioconda.github.io/recipes/mokapot/README.html)\n[](https://pypi.org/project/mokapot/)\n[](https://github.com/wfondrie/mokapot/actions?query=workflow%3Atests)\n[](https://mokapot.readthedocs.io/en/latest/?badge=latest)\n\n\n\nFast and flexible semi-supervised learning for peptide detection. \n\nmokapot is fundamentally a Python implementation of the semi-supervised learning\nalgorithm first introduced by Percolator. We developed mokapot to add additional\nflexibility to our analyses, whether to try something experimental---such as\nswapping Percolator's linear support vector machine classifier for a non-linear,\ngradient boosting classifier---or to train a joint model across experiments\nwhile retaining valid, per-experiment confidence estimates. We designed mokapot\nto be extensible and support the analysis of additional types of proteomics\ndata, such as cross-linked peptides from cross-linking mass spectrometry\nexperiments. mokapot offers basic functionality from the command line, but using\nmokapot as a Python package unlocks maximum flexibility.\n\nFor more information, check out our\n[documentation](https://mokapot.readthedocs.io). \n\n## Citing \nIf you use mokapot in your work, please cite: \n\n> Fondrie W. E. & Noble W. S. mokapot: Fast and Flexible Semisupervised\n> Learning for Peptide Detection. J Proteome Res (2021) doi:\n> 10.1021/acs.jproteome.0c01010. PMID: 33596079.\n> [Link](https://doi.org/10.1021/acs.jproteome.0c01010)\n\n## Installation \n\nmokapot requires Python 3.6+ and can be installed with pip or conda. \n\nUsing conda:\n```\n$ conda install -c bioconda mokapot\n```\n\nUsing pip:\n```\n$ pip3 install mokapot\n```\n\nAdditionally, you can install the development version directly from GitHub: \n\n```\n$ pip3 install git+git://github.com/wfondrie/mokapot\n```\n\n## Basic Usage \n\nBefore you can use mokapot, you need PSMs assigned by a search engine available\nin the [Percolator tab-delimited file\nformat](https://github.com/percolator/percolator/wiki/Interface#tab-delimited-file-format)\n(often referred to as the Percolator input, or \"PIN\", file format) or as a \nPepXML file. \n\nSimple mokapot analyses can be performed at the command line:\n\n```Bash\n$ mokapot psms.pin\n```\n\nAlternatively, the Python API can be used to perform analyses in the Python\ninterpreter and affords greater flexibility:\n\n```Python\n>>> import mokapot\n>>> psms = mokapot.read_pin(\"psms.pin\")\n>>> results, models = mokapot.brew(psms)\n>>> results.to_txt()\n```\n\nCheck out our [documentation](https://mokapot.readthedocs.io) for more details\nand examples of mokapot in action.\n",
"bugtrack_url": null,
"license": "Apache 2.0",
"summary": "Fast and flexible semi-supervised learning for peptide detection",
"version": "0.10.0",
"project_urls": {
"Bug Tracker": "https://github.com/wfondrie/mokapot/issues",
"Discussion Board": "https://github.com/wfondrie/mokapot/discussions",
"Documentation": "https://mokapot.readthedocs.io",
"Homepage": "https://github.com/wfondrie/mokapot"
},
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "f957d902c4b7b9d1b05a0e95283947e022f1a1c4478a99b79fe60c9202eb7488",
"md5": "53b429b26deeac7d85f6d8d817b06396",
"sha256": "3504f1a3b03214a0dc955c95297f9c28b4dfbe9d3162f36e70de19516a8ad521"
},
"downloads": -1,
"filename": "mokapot-0.10.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "53b429b26deeac7d85f6d8d817b06396",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.6",
"size": 74392,
"upload_time": "2023-09-11T18:51:09",
"upload_time_iso_8601": "2023-09-11T18:51:09.433784Z",
"url": "https://files.pythonhosted.org/packages/f9/57/d902c4b7b9d1b05a0e95283947e022f1a1c4478a99b79fe60c9202eb7488/mokapot-0.10.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "298007b90c51194720b859efa64ca23d87e0f1833c03dd0d4f03c07622ffab92",
"md5": "064f734f8de1217cf8da3f7fdd3afde4",
"sha256": "80e483491a5b2e6a069f561e88176d5751258b89a7147f5d855e3c11461b7c0b"
},
"downloads": -1,
"filename": "mokapot-0.10.0.tar.gz",
"has_sig": false,
"md5_digest": "064f734f8de1217cf8da3f7fdd3afde4",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.6",
"size": 39000341,
"upload_time": "2023-09-11T18:51:12",
"upload_time_iso_8601": "2023-09-11T18:51:12.428106Z",
"url": "https://files.pythonhosted.org/packages/29/80/07b90c51194720b859efa64ca23d87e0f1833c03dd0d4f03c07622ffab92/mokapot-0.10.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-09-11 18:51:12",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "wfondrie",
"github_project": "mokapot",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "mokapot"
}
