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Assorted pipeline Tools"],"sub_categories":["Table of Contents"],"readme":"Fast 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\u003e Fondrie W. E. \u0026 Noble W. S. mokapot: Fast and Flexible Semisupervised\n\u003e Learning for Peptide Detection. J Proteome Res (2021) doi:\n\u003e 10.1021/acs.jproteome.0c01010. PMID: 33596079.\n\u003e [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\nimport mokapot\npsms = mokapot.read_pin(\"psms.pin\")\nresults, models = mokapot.brew(psms)\nresults.to_txt()\n```\n\nCheck out our [documentation](https://mokapot.readthedocs.io) for more details\nand examples of mokapot in action.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwfondrie%2Fmokapot","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwfondrie%2Fmokapot","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwfondrie%2Fmokapot/lists"}