semvec/pypi-packages huggingface dataset:
mokapot 0.10.0
Fast and flexible semi-supervised learning for peptide detection
License: Apache 2.0
Requires Python: >=3.6

<img src="https://raw.githubusercontent.com/wfondrie/mokapot/master/static/mokapot_logo_dark.svg" width=300>  

---  
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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.