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https://github.com/wfondrie/mokapot
Fast and flexible semi-supervised learning for peptide detection in Python
https://github.com/wfondrie/mokapot
bioinformatics conda machine-learning peptide-detection percolator proteomics python
Last synced: 17 days ago
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Fast and flexible semi-supervised learning for peptide detection in Python
- Host: GitHub
- URL: https://github.com/wfondrie/mokapot
- Owner: wfondrie
- License: apache-2.0
- Created: 2019-08-29T17:03:27.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2024-09-06T22:25:11.000Z (2 months ago)
- Last Synced: 2024-10-07T03:43:13.198Z (about 1 month ago)
- Topics: bioinformatics, conda, machine-learning, peptide-detection, percolator, proteomics, python
- Language: Python
- Homepage: https://mokapot.readthedocs.io
- Size: 67.9 MB
- Stars: 41
- Watchers: 7
- Forks: 15
- Open Issues: 23
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- awesome-proteomics - mokapot - python - Semisupervised Learning for Peptide Detection - [paper](https://pubs.acs.org/doi/10.1021/acs.jproteome.0c01010) (4. Assorted pipeline Tools / Table of Contents)
README
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.