| Name | phosx JSON |
| Version |
0.7.1
JSON |
| download |
| home_page | None |
| Summary | Differential kinase activity inference from phosphosproteomics data |
| upload_time | 2024-08-14 18:48:36 |
| maintainer | None |
| docs_url | None |
| author | Alessandro Lussana |
| requires_python | <4.0,>=3.10 |
| license | None |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<p align="center">
<img width="190" src="https://i.imgur.com/OzGTvkt.png">
<br>
Kinase activity inference from phosphosproteomics data based on substrate sequence specificity
<br><br>
</p>
> Current version: `0.7.1`
> Research paper: [https://doi.org/10.1101/2024.03.22.586304](https://doi.org/10.1101/2024.03.22.586304)
> Benchmark repository: [https://github.com/alussana/phosx-benchmark](https://github.com/alussana/phosx-benchmark)
# Overview
<p align="center">
<br>
<img width="900" src="https://i.imgur.com/6DdMDom.png">
<br>
</p>
PhosX infers differential kinase activities from phosphoproteomics data without requiring any prior knowledge database of kinase-phosphosite associations. PhosX assigns the detected phosphopeptides to potential upstream kinases based on experimentally determined substrate sequence specificities, and it tests the enrichment of a kinase's potential substrates in the extremes of a ranked list of phosphopeptides using a Kolmogorov-Smirnov-like statistic. A _p_ value for this statistic is extracted empirically by random permutations of the phosphosite ranks.
# Installation
## From [PyPI](https://pypi.org/project/phosx/)
```bash
pip install phosx
```
## From source (requires [Poetry](https://python-poetry.org))
```
poetry build
pip install dist/*.whl
```
# Usage
Run PhosX with default parameters on an example dataset, using up to 8 cores, and redirecting the output table to `kinase_activities.tsv`:
```bash
phosx -c 8 tests/seqrnk/koksal2018_log2.fold.change.8min.seqrnk > kinase_activities.tsv
```
See the full list of command line options with `phosx -h`.
Alongside the main program, this package also installs `make-seqrnk`. This utily can be used to easily generate a _seqrnk_ file, which is used as input by PhosX, given a list of phosphosites, each one identified by a UniProtAC and residue coordinate. `make-seqrnk` will query the UniProt database to fetch the appropriate subsequences and build the _seqrnk_ file. Run `make-seqrnk -h` for more details, or see an example with:
```bash
cat tests/p_list/15_3.tsv | make-seqrnk > 15_3.seqrnk
```
# Cite
BibTeX:
```bibtex
@article{Lussana2024,
title = {PhosX: data-driven kinase activity inference from phosphoproteomics experiments},
url = {http://dx.doi.org/10.1101/2024.03.22.586304},
DOI = {10.1101/2024.03.22.586304},
publisher = {Cold Spring Harbor Laboratory},
author = {Lussana, Alessandro and Petsalaki, Evangelia},
year = {2024},
month = mar
}
```
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"description": "<p align=\"center\">\n <img width=\"190\" src=\"https://i.imgur.com/OzGTvkt.png\">\n <br>\n Kinase activity inference from phosphosproteomics data based on substrate sequence specificity\n <br><br>\n</p>\n\n\n\n> Current version: `0.7.1`\n\n> Research paper: [https://doi.org/10.1101/2024.03.22.586304](https://doi.org/10.1101/2024.03.22.586304)\n\n> Benchmark repository: [https://github.com/alussana/phosx-benchmark](https://github.com/alussana/phosx-benchmark)\n\n# Overview\n\n<p align=\"center\">\n<br>\n <img width=\"900\" src=\"https://i.imgur.com/6DdMDom.png\">\n <br>\n</p>\n\nPhosX infers differential kinase activities from phosphoproteomics data without requiring any prior knowledge database of kinase-phosphosite associations. PhosX assigns the detected phosphopeptides to potential upstream kinases based on experimentally determined substrate sequence specificities, and it tests the enrichment of a kinase's potential substrates in the extremes of a ranked list of phosphopeptides using a Kolmogorov-Smirnov-like statistic. A _p_ value for this statistic is extracted empirically by random permutations of the phosphosite ranks.\n\n# Installation\n\n## From [PyPI](https://pypi.org/project/phosx/)\n\n```bash\npip install phosx\n```\n\n## From source (requires [Poetry](https://python-poetry.org))\n\n```\npoetry build\npip install dist/*.whl\n```\n\n# Usage\n\nRun PhosX with default parameters on an example dataset, using up to 8 cores, and redirecting the output table to `kinase_activities.tsv`:\n\n```bash\nphosx -c 8 tests/seqrnk/koksal2018_log2.fold.change.8min.seqrnk > kinase_activities.tsv\n```\n\nSee the full list of command line options with `phosx -h`.\n\nAlongside the main program, this package also installs `make-seqrnk`. This utily can be used to easily generate a _seqrnk_ file, which is used as input by PhosX, given a list of phosphosites, each one identified by a UniProtAC and residue coordinate. `make-seqrnk` will query the UniProt database to fetch the appropriate subsequences and build the _seqrnk_ file. Run `make-seqrnk -h` for more details, or see an example with: \n\n```bash\ncat tests/p_list/15_3.tsv | make-seqrnk > 15_3.seqrnk\n```\n\n# Cite\n\nBibTeX:\n\n```bibtex\n@article{Lussana2024,\n title = {PhosX: data-driven kinase activity inference from phosphoproteomics experiments},\n url = {http://dx.doi.org/10.1101/2024.03.22.586304},\n DOI = {10.1101/2024.03.22.586304},\n publisher = {Cold Spring Harbor Laboratory},\n author = {Lussana, Alessandro and Petsalaki, Evangelia},\n year = {2024},\n month = mar \n}\n```\n",
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