phosx 0.8.0

Differential kinase activity inference from phosphosproteomics data

Kinase activity inference from phosphosproteomics data based on substrate sequence specificity

Current version: 0.8.0

Research paper: https://doi.org/10.1101/2024.03.22.586304

Benchmark: https://github.com/alussana/phosx-benchmark

Data: https://github.com/alussana/kinase_pssms

Overview

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

pip install phosx

From source (requires Poetry)

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:

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:

cat tests/p_list/15_3.tsv | make-seqrnk > 15_3.seqrnk

Cite

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 
}