Our group builds tool for interaction proteomics, phage biology and network modeling. This page collects the code, datasets and methods we share with collaborators.

Code & pipelines

• FossatiLab GitHub organization — all public repos live here: https://github.com/FossatiLab

• saintPy — Python implementation inspired by SAINT, the statistical model introduced for scoring AP-MS protein–protein interactions using spectral count–based likelihood ratios. Designed for AP-MS and Proximity labeling experiments using either DIA or DDA.

• PCprophet — implementation of the PCprophet framework, which detects protein complexes from co-fractionation/SEC-MS data utilizing ensemble learning and enables differential analysis of co-fractionation mass spectrometry datasets.

• PPIprophet — toolkit based on the Nature Methods DIP-MS methods, combining deep-learning protein interaction prediction, graph clustering to resolve subcomplexes from an DIP-MS datasets, especially under perturbations or time courses.

• NetworkPropagationPy — Python port of the network-propagation algorithm described in the Nature Reviews Genetics primer, implementing heat-diffusion–style smoothing of node scores across a biological network for gene-ranking, module detection and signal-enhancement tasks.