Tool for improving affinity proteomics data.

Description

WeSA (weighted socioaffinity) is a novel statistical score which can be used to rank proteomics experiments results of the bait-prey type (e.g. affinity purification, immunoprecipitation or proximity labelling) and help distinguishing true protein-protein interactions from the noise.

We have implemented WeSA as a web application that can be used to rank and filter any list of protein-protein interactions. WeSA is freely available and open to all users without login requirement at wesa.russelllab.org.

Results are presented in an interactive network that allows the user to filter out the low ranked interactions, as well as in a table that contains all interacting protein pairs together with their WeSA score and which can be downloaded for further analysis.

Evaluation

ROC analysis (using CORUM-PDB positives and Negatome negatives) shows that WeSA improves over other measures of interaction confidence. WeSA shows consistently good results over all datasets (up to: AUC = 0.93 and at best threshold: TPR = 0.84, FPR = 0.11, Precision = 0.98).

Setup & Configuration

Requirements

WeSA requires Python3.6 or higher. We recommend using a virtual environment to install the required packages, e.g.:

source my_env/bin/activate
pip install -r wesa/requirements.txt

Data

All data files required for WeSA can be downloaded at http://russelllab.org/wesa/data/.

Edit wesa_app/.env file to point to the correct data files location.

Use

WeSA is provided as a Flask app that allows the same functionalities as our online version.

In addition, WeSA can be run via command line interface, directly generating the network and table output files.

python run.py -i <input_file> -d <database> -o <output_dir>