The EPIC toolkit was initially published here: Hu, L. Z., et al. “EPIC: software toolkit for elution profile-based inference of protein complexes.” Nature methods 16.8 (2019): 737-742. Link to the publication
Forked from the orignial repository, I have created RunEPIC to provide the code to run EPIC locally.
1. Environment The main function in EPIC was implemented in Python, given the headache caused by various libraries, the Anaconda enrionment was used.
This R package implements statistical modelling of affinity purification–mass spectrometry (AP-MS) data to compute confidence scores to identify bona fide protein-protein interactions (PPI).
Installation The development version can be installed through github:
devtools::install_github(repo="zqzneptune/SMAD") library(SMAD) Input Data A demo data.frame was provided as a hint how the input data should strcutured in order to run the scoring functions:
data(TestDatInput) colnames(TestDataInput) [1] "idRun" "idBait" "idPrey" "countPrey" "lenPrey" idRun idBait idPrey countPrey lenPrey Unique ID of one AP-MS run Bait ID Prey ID Prey peptide count Protein sequence length of the prey In case of duplcates, a suffix or prefix of e.
A collection of resources regarding affinity purification mass spectrometry proteomics for the identification of protein-protein interactions.
Algorithms Year Algorithm Publication Implementation 2006 SAI (socio-affinity index) Anne-Claude Gavin et al., Nature 2007 partial least squares based regression model Rob M Ewing et al., Mol Syst Biol 2007 PE (purification enrichment) Sean R. Collins et al., Molecular & Cellular Proteomics SMAD 2007 Hart G Traver Hart et al.