Identification of protein complexes from co-immunoprecipitation data.
BACKGROUND
Advanced technologies are producing large-scale protein-protein interaction data at an ever increasing pace. A fundamental challenge in analyzing these data is the inference of protein machineries. Previous methods for detecting protein complexes have been mainly based on analyzing binary protein-protein interaction data, ignoring the more involved co-complex relations obtained from co-immunoprecipitation experiments.


RESULTS
Here, we devise a novel framework for protein complex detection from co-immunoprecipitation data. The framework aims at identifying sets of preys that significantly co-associate with the same set of baits. In application to an array of datasets from yeast, our method identifies thousands of protein complexes. Comparing these complexes to manually curated ones, we show that our method attains very high specificity and sensitivity levels (∼ 80%), outperforming current approaches for protein complex inference.


BACKGROUND
Supplementary information and the program are available at http://www.cs.tau.ac.il/~roded/CODEC/main.html.
School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
Guy
G
Geva
Roded
R
Sharan
D000465:Algorithms; D005656:Fungal Proteins / Q000032:analysis; D047468:Immunoprecipitation; D046912:Multiprotein Complexes / Q000032:analysis*
D005656:Fungal Proteins; D046912:Multiprotein Complexes