Identifying protein complexes directly from high-throughput TAP data with Markov random fields

Background: Predicting protein complexes from experimental data remains a challenge due to limited resolution and stochastic errors of high-throughput methods. Current algorithms to reconstruct the complexes typically rely on a two-step process. First, they construct an interaction graph from the data, predominantly using heuristics, and subsequently cluster its vertices to identify protein complexes. Results: We propose a model-based identification of protein complexes directly from the experimental observations. Our model of protein complexes based on Markov random fields explicitly incorporates false negative and false positive errors and exhibits a high robustness to noise. A model-based quality score for the resulting clusters allows us to identify reliable predictions in the complete data set. Comparisons with prior work on reference data sets shows favorable results, particularly for larger unfiltered data sets. Additional information on predictions, including the source code u...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Wasinee Rungsarityotin, Roland Krause, Arno Schödl, Alexander Schliep
• Keywords: BMCBI 2007, Larger Unfiltered Data, Protein Complexes, Reference Data