Abstract
Background: Tandem affinity purification coupled with mass-spectrometry (TAP/MS) analysis is a popular method\r for the identification of novel endogenous protein-protein interactions (PPIs) in large-scale. Computational analysis\r of TAP/MS data is a critical step, particularly for high-throughput datasets, yet it remains challenging due to the\r noisy nature of TAP/MS data.\r Results: We investigated several major TAP/MS data analysis methods for identifying PPIs, and developed an\r advanced method, which incorporates an improved statistical method to filter out false positives from the negative\r controls. Our method is named PPIRank that stands for PPI ranking in TAP/MS data. We compared PPIRank with\r several other existing methods in analyzing two pathway-specific TAP/MS PPI datasets from Drosophila.\r Conclusion: Experimental results show that PPIRank is more capable than other approaches in terms of identifying\r known interactions collected in the BioGRID PPI database. Specifically, PPIRank is able to capture more true\r interactions and simultaneously less false positives in both Insulin and Hippo pathways of Drosophila Melanogaster.