Increasing confidence of protein interactomes using network topological metrics

Experimental limitations in high-throughput protein-protein interaction detection methods have resulted in low quality interaction datasets that contained sizable fractions of false positives and false negatives. Small-scale, focused experiments are then needed to complement the high throughput methods to extract true protein interactions. However, the naturally vast interactomes would require much more scalable approaches.

We describe a novel method called IRAP* as a computational complement for repurification of the highly erroneous experimentally-derived protein interactomes. Our method involves an iterative process of removing interactions that are confidently identified as false positives and adding interactions detected as false negatives into the interactomes. Identification of both false positives and false negatives are performed in IRAP* using interaction confidence measures based on network topological metrics. Potential false positives are identified amongst the detected interactions as those with very low computed confidence values, while potential false negatives are discovered as the undetected interactions with high computed confidence values. Our results from applying IRAP* on large-scale interaction data sets generated by the popular yeast-two-hybrid assays for yeast, fruit fly and worm showed that the computationally repurified interaction data sets contained potentially lower fractions of false positive and false negative errors based on functional homogeneity.

--by Jin Chen, Wynne Hsu, Mong Li Lee and See-Kiong Ng, Bioinformatics, 2006

NeMoFinder: Dissecting genome wide protein-protein interactions with repeated and unique network motifs

Recent works in network analysis have revealed the existence of network motifs in biological networks such as protein-protein interaction (PPI) networks. However, existing motif mining algorithms are not sufficiently scalable to find meso-scale network motifs. Also, there has been little or no work to systematically exploit the extracted network motifs for dissecting the vast interactomes.

We describe an efficient network motif discovery algorithm, NeMoFinder, that can mine meso-scale repeated and unique network motifs in large PPI networks. Using NeMoFinder, we successfully discovered, for the first time, up to size-12 network motifs in a large whole-genome S. cerevisiae (Yeast) PPI network. We also show that such network motifs can be systematically exploited for indexing the reliability of PPI data generated via highly erroneous high-throughput experimental methods.

--by Jin Chen, Wynne Hsu, Mong Li Lee and See-Kiong Ng, SIGKDD, 2006