HIPPIE: Integrating Protein Interaction Networks with Experiment Based Quality Scores

By Martin H. Schaefer, Jean-Fred Fontaine, Arunachalam Vinayagam, P. Porras, Erich E. Wanker, Miguel Andrade. PLoS ONE, 2012

337 citations recorded by the source corpus.

Protein function is often modulated by protein-protein interactions (PPIs) and therefore defining the partners of a protein helps to understand its activity. PPIs can be detected through different experimental approaches and are collected in several expert curated databases. These databases are used by researchers interested in examining detailed information on particular proteins. In many analyses the reliability of the characterization of the interactions becomes important and it might be necessary to select sets of PPIs of different confidence levels. To this goal, we generated HIPPIE (Human Integrated Protein-Protein Interaction rEference), a human PPI dataset with a normalized scoring scheme that integrates multiple experimental PPI datasets. HIPPIE's scoring scheme has been optimized by human experts and a computer algorithm to reflect the amount and quality of evidence for a given PPI and we show that these scores correlate to the quality of the experimental characterization. The HIPPIE web tool (available at http://cbdm.mdc-berlin.de/tools/hippie) allows researchers to do network analyses focused on likely true PPI sets by generating subnetworks around proteins of interest at a specified confidence level.

Subfields: Cell Biology, Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d56b981091deb6286078833db6ca8729aaf0e6d2

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In Molecular Biology

Citations in the corpus

HIPPIE v2.0: enhancing meaningfulness and reliability of protein–protein interaction networks (2016, background): “Since its first release in early 2011 (18), HIPPIE has grown significantly both in size and functionality.”
More Is Better: Recent Progress in Multi-Omics Data Integration Methods (2017, methodology): “Propagated mutation and DGE proﬁles for each gene are generated with the help of prior knowledge in PPI framework (Schaefer et al., 2012).”