Deane CM, et al. (2002) Protein interactions: two methods for assessment of the reliability of high throughput observations. Mol Cell Proteomics 1(5):349-56
Abstract: High throughput methods for detecting protein interactions require assessment of their accuracy. We present two forms of computational assessment. The first method is the expression profile reliability (EPR) index. The EPR index estimates the biologically relevant fraction of protein interactions detected in a high throughput screen. It does so by comparing the RNA expression profiles for the proteins whose interactions are found in the screen with expression profiles for known interacting and non-interacting pairs of proteins. The second form of assessment is the paralogous verification method (PVM). This method judges an interaction likely if the putatively interacting pair has paralogs that also interact. In contrast to the EPR index, which evaluates datasets of interactions, PVM scores individual interactions. On a test set, PVM identifies correctly 40% of true interactions with a false positive rate of approximately 1%. EPR and PVM were applied to the Database of Interacting Proteins (DIP), a large and diverse collection of protein-protein interactions that contains over 8000 Saccharomyces cerevisiae pairwise protein interactions. Using these two methods, we estimate that approximately 50% of them are reliable, and with the aid of PVM we identify confidently 3003 of them. Web servers for both the PVM and EPR methods are available on the DIP website (dip.doe-mbi.ucla.edu/Services.cgi).
|Status: Published||Type: Journal Article||PubMed ID: 12118076|
Topics addressed in this paper
- To find other papers on a gene and topic, click on the colored ball in the appropriate box.
- displays other papers with information about that topic for that gene.
- displays other papers in SGD that are associated with that topic.
The topic is addressed in these papers but does not describe a specific gene or chromosomal feature.
- To go to the Locus page for a gene, click on the gene name.