title: Assessing coverage of protein interaction data using capture-recapture models.
abstract: Protein interaction networks comprise thousands of individual binary links between distinct proteins. Whilst these data have attracted considerable attention and been the focus of many different studies, the networks, their structure, function, and how they change over time are still not fully known. More importantly, there is still considerable uncertainty regarding their size, and the quality of the available data continues to be questioned. Here, we employ statistical models of the experimental sampling process, in particular capture-recapture methods, in order to assess the false discovery rate and size of protein interaction networks. We uses these methods to gauge the ability of different experimental systems to find the true binary interactome. Our model allows us to obtain estimates for the size and false-discovery rate from simple considerations regarding the number of repeatedly interactions, and provides suggestions as to how we can exploit this information in order to reduce the effects of noise in such data. In particular our approach does not require a reference dataset. We estimate that approximately more than half of the true physical interactome has now been sampled in yeast.
authors.affiliation: Centre for Bioinformatics, Imperial College London, London, UK. william.kelly04@imperial.ac.uk
authors.forename: W P
authors.initials: WP
authors.lastname: Kelly
authors.forename: M P H
authors.initials: MP
authors.lastname: Stumpf
mesh_terms: D008956:Models, Chemical; D025941:Protein Interaction Mapping / Q000379:methods* / Q000706:statistics & numerical data*; D060066:Protein Interaction Maps; D012441:Saccharomyces cerevisiae / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry / Q000378:metabolism
chemical_list: D029701:Saccharomyces cerevisiae Proteins