Interaction network

About: Interaction network is a research topic. Over the lifetime, 2700 publications have been published within this topic receiving 113372 citations.

Still stratus not altocumulus: further evidence against the date/party hub distinction.

Abstract: Using a small dataset of protein–protein interactions [1], it was proposed that the yeast protein interaction network is made up of two sorts of hubs, party and date, and that these define modularity in the yeast protein interaction network. We found [2], by using several larger high-confidence datasets and appropriate statistical analyses, that we could not support these conclusions. Bertin et al. now invite analysis of a further dataset of protein-protein interactions, which they argue support the party/date distinction. The claimed properties of party and date hubs are not, however, present in this dataset either. In particular, when controlling for important covariables where necessary, there is no evidence for (1) bimodality in partner co-expression, (2) enrichment for similarly localized proteins that physically interact with party hubs, (3) a lower rate of evolution of party hubs, (4) differences in the effects of deletion of date and party hubs, or (5) higher genetic connectivity of date hubs. In sum, all of our prior conclusions remain robust and there is no evidence for distinctive classes of network hubs. It was suggested [1] that some hub proteins operate at the same intracellular place and time with their multiple interactants (as if at a party) while others operate on a one-by-one basis with their numerous partners (as if on a date). Is this distinction informative? Originally, four features were used to the draw a partition between date and party hubs: expression bimodality, localization entropy, network fragmentation, and genetic connectivity [1]. A subsequent analysis suggested a fifth distinction, namely different rates of evolution after control for covariables [3]. Given the small size of the original dataset and the absence of statistical support for some of the assertions, we asked [2] whether these claims were robust. In both the original dataset and in new high-confidence interaction datasets [2], we found we could not support any of the five points of evidence. Bertin et al. now nominate a new dataset, which they argue supports three of the five points of evidence. Bertin et al. first note a curation issue with one of our many datasets, called HC, which inadvertently contains interactions that were, owing to an ambiguity in the literature, supported by a single analysis. We certainly agree that inclusion of the data from [4] and [5] as independent validations was in error, as the data in [5] indeed fully encompasses that of [4] (A-C Gavin, personal communication). However, approximately half of the interactions reported in [4] remain multi-validated by other means. An updated high-confidence dataset that removes this duplication and incorporates more recent interaction data is available here (Dataset S1) and as a download from the BioGRID database (see http://www.thebiogrid.org). Importantly, however, our dataset HCm is unaffected by the above concern as we required validation of an interaction by multiple different methods. The new build of Bertin et al. (called “filtered-HC”) mimics HCm by excluding interactions not multivalidated with different methods. As the results of HCm confirmed those of our other datasets [2], we were surprised by the claim that the date/party distinction is still supported in filtered-HC. Because this dataset provides the most robustly defendable set of interactions, here we re-analyse the filtered-HC network to ask whether it substantiates the date/party distinction.

A structural approach for finding functional modules from large biological networks

Abstract: Background Biological systems can be modeled as complex network systems with many interactions between the components. These interactions give rise to the function and behavior of that system. For example, the protein-protein interaction network is the physical basis of multiple cellular functions. One goal of emerging systems biology is to analyze very large complex biological networks such as protein-protein interaction networks, metabolic networks, and regulatory networks to identify functional modules and assign functions to certain components of the system. Network modules do not occur by chance, so identification of modules is likely to capture the biologically meaningful interactions in large-scale PPI data. Unfortunately, existing computer-based clustering methods developed to find those modules are either not so accurate or too slow.

Large Scale Statistical Prediction of Protein-Protein Interaction by Potentially Interacting Domain (PID) Pair

Abstract: Protein-protein interaction plays a critical role in biological processes. The identication of interacting proteins by computational methods can provide new leads in functional studies of uncharacterized proteins without performing extensive experiments. We developed a database for the potentially interacting domain pairs (PID) extracted from a dataset of experimentally identied interacting protein pairs (DIP: database of interacting proteins) with InterPro, an integrated database of protein families, domains and functional sites. In developing protein interaction databases and predictive methods, sensitive statistical scoring systems is critical to provide a reliability index for accurate functional analysis of interaction networks. We present a statistical scoring system, named \PID matrix score" as a measure of the interaction probability (interactability) between domains. This system provided a valuable tool for functional prediction of unknown proteins. For the evaluation of PID matrix, cross validation was performed with subsets of DIP data. The prediction system gives about 50% sensitivity and more than 98% specicit y, which implies that the information for interacting proteins pairs could be enriched about 30 fold with the PID matrix. It is demonstrated that mapping of the genome-wide interaction network can be achieved by using the PID matrix.

Multifunctional proteins revealed by overlapping clustering in protein interaction network

Abstract: Motivation: Multifunctional proteins perform several functions. They are expected to interact specifically with distinct sets of partners, simultaneously or not, depending on the function performed. Current graph clustering methods usually allow a protein to belong to only one cluster, therefore impeding a realistic assignment of multifunctional proteins to clusters. Results: Here, we present Overlapping Cluster Generator (OCG), a novel clustering method which decomposes a network into overlapping clusters and which is, therefore, capable of correct assignment of multifunctional proteins. The principle of OCG is to cover the graph with initial overlapping classes that are iteratively fused into a hierarchy according to an extension of Newman's modularity function. By applying OCG to a human protein–protein interaction network, we show that multifunctional proteins are revealed at the intersection of clusters and demonstrate that the method outperforms other existing methods on simulated graphs and PPI networks. Availability: This software can be downloaded from http://tagc.univ-mrs.fr/welcome/spip.php?rubrique197 Contact: brun@tagc.univ-mrs.fr Supplementary information:Supplementary data are available at Bioinformatics online.

PID: The Pathway Interaction Database

Abstract: The Pathway Interaction Database (PID, "http://pid.nci.nih.gov":http://pid.nci.nih.gov) is a freely available collection of curated and peer-reviewed pathways composed of human molecular signaling and regulatory events and key cellular processes. Created in a collaboration between the U.S. National Cancer Institute and Nature Publishing Group, the database serves as a research tool for the cancer research community and others interested in cellular pathways, such as neuroscientists, developmental biologists, and immunologists. PID offers a range of search features to facilitate pathway exploration. Users can browse the predefined set of pathways or create interaction network maps centered on a single molecule or cellular process of interest. In addition, the batch query tool allows users to upload long list(s) of molecules, such as those derived from microarray experiments, and either overlay these molecules onto predefined pathways or visualize the complete molecular connectivity map. Users can also download molecule lists, citation lists and complete database content in extensible markup language (XML) and Biological Pathways Exchange (BioPAX) Level 2 format. The database is updated with new pathway content every month and supplemented by specially commissioned articles on the practical uses of other relevant online tools.