The interactome as a tree--an attempt to visualize the protein-protein interaction network in yeast

Abstract

The refinement and high-throughput of protein interaction detection methods offer us a protein-protein interaction network in yeast. The challenge coming along with the network is to find better ways to make it accessible for biological investigation. Visualization would be helpful for extraction of meaningful biological information from the network. However, traditional ways of visualizing the network are unsuitable because of the large number of proteins. Here, we provide a simple but information-rich approach for visualization which integrates topological and biological information. In our method, the topological information such as quasi-cliques or spoke-like modules of the network is extracted into a clustering tree, where biological information spanning from protein functional annotation to expression profile correlations can be annotated onto the representation of it. We have developed a software named PINC based on our approach. Compared with previous clustering methods, our clustering method ADJW performs well both in retaining a meaningful image of the protein interaction network as well as in enriching the image with biological information, therefore is more suitable in visualization of the network.

The interactome: Predicting the protein-protein interactions in cells

“…Pairs of sequences homologous to a known interacting pair are then scored for how well they preserve the atomic contacts at the interaction interface [153][154][155]. Up to now, all the computational algorithms have only used single machine learning methods for the analysis and prediction of protein-protein interactions [156][157][158][159][160], or the statistical analysis of interacting patches of protein surfaces [75,149,161,162]. Our experience clearly supports the idea that each machine learning algorithm performs better for selected types of training data [163,164].…”

A Graph-Theoretic Method for Mining Functional Modules in Large Sparse Protein Interaction Networks

“…Pre-processed interaction data for yeast Sacchromyces cerevisiae is obtained from [6] where the data is further collected from the MIPS (http://mips.gsf.de/), PreBIND (http://www.blueprint.org /products/prebind/index.html), BIND (http:// bind.ca/), GRID (http://biodata.mshri.on.ca/grid/ servlet/Index) and the spoke model data [15]. Fly and worm PPI datasets are obtained from [16,17] respectively.…”

“…Hierarchical clustering methods have been proven to be a good strategy for metabolic networks and PPI networks. Ravasz et al [3] analyzed the hierarchical organization of modularity in metabolic networks, and authors of [4][5][6] applied three different clustering methods respectively, based on different metrics induced by shortest-distance, graphical distances, and probabilistic functions, to analyze the module structure of the yeast protein interaction networks on a clustering tree. Several papers [2,7,8] have also shown that network modules which are densely connected within themselves but sparsely connected with the rest of network generally correspond to meaningful biological units such as protein complexes and functional modules.…”

Protein Interactome and Its Application to Protein Function Prediction

“…Although many methods in the similarity measures have been proposed, a single validation for such methods is insufficient. For this, two evaluation schema are suggested, which are based on the depth of a hierarchical tree and width of the ordered adjacency matrix (Lu et al, 2004). Furthermore, there are various types of cellular network with distinct modular patterns, and so network-specific methods should be investigated in the future.…”