Hubba: hub objects analyzer--a framework of interactome hubs identification for network biology

Abstract

One major task in the post-genome era is to reconstruct proteomic and genomic interacting networks using high-throughput experiment data. To identify essential nodes/hubs in these interactomes is a way to decipher the critical keys inside biochemical pathways or complex networks. These essential nodes/hubs may serve as potential drug-targets for developing novel therapy of human diseases, such as cancer or infectious disease caused by emerging pathogens. Hub Objects Analyzer (Hubba) is a web-based service for exploring important nodes in an interactome network generated from specific small- or large-scale experimental methods based on graph theory. Two characteristic analysis algorithms, Maximum Neighborhood Component (MNC) and Density of Maximum Neighborhood Component (DMNC) are developed for exploring and identifying hubs/essential nodes from interactome networks. Users can submit their own interaction data in PSI format (Proteomics Standards Initiative, version 2.5 and 1.0), tab format and tab with weight values. User will get an email notification of the calculation complete in minutes or hours, depending on the size of submitted dataset. Hubba result includes a rank given by a composite index, a manifest graph of network to show the relationship amid these hubs, and links for retrieving output files. This proposed method (DMNC || MNC) can be applied to discover some unrecognized hubs from previous dataset. For example, most of the Hubba high-ranked hubs (80% in top 10 hub list, and >70% in top 40 hub list) from the yeast protein interactome data (Y2H experiment) are reported as essential proteins. Since the analysis methods of Hubba are based on topology, it can also be used on other kinds of networks to explore the essential nodes, like networks in yeast, rat, mouse and human. The website of Hubba is freely available at http://hub.iis.sinica.edu.tw/Hubba.

Figure 1.

Inside Hubba. In ‘user mode’ (top), user submits PPI dataset in PSI format, tabulated format with/without weight values and fill the note for this submission on the input page. After the calculation, user will receive the notification to views the result on the result display page. The result will be included general properties of submitted network, list of hub proteins with the graph of interaction among them, Cytoscape's format output and lists of protein ranking scores by several algorithms. In ‘system mode’ (bottom), HubbaD controls the entire process and calls programs to process input data, send notification to users and generate output files.

Figure 2.

The top10 nodes of the dataset yeast20070107.lst explored in Hubba. A coloring scheme is used to display the ranking score of each node. When the node is redder, the ranking of it will be higher. Solid lines indicate the connected nodes interact to each other (direct connections), while dotted lines with a number indicate the shortest path (distance) between two linking nodes (indirect connections).