Selecting relevant nodes and structures in biological networks. BiNAT: a new plugin for Cytoscape

Background

In this section we briefly introduce the fundamental concepts that are needed to understand the principles of the software described in this article. We introduce the main theoretical concepts used to describe and analyze networks, most of which come from graph theory that is a large field containing many results and we describe only a small fraction of those here, focusing on the ones that are relevant to the study of real-world networks.

Software overview

The increasing availability of large network datasets, along with the progress in experimental high-throughput technologies, have promoted the need for tools that allow an easy integration of experimental data with data derived from network computational analysis. In order to enrich experimental data with network topological parameters, it has been developed the Cytoscape plugin BiNAT (Biological Network Analysis Tool).

The plugin computes several network centrality parameters, and allows the user to analyze these computational results in textual and graphical format. BiNAT identifies the nodes that are relevant from the experimental and the topological viewpoint. BiNAT is one of the few Cytoscape plugins that computes several centrality indices at once. In BiNAT the centrality measures can be easily correlated with each other, in order to identify the most significant nodes according to topological properties. Functional to this capability is the scatter plot options, which allows an easy correlation of node centralities.

Equally to other Cytoscape plugins, BiNAT (and its dependencies) must reside in the plugin directory in the Cytoscape root folder. BiNAT needs to be executed with administrative privileges to working proper. After first running, BiNAT creates three folders (binatData, binatLog and binatTemp) in the Cytoscape root directory:

Implementation

The idea behind the developed interface consists of a clear separation from the visualization of results and the Java classes that contain the processing logic. The structure is comparable to the MVC (Model - View - Controller) design pattern that is based on the separation of roles between the software components that interpret three major roles:

The Controller is the key class that guides the input information flow (commands and parameters) towards the right actions to be performed.

Implemented algorithms

In this section the algorithms implemented in BiNAT are introduced through a pseudocode and a comprehensive description for each algorithms. The following algorithms have been implemented: Dijkstra algorithm, Betweenness centrality, Closeness centrality, Degree centrality, Eccentricity centrality, Stress centrality and Clique Finder (Bron-Kerbosch) algorithm.

dijkstra (G, w, s)
1. initialize_single_source (G, s)
2. S <- { } // S will ultimately contains
    vertices of final shortest-path weights
    from s
3. initialize_property_queue Q
4. Q <-V[G]
5. while Q is not empty do
6.   u <- extract_min(Q) // pull out new
    vertex
7.   s <- S E’ {u} // perform relaxation for
    each vertex v adjacent to u
8.   foreach v in Adj[u] do
9.     relax(u, v, w)

bron_kerbosch_clique_finder (potential-clique,
    candidate, already-found)
1. if a node in already-found is connected to
    all nodes in candidates then
2.   no clique can ever be found (branch and
    bound step)
3. else
4.   foreach candidate-node in candidates do
5.      move candidate-node to
    potential-clique
6.      create new-candidates by removing
    nodes in candidates not connected to
    candidate-node
7.      create new-already-found by removing
    nodes in already-found not connected to
    candidate-node
8.      if new-candidate and
    new-already-found are empty
9.         potential-clique is a maximal-clique
10.     else
11.       bron_kerbosch_clique_finder
    (potential-clique, new-candidates,
    new-already-found)
12.     endif
13.     move candidate-node from
    potential-clique to already-found
14.  endfor
15. endif

Supported features

One of the most important commands in BiNAT is the one for the creation of the principal output file (in MS Excel or CSV format), in which all network nodes with all their own centrality measures defined before are listed: the ranking measure has been introduced to find a correlation between centrality measures that are in conflict with each other; it is a value ranging from 0 to 10, where highest value are received by nodes that are candidates to be considered hubs. This file creation step passes through almost all stages of operational calculus which constitute the heart of the software. The first step consists is the input of a network. The network must be in TXT format according to the TAB2 standard. The format supported until now is that adopted by BioGRID. To compute all nodes and network centrality measures, it is needed to type the “makestatistics” command in the plugin text field (please see the software manual on the official site for more information about available commands). BiNAT will then performs the following steps:

Data is ready to be returned in output now. BiNAT creates the output file in the folder specified at the beginning of the operation. To create XLSX files, BiNAT uses the Apache POI libraries: Java API for Microsoft Documents. The aim of the Java POI is to manipulate various file format based upon the Office Open XML standards (OOXML) and Microsoft’s OLE 2 Compound Document format (OLE2). In short, the programmer is able to read and write MS Excel, MS Word and MS PowerPoint files using Java.

For a list of all supported command line options, please take a look at the User Manual on the plugin official site. BiNAT also provides a server mode that allows the user to fully remotely control the plugin. When the server mode is enabled, user can send commands remotely using BiNAT client available as desktop and mobile application (for Android devices only).

BiNAT at work

We tested BiNAT on Saccharomyces Cerevisiae (Figure 1) protein-protein interaction network. PPI network was extracted from BioGRID database and consist of 6,096 nodes and 214,235 interactions.

Figure 1. S. Cerevisiae represented in Cytoscape with biological layout.

It was analyzed with information about yeast complexes provided by the Wodak Lab (Molecular Structure & Function program, Hospital for Sick Children, Toronto, ON, Canada). We first used BiNAT to compute all the centrality measures. A first overview of the global topological properties comes from the values of all centralities along with the diameter and the nodes average degree (Figure 2).

Figure 2. Overview of the global topological properties for S. Cerevisiae PPI network.

With this data and with the help of a graphical layout, we can deduce a highly connected network. The computation of network centrality measures allow us a first ranking of this network. Further analysis bases on a Gene Ontology database search, or by adding functional annotation data, may allow a deeper functional exploration of the network. The combination of BiNAT with other bioinformatics tools, such as CentiScaPe or Network Analyzer or MCODE, may help to analyze high-throughput genomic and or proteomic experimental data and facilitate the analysis process. It’s recommended to see the plugin User Manual for a step-by-step guide on how to use BiNAT and all its features.

Conclusions

The Cytoscape plugin BiNAT was designed to provide the user a powerful tool for an accurate analysis of networks centrality. The plugin interface is simple as a shell; a practical user manual can be downloaded from the official web site http://dmb.iasi.cnr.it/binat.php.

BiNAT plugin has been accepted by the Cytoscape community and is actually available for the download on the official plugins repository for the 2.8.3 version of Cytoscape.

Software availability