Graphical Analysis and Visualization Tools for Protein Interaction Networks

Graphical Analysis and Visualization Tools for Protein Interaction Networks

Sirisha Gollapudi (MyCIB and University of Nottingham, UK), Alex Marshall (MyCIB and University of Nottingham, UK), Daniel Zadik (MyCIB and University of Nottingham, UK) and Charlie Hodgman (MyCIB and University of Nottingham, UK)
Copyright: © 2009 |Pages: 26
DOI: 10.4018/978-1-60566-398-2.ch016
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Software for the visualization and analysis of protein-protein interaction (PPI) networks can enable general exploration, as well as providing graph-theoretic algorithms for specific tasks. Analyses can include reduction of complexity or the scope of the network in order to make it more manageable, or increase in complexity by integration with other datasets, to represent biology more accurately. Two software approaches are outlined in this chapter: desktop applications and web services. Desktop applications have attractive user interfaces with a wide range of analysis tools, and often capabilities for integration of other bio-molecular data. Web services provide a newer approach to network analysis. They have the advantages of a broader range of potential functionalities and a more extensible framework than standalone desktop tools. However, their relative infancy means that they are not as well developed. This chapter provides an evaluation of some common desktop applications, compared to and contrasted with several examples of web services.
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* Joint first authors



In recent years, a predominant theme in biological research has been a shift from the reductionist approach to an integrative systems approach (Kitano et al., 2002; Oltvai & Barabási, 2002; Han, 2008), which views the components of a cell as acting together in a network of reactions and interactions. This change has been aspired to for many years, as it is widely understood that observed cell behaviour is rarely attributed to one component acting alone. The continued growth in the processing power of computers and the increase in availability of biological data from high throughput technologies have allowed such an approach to become realistic.

This chapter describes software that uses algorithms taken from the field of graph theory to draw biological conclusions about protein-protein interaction (PPI) networks. It contains background on the subject, a method for the representation of networks, including bio-molecular networks, as a set of nodes connected by edges. In PPI networks, the nodes represent proteins, while the edges represent the interactions between them. Very often, PPI networks aim to represent the whole proteome of a species. As an example, Figure 1 shows a holistic PPI network for the budding yeast, Saccharomyces cerevisiae (Schwikowski, Uetz & Fields, 2000). Holistic PPI networks are usually constructed from two-hybrid screening (Fields & Song, 1989) and other experimental data, much of which is available in public databases (see Background).

Figure 1.

An S. cerevisiae holistic PPI network, containing 1548 proteins and 2358 interactions, from a study carried out by Schwikowski, Uetz and Fields, (2000). (Permission to reproduce figure kindly provided by Peter Uetz, Institute of Genetics, University of Karlsruhe.)

Two-hybrid technology is particularly powerful as it identifies large numbers of putative interactions. To test if two proteins interact, the DNA-binding domain from a transcription factor is spliced onto the end of the gene for one, and the activating domain from the transcription factor is spliced onto the other. When the genes are transformed into a cell (usually in S. cerevisiae or E. coli) and expressed, two hybrid proteins are produced. The protein with the DNA-binding domain binds to an upstream activating sequence (UAS) of a reporter gene and the protein with the activating domain binds the remaining transcriptional machinery. If the two proteins bind each other then the transcriptional machinery will be brought into close proximity with the UAS, and the reporter gene will be expressed. Whole libraries of genes can be transformed into a cell suspension, to create a population with a very diverse set of combinations of hybrid genes. If a reporter gene is used that is essential for survival under particular conditions, then those conditions can be used to select only those cells that contain a pair of interacting hybrid proteins, and the genes from these can then be sequenced to identify them. The artificial context in which the interactions take place can result in both false positive and false negative results. For example, animal proteins may fold incorrectly in a yeast cell, or proteins that can interact but would never come into contact in vivo (because they are found in different cellular compartments or at different times) are reported as interacting.

It is often desirable to visualise PPI networks in order to see their structure and therefore provide further insight into the system. Visualizations of large networks are often complicated and dense, making it impossible to discern structure by eye. However, the use of analysis techniques can help elucidate structure and therefore function. The results of such analyses may be interesting in their own right, or may lead to further visualization steps on selected subsets or simplifications of the network. For these reasons, the software for visualising networks usually contains various network analysis functions.

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Editorial Advisory Board
Table of Contents
Xiao-Li Li, See-Kiong Ng
Chapter 1
Christian Schönbach
Advances in protein-protein interaction (PPI) detection technology and computational analysis methods have produced numerous PPI networks, whose... Sample PDF
Molecular Biology of Protein-Protein Interactions for Computer Scientists
Chapter 2
Koji Tsuda
In this tutorial chapter, the author reviews basics about frequent pattern mining algorithms, including itemset mining, association rule mining, and... Sample PDF
Data Mining for Biologists
Chapter 3
Tatsuya Akutsu, Morihiro Hayashida
Many methods have been proposed for inference of protein-protein interactions from protein sequence data. This chapter focuses on methods based on... Sample PDF
Domain-Based Prediction and Analysis of Protein-Protein Interactions
Chapter 4
Martin S.R. Paradesi, Doina Caragea, William H. Hsu
This chapter presents applications of machine learning to predicting protein-protein interactions (PPI) in Saccharomyces cerevisiae. Several... Sample PDF
Incorporating Graph Features for Predicting Protein-Protein Interactions
Chapter 5
David La, Daisuke Kihara
This chapter gives a comprehensive introduction of the sequence/structural features that are characteristic of protein- protein interaction sites... Sample PDF
Discovering Protein-Protein Interaction Sites from Sequence and Structure
Chapter 6
Paolo Marcatili, Anna Tramontano
This chapter provides an overview of the current computational methods for PPI network cleansing. The authors first present the issue of identifying... Sample PDF
Network Cleansing: Reliable Interaction Networks
Chapter 7
Hugo Willy
Recent breakthroughs in high throughput experiments to determine protein-protein interaction have generated a vast amount of protein interaction... Sample PDF
Discovering Interaction Motifs from Protein Interaction Networks
Chapter 8
Raymond Wan, Hiroshi Mamitsuka
This chapter examines some of the available techniques for analyzing a protein interaction network (PIN) when depicted as an undirected graph.... Sample PDF
Discovering Network Motifs in Protein Interaction Networks
Chapter 9
Clara Pizzuti, Simona Ester Rombo
In this chapter a survey on the main graph-based clustering techniques proposed in the literature to mine proteinprotein interaction networks (PINs)... Sample PDF
Discovering Protein Complexes in Protein Interaction Networks
Chapter 10
Takashi Makino, Aoife McLysaght
This chapter introduces evolutionary analyses of protein interaction networks and of proteins as components of the networks. The authors show... Sample PDF
Evolutionary Analyses of Protein Interaction Networks
Chapter 11
Kar Leong Tew, Xiao-Li Li
This chapter introduces state-of-the-art computational methods which discover lethal proteins from Protein Interaction Networks (PINs). Lethal... Sample PDF
Discovering Lethal Proteins in Protein Interaction Networks
Chapter 12
Hon Nian Chua, Limsoon Wong
Functional characterization of genes and their protein products is essential to biological and clinical research. Yet, there is still no reliable... Sample PDF
Predicting Protein Functions from Protein Interaction Networks
Chapter 13
Pablo Minguez, Joaquin Dopazo
Here the authors review the state of the art in the use of protein-protein interactions (ppis) within the context of the interpretation of genomic... Sample PDF
Protein Interactions for Functional Genomics
Chapter 14
Xiaoyue Zhao, Lilia M. Iakoucheva, Michael Q. Zhang
Genetic factors play a major role in the etiology of many human diseases. Genome-wide experimental methods produce an increasing number of genes... Sample PDF
Prioritizing Disease Genes and Understanding Disease Pathways
Chapter 15
Smita Mohanty, Shashi Bhushan Pandit, Narayanaswamy Srinivasan
Integration of organism-wide protein interactome data with information on expression of genes, cellular localization of proteins and their functions... Sample PDF
Dynamics of Protein-Protein Interaction Network in Plasmodium Falciparum
Chapter 16
Sirisha Gollapudi, Alex Marshall, Daniel Zadik, Charlie Hodgman
Software for the visualization and analysis of protein-protein interaction (PPI) networks can enable general exploration, as well as providing... Sample PDF
Graphical Analysis and Visualization Tools for Protein Interaction Networks
Chapter 17
Valeria Fionda, Luigi Palopoli
The aim of this chapter is that of analyzing and comparing network querying techniques as applied to protein interaction networks. In the last few... Sample PDF
Network Querying Techniques for PPI Network Comparison
Chapter 18
Tero Aittokallio
This chapter provides an overview of the computational approaches developed for exploring the modular organization of protein interaction networks.... Sample PDF
Module Finding Approaches for Protein Interaction Networks
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