Prioritizing Disease Genes and Understanding Disease Pathways

Prioritizing Disease Genes and Understanding Disease Pathways

Xiaoyue Zhao (Bionovo Inc., USA), Lilia M. Iakoucheva (Rockefeller University, USA) and Michael Q. Zhang (Cold Spring Harbor Laboratory, USA)
Copyright: © 2009 |Pages: 18
DOI: 10.4018/978-1-60566-398-2.ch014
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Genetic factors play a major role in the etiology of many human diseases. Genome-wide experimental methods produce an increasing number of genes associated with such diseases. This chapter introduces data sources, bioinformatics tools, and computational methods for prioritizing disease candidate genes and identifying disease pathways. The main strategy is to examine the similarity among the candidate genes and known disease genes at the functional level. The authors review different similarity measures and prevailing methods for integrating results from different functional aspects. They hope this chapter will help advocate many useful resources that the researchers can use to investigate diseases of their interest.
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Genetic factors play a major role in the etiology of many diseases, including cancers and neurological disorders. Identifying genes that confer increased risk to the disease, and elucidating cellular and molecular processes in which these genes participate are very important problems in biomedical research. Genome-wide experimental methods, such as linkage, association (Botstein & Risch, 2003) and recently copy number variation (CNV) studies (McCarroll & Altshuler, 2007; Sebat, 2007), are all aimed at narrowing down genomic regions containing candidate disease genes. However, due to the linkage disequilibrium and the limited resolution of genome-wide technologies, the disease-associated regions could contain hundreds of candidate genes. The list of genes produced from such studies is constantly growing. The traditional one-gene-at-a-time approach is a time-consuming and expensive step to validate the disease-causing genes using experimental methods. Therefore it is of great importance and also a challenging task to use computational methods to prioritize disease gene candidates. Computational methods could greatly speed up the efforts directed towards elucidating disease mechanisms and ultimately translating genetic findings into effective prevention, diagnosis and treatment.

The recent availability of a large variety of genomic data and modern high-throughput technologies provide unique opportunities and complementary powerful resources for this purpose. Although disease-gene relationships are not simple (such as different diseases may be caused by mutations in the same gene, and the same disease may be caused by mutations in different genes), disease genes usually share at least some common characteristics including sequence features, expression patterns, involvement in the same protein-protein interaction sub-network, common gene ontology annotations, shared pathways and others (Goh et al., 2007; Oti & Brunner, 2007). For example, it was shown that genes involved in the same disease share up to 80% of their annotations in the GO and InterPro databases (Mulder et al., 2007). The similarity among disease genes is not restricted to the sequences and annotations; the similarity in their functions could also be noted. This leads to the main strategy in prioritizing disease genes, that is, to examine the similarity among candidate genes and known disease genes at the functional level (Han, 2008).

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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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