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      Use of Data-Biased Random Walks on Graphs for the Retrieval of Context-Specific Networks from Genomic Data

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          Abstract

          Extracting network-based functional relationships within genomic datasets is an important challenge in the computational analysis of large-scale data. Although many methods, both public and commercial, have been developed, the problem of identifying networks of interactions that are most relevant to the given input data still remains an open issue. Here, we have leveraged the method of random walks on graphs as a powerful platform for scoring network components based on simultaneous assessment of the experimental data as well as local network connectivity. Using this method, NetWalk, we can calculate distribution of Edge Flux values associated with each interaction in the network, which reflects the relevance of interactions based on the experimental data. We show that network-based analyses of genomic data are simpler and more accurate using NetWalk than with some of the currently employed methods. We also present NetWalk analysis of microarray gene expression data from MCF7 cells exposed to different doses of doxorubicin, which reveals a switch-like pattern in the p53 regulated network in cell cycle arrest and apoptosis. Our analyses demonstrate the use of NetWalk as a valuable tool in generating high-confidence hypotheses from high-content genomic data.

          Author Summary

          Analysis of high-content genomic data within the context of known networks of interactions of genes can lead to a better understanding of the underlying biological processes. However, finding the networks of interactions that are most relevant to the given data is a challenging task. We present a random walk-based algorithm, NetWalk, which integrates genomic data with networks of interactions between genes to score the relevance of each interaction based on both the data values of the genes as well as their local network connectivity. This results in a distribution of Edge Flux values, which can be used for dynamic reconstruction of user-defined networks. Edge Flux values can be further subjected to statistical analyses such as clustering, allowing for direct numerical comparisons of context-specific networks between different conditions. To test NetWalk performance, we carried out microarray gene expression analysis of MCF7 cells subjected to lethal and sublethal doses of a DNA damaging agent. We compared NetWalk to other network-based analysis methods and found that NetWalk was superior in identifying coherently altered sub-networks from the genomic data. Using NetWalk, we further identified p53-regulated networks that are differentially involved in cell cycle arrest and apoptosis, which we experimentally tested.

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          Most cited references26

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Integration of biological networks and gene expression data using Cytoscape.

            Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape.
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              Network-based classification of breast cancer metastasis

              Mapping the pathways that give rise to metastasis is one of the key challenges of breast cancer research. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with metastasis. Here, we apply a protein-network-based approach that identifies markers not as individual genes but as subnetworks extracted from protein interaction databases. The resulting subnetworks provide novel hypotheses for pathways involved in tumor progression. Although genes with known breast cancer mutations are typically not detected through analysis of differential expression, they play a central role in the protein network by interconnecting many differentially expressed genes. We find that the subnetwork markers are more reproducible than individual marker genes selected without network information, and that they achieve higher accuracy in the classification of metastatic versus non-metastatic tumors.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                August 2010
                August 2010
                19 August 2010
                : 6
                : 8
                : e1000889
                Affiliations
                [1 ]Department of Systems Biology, University of Texas M.D. Anderson Cancer Center, Houston, Texas, United States of America
                [2 ]Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
                University of Illinois at Urbana-Champaign, United States of America
                Author notes

                Conceived and designed the experiments: KK PTR. Performed the experiments: KK. Analyzed the data: KK. Contributed reagents/materials/analysis tools: MAW. Wrote the paper: KK PTR.

                Article
                10-PLCB-RA-1860R3
                10.1371/journal.pcbi.1000889
                2924243
                20808879
                006b4dc0-0ac7-47a0-905d-709148959cf6
                Komurov et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                : 26 February 2010
                : 15 July 2010
                Page count
                Pages: 10
                Categories
                Research Article
                Computational Biology/Genomics
                Computational Biology/Systems Biology

                Quantitative & Systems biology
                Quantitative & Systems biology

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