Use of data-biased random walks on graphs for the retrieval of context-specific networks from genomic data.
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.
Komurov
Kakajan
K
Department of Systems Biology, University of Texas M.D. Anderson Cancer Center, Houston, Texas, United States of America. kkomurov@mdanderson.org
White
Michael A
MA
Ram
Prahlad T
PT
D000465:Algorithms; D017209:Apoptosis / Q000187:drug effects / Q000235:genetics; D001943:Breast Neoplasms / Q000188:drug therapy / Q000235:genetics*; D002453:Cell Cycle / Q000187:drug effects / Q000235:genetics; D045744:Cell Line, Tumor; D019295:Computational Biology / Q000379:methods; D004317:Doxorubicin / Q000627:therapeutic use; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods; D053263:Gene Regulatory Networks; D016158:Genes, p53; D023281:Genomics / Q000379:methods* / Q000706:statistics & numerical data*; D006801:Humans; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis; D013269:Stochastic Processes
D004317:Doxorubicin
Science. 2006 Jun 16;312(5780):1650-3
16728594
Nature. 2005 Oct 13;437(7061):1032-7
16136080
Nat Genet. 2007 Jan;39(1):41-51
17173048
Drug Discov Today. 2005 May 1;10(9):653-62
15894230
Nat Methods. 2009 Jul;6(7):476-7
19564846
Nucleic Acids Res. 2000 Jan 1;28(1):316-9
10592259
Nat Genet. 2003 Jun;34(2):166-76
12740579
Nat Genet. 2000 May;25(1):25-9
10802651
Science. 2005 Apr 22;308(5721):523-9
15845847
Pharmacogenomics. 2008 Jan;9(1):35-54
18154447
Nucleic Acids Res. 2007 Jan;35(Database issue):D561-5
17145710
Methods Mol Biol. 2007;356:319-50
16988414
Comp Funct Genomics. 2004;5(2):163-8
18629064
Nat Protoc. 2007;2(10):2366-82
17947979
Nucleic Acids Res. 2007 Jan;35(Database issue):D572-4
17135203
PLoS Comput Biol. 2008 Nov;4(11):e1000217
18989396
Proc Natl Acad Sci U S A. 2005 Oct 25;102(43):15545-50
16199517
Proc Natl Acad Sci U S A. 2008 Jan 29;105(4):1118-23
18216267
Mol Syst Biol. 2007;3:140
17940530
BMC Syst Biol. 2009 Mar 23;3:36
19309513
Curr Protoc Bioinformatics. 2004 May;Chapter 7:Unit 7.5
18428731
Nucleic Acids Res. 2006 Jan 1;34(Database issue):D411-4
16381900
Mol Syst Biol. 2008;4:216
18766176
Nat Protoc. 2007;2(12):3111-4
18079710
Nucleic Acids Res. 2007 Jan;35(Database issue):D26-31
17148475
Cancer Cell. 2006 Dec;10(6):515-27
17157791
Drug Discov Today. 2004 Feb 1;9(3):127-35
14960390
Nucleic Acids Res. 2000 Jan 1;28(1):27-30
10592173
Nature. 2008 Sep 18;455(7211):401-5
18724358
Nucleic Acids Res. 2008 Jan;36(Database issue):D107-13
18006570
Nucleic Acids Res. 2001 Jan 1;29(1):242-5
11125103
Methods Mol Biol. 2006;338:175-90
16888359
Bioinformatics. 2009 May 1;25(9):1158-64
19297352