Graphlet Based Metrics for the Comparison of Gene Regulatory Networks.
Understanding the control of gene expression remains one of the main challenges in the post-genomic era. Accordingly, a plethora of methods exists to identify variations in gene expression levels. These variations underlay almost all relevant biological phenomena, including disease and adaptation to environmental conditions. However, computational tools to identify how regulation changes are scarce. Regulation of gene expression is usually depicted in the form of a gene regulatory network (GRN). Structural changes in a GRN over time and conditions represent variations in the regulation of gene expression. Like other biological networks, GRNs are composed of basic building blocks called graphlets. As a consequence, two new metrics based on graphlets are proposed in this work: REConstruction Rate (REC) and REC Graphlet Degree (RGD). REC determines the rate of graphlet similarity between different states of a network and RGD identifies the subset of nodes with the highest topological variation. In other words, RGD discerns how th GRN was rewired. REC and RGD were used to compare the local structure of nodes in condition-specific GRNs obtained from gene expression data of Escherichia coli, forming biofilms and cultured in suspension. According to our results, most of the network local structure remains unaltered in the two compared conditions. Nevertheless, changes reported by RGD necessarily imply that a different cohort of regulators (i.e. transcription factors (TFs)) appear on the scene, shedding light on how the regulation of gene expression occurs when E. coli transits from suspension to biofilm. Consequently, we propose that both metrics REC and RGD should be adopted as a quantitative approach to conduct differential analyses of GRNs. A tool that implements both metrics is available as an on-line web server (http://dlab.cl/loto).
Computational Biology Lab, Fundación Ciencia & Vida, Santiago, Chile.|Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Chile.
Alberto J M
AJ
Martin
Computational Biology Lab, Fundación Ciencia & Vida, Santiago, Chile.|Center for Bioinformatics and Genome Biology, Fundación Ciencia & Vida and Facultad de Ciencias Biologicas, Universidad Andres Bello, Santiago, Chile.
Calixto
C
Dominguez
Computational Biology Lab, Fundación Ciencia & Vida, Santiago, Chile.
Sebastián
S
Contreras-Riquelme
Center for Bioinformatics and Genome Biology, Fundación Ciencia & Vida and Facultad de Ciencias Biologicas, Universidad Andres Bello, Santiago, Chile.
David S
DS
Holmes
Computational Biology Lab, Fundación Ciencia & Vida, Santiago, Chile.|Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Chile.
Tomas
T
Perez-Acle
D000465:Algorithms; D018441:Biofilms / Q000254:growth & development; D019295:Computational Biology / Q000379:methods*; D004926:Escherichia coli / Q000235:genetics / Q000254:growth & development; D020869:Gene Expression Profiling / Q000379:methods* / Q000706:statistics & numerical data; D015964:Gene Expression Regulation, Bacterial / Q000235:genetics; D053263:Gene Regulatory Networks; D008954:Models, Biological; D014157:Transcription Factors / Q000235:genetics
D014157:Transcription Factors
Bioinformatics. 2015 Aug 15;31(16):2697-704
25810431
Cell Death Dis. 2016 Jan 14;7:e2040
26775695
Biotechnol Adv. 2012 Jan-Feb;30(1):99-107
21741466
Science. 2002 Oct 25;298(5594):824-7
12399590
Science. 1995 Oct 20;270(5235):467-70
7569999
FEBS Lett. 2005 Aug 29;579(21):4642-6
16095595
PLoS Biol. 2004 Nov;2(11):e369
15510229
Phys Rev E Stat Nonlin Soft Matter Phys. 2004 Dec;70(6 Pt 2):066117
15697444
Genome Res. 2003 Nov;13(11):2498-504
14597658
J R Soc Interface. 2013 Jan 6;10(78):20120574
23034351
Science. 2004 Aug 20;305(5687):1107; author reply 1107
15326338
Nat Rev Genet. 2007 Jun;8(6):450-61
17510665
Science. 2008 Mar 28;319(5871):1785-6
18369135
Nat Genet. 2007 May;39(5):683-7
17417638
Environ Microbiol. 2007 Feb;9(2):332-46
17222132
PLoS One. 2014 Dec 08;9(12):e114503
25486535
Bioinformatics. 2007 Jan 15;23(2):e177-83
17237089
Nat Genet. 2002 May;31(1):64-8
11967538
Bioinformatics. 2014 Jun 15;30(12):1721-9
24554629
Biosystems. 2008 Oct-Nov;94(1-2):68-74
18611431
Nat Genet. 2004 May;36(5):486-91
15107854
Nature. 2014 Aug 28;512(7515):453-6
25164757
Stat Appl Genet Mol Biol. 2004;3:Article3
16646809
Proc Natl Acad Sci U S A. 2005 Apr 5;102(14):4936-42
15788537
Nucleic Acids Res. 2011 Jan;39(Database issue):D583-90
21097882
Science. 2004 Feb 27;303(5662):1378-81
14988562
Nucleic Acids Res. 2013 Jan;41(Database issue):D203-13
23203884
Sci Rep. 2014 Apr 01;4:4547
24686408
BMC Bioinformatics. 2015 Oct 09;16:327
26453322
Proc Natl Acad Sci U S A. 2002 Aug 6;99(16):10555-60
12145321
Proteomics. 2015 Jan;15(2-3):608-17
25283527
Mol Biosyst. 2009 Dec;5(12):1617-29
19763330
Gene Regul Syst Bio. 2008 May 15;2:193-201
19787083
Bioinformatics. 2015 Jun 15;31(12):i171-80
26072480
Science. 2002 Mar 1;295(5560):1669-78
11872831
Proc Natl Acad Sci U S A. 2001 Apr 24;98(9):5116-21
11309499
Nat Genet. 2003 Oct;35(2):176-9
12973352
Genes Brain Behav. 2014 Jan;13(1):13-24
24320616
J Theor Biol. 2008 Aug 21;253(4):717-24
18541271
BMC Syst Biol. 2012 Aug 16;6:102
22898448
Bioinformatics. 2004 Dec 12;20(18):3508-15
15284103
PLoS Comput Biol. 2015 Sep 22;11(9):e1004504
26393364
Exp Biol Med (Maywood). 2011 May 1;236(5):628-36
21540249
Bioinformatics. 2008 Jan 15;24(2):282-4
18006545