Display options
Cite
Padi M, Quackenbush J. Detecting phenotype-driven transitions in regulatory network structure. NPJ Syst Biol Appl. 2018;4:16doi: 10.1038/s41540-018-0052-5.
Padi, M., & Quackenbush, J. (2018). Detecting phenotype-driven transitions in regulatory network structure. NPJ systems biology and applications, 416. https://doi.org/10.1038/s41540-018-0052-5
Padi, Megha, and Quackenbush, John. "Detecting phenotype-driven transitions in regulatory network structure." NPJ systems biology and applications vol. 4 (2018): 16. doi: https://doi.org/10.1038/s41540-018-0052-5
Padi M, Quackenbush J. Detecting phenotype-driven transitions in regulatory network structure. NPJ Syst Biol Appl. 2018 Apr 19;4:16. doi: 10.1038/s41540-018-0052-5. eCollection 2018. PMID: 29707235; PMCID: PMC5908977.
Copy Download .nbib Format: NLM
Share it on

NPJ Syst Biol Appl. 2018 Apr 19;4:16. doi: 10.1038/s41540-018-0052-5. eCollection 2018.

Detecting phenotype-driven transitions in regulatory network structure.

NPJ systems biology and applications

Megha Padi, John Quackenbush

Affiliations

  1. 1Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ USA.
  2. 2Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA USA.
  3. 3Department of Biostatistics, Harvard School of Public Health, Boston, MA USA.

PMID: 29707235 PMCID: PMC5908977 DOI: 10.1038/s41540-018-0052-5

Abstract

Complex traits and diseases like human height or cancer are often not caused by a single mutation or genetic variant, but instead arise from functional changes in the underlying molecular network. Biological networks are known to be highly modular and contain dense "communities" of genes that carry out cellular processes, but these structures change between tissues, during development, and in disease. While many methods exist for inferring networks and analyzing their topologies separately, there is a lack of robust methods for quantifying differences in network structure. Here, we describe ALPACA (ALtered Partitions Across Community Architectures), a method for comparing two genome-scale networks derived from different phenotypic states to identify condition-specific modules. In simulations, ALPACA leads to more nuanced, sensitive, and robust module discovery than currently available network comparison methods. As an application, we use ALPACA to compare transcriptional networks in three contexts: angiogenic and non-angiogenic subtypes of ovarian cancer, human fibroblasts expressing transforming viral oncogenes, and sexual dimorphism in human breast tissue. In each case, ALPACA identifies modules enriched for processes relevant to the phenotype. For example, modules specific to angiogenic ovarian tumors are enriched for genes associated with blood vessel development, and modules found in female breast tissue are enriched for genes involved in estrogen receptor and ERK signaling. The functional relevance of these new modules suggests that not only can ALPACA identify structural changes in complex networks, but also that these changes may be relevant for characterizing biological phenotypes.

Conflict of interest statement

The authors declare no competing financial interests.

References

  1. Nature. 2012 Sep 6;489(7414):91-100 - PubMed
  2. Proc Natl Acad Sci U S A. 2008 Jan 29;105(4):1118-23 - PubMed
  3. Science. 2010 May 14;328(5980):876-8 - PubMed
  4. PLoS Comput Biol. 2016 Jan 25;12(1):e1004714 - PubMed
  5. PLoS One. 2013 May 31;8(5):e64832 - PubMed
  6. Phys Rev E Stat Nonlin Soft Matter Phys. 2015 Dec;92(6):062825 - PubMed
  7. Sci Rep. 2012;2:336 - PubMed
  8. Science. 2002 Aug 30;297(5586):1551-5 - PubMed
  9. Bioinformatics. 2002;18 Suppl 1:S233-40 - PubMed
  10. Nat Methods. 2012 Jul 15;9(8):796-804 - PubMed
  11. PLoS Comput Biol. 2013;9(3):e1002955 - PubMed
  12. Phys Rev E Stat Nonlin Soft Matter Phys. 2010 Apr;81(4 Pt 2):046106 - PubMed
  13. PLoS Comput Biol. 2016 Sep 12;12 (9):e1005033 - PubMed
  14. Bioinformatics. 2013 Jul 15;29(14):1776-85 - PubMed
  15. Proc Natl Acad Sci U S A. 2014 Dec 23;111(51):18144-9 - PubMed
  16. Nat Methods. 2016 Apr;13(4):366-70 - PubMed
  17. Am J Clin Nutr. 2014 Nov;100(5):1344-51 - PubMed
  18. Nature. 2012 Jul 26;487(7408):491-5 - PubMed
  19. Nature. 2012 Sep 6;489(7414):57-74 - PubMed
  20. Nat Rev Genet. 2013 Oct;14(10):719-32 - PubMed
  21. Nat Genet. 2014 Nov;46(11):1173-86 - PubMed
  22. Nature. 2001 May 3;411(6833):41-2 - PubMed
  23. Phys Rev E Stat Nonlin Soft Matter Phys. 2007 Sep;76(3 Pt 2):036106 - PubMed
  24. Science. 2015 Feb 20;347(6224):1257601 - PubMed
  25. Phys Rev E Stat Nonlin Soft Matter Phys. 2006 Jul;74(1 Pt 2):016110 - PubMed
  26. BMC Bioinformatics. 2006 Nov 20;7:509 - PubMed
  27. Stat Appl Genet Mol Biol. 2004;3:Article3 - PubMed
  28. BMC Bioinformatics. 2010 Oct 06;11:497 - PubMed
  29. Nature. 2016 Aug 4;536(7614):41-47 - PubMed
  30. Mol Syst Biol. 2012 Jan 17;8:565 - PubMed
  31. Phys Rev E Stat Nonlin Soft Matter Phys. 2004 Feb;69(2 Pt 2):026113 - PubMed
  32. Proc Natl Acad Sci U S A. 2003 Oct 14;100(21):12123-8 - PubMed
  33. Nature. 2002 Jul 25;418(6896):387-91 - PubMed
  34. BMC Bioinformatics. 2016 Jan 05;17:18 - PubMed
  35. Phys Rev E Stat Nonlin Soft Matter Phys. 2009 Sep;80(3 Pt 2):036115 - PubMed
  36. Proc Natl Acad Sci U S A. 2007 Jan 2;104(1):36-41 - PubMed
  37. Nature. 1999 Dec 2;402(6761 Suppl):C47-52 - PubMed
  38. PLoS One. 2016 Mar 09;11(3):e0151134 - PubMed
  39. BMC Bioinformatics. 2015 Apr 11;16:115 - PubMed
  40. BMC Syst Biol. 2015 Nov 14;9:80 - PubMed
  41. Cell. 2015 Apr 23;161(3):647-660 - PubMed
  42. Nature. 2015 Feb 12;518(7538):197-206 - PubMed
  43. PLoS One. 2012;7(2):e30269 - PubMed
  44. BMC Bioinformatics. 2010 Feb 19;11:95 - PubMed
  45. Int J Cancer. 2009 Apr 15;124(8):1918-25 - PubMed
  46. J R Soc Interface. 2014 Feb 26;11(94):20130908 - PubMed
  47. Curr Oncol. 2010 Oct;17(5):6-11 - PubMed
  48. Cell Rep. 2017 Oct 24;21(4):1077-1088 - PubMed
  49. Sci Rep. 2015 Sep 23;5:14339 - PubMed

Publication Types

Grant support