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Comparative analysis of module-based versus direct methods for reverse-engineering transcriptional regulatory networks

Tom Michoel UGent, Riet De Smet UGent, Anagha Madhusudan Joshi UGent, Yves Van de Peer UGent and Kathleen Marchal UGent (2009) BMC Systems Biology. 3(49). p.1-13
abstract
Background: A myriad of methods to reverse-engineer transcriptional regulatory networks have been developed in recent years. Direct methods directly reconstruct a network of pairwise regulatory interactions while module-based methods predict a set of regulators for modules of coexpressed genes treated as a single unit. To date, there has been no systematic comparison of the relative strengths and weaknesses of both types of methods. Results: We have compared a recently developed module-based algorithm, LeMoNe (Learning Module Networks), to a mutual information based direct algorithm, CLR (Context Likelihood of Relatedness), using benchmark expression data and databases of known transcriptional regulatory interactions for Escherichia coli and Saccharomyces cerevisiae. A global comparison using recall versus precision curves hides the topologically distinct nature of the inferred networks and is not informative about the specific subtasks for which each method is most suited. Analysis of the degree distributions and a regulator specific comparison show that CLR is 'regulator-centric', making true predictions for a higher number of regulators, while LeMoNe is 'target-centric', recovering a higher number of known targets for fewer regulators, with limited overlap in the predicted interactions between both methods. Detailed biological examples in E. coli and S. cerevisiae are used to illustrate these differences and to prove that each method is able to infer parts of the network where the other fails. Biological validation of the inferred networks cautions against over-interpreting recall and precision values computed using incomplete reference networks. Conclusion: Our results indicate that module-based and direct methods retrieve largely distinct parts of the underlying transcriptional regulatory networks. The choice of algorithm should therefore be based on the particular biological problem of interest and not on global metrics which cannot be transferred between organisms. The development of sound statistical methods for integrating the predictions of different reverse-engineering strategies emerges as an important challenge for future research.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
CELLS, ALGORITHMS, CEREVISIAE, YEAST, ORGANIZATION, GENE-EXPRESSION DATA, ESCHERICHIA-COLI
journal title
BMC Systems Biology
BMC Syst. Biol.
volume
3
issue
49
pages
1 - 13
Web of Science type
Article
Web of Science id
000266992000001
JCR category
MATHEMATICAL & COMPUTATIONAL BIOLOGY
JCR impact factor
4.064 (2009)
JCR rank
3/29 (2009)
JCR quartile
1 (2009)
ISSN
1752-0509
DOI
10.1186/1752-0509-3-49
language
English
UGent publication?
yes
classification
A1
copyright statement
I don't know the status of the copyright for this publication
id
749001
handle
http://hdl.handle.net/1854/LU-749001
date created
2009-09-15 11:57:10
date last changed
2013-09-16 15:24:49
@article{749001,
  abstract     = {Background: A myriad of methods to reverse-engineer transcriptional regulatory networks have been developed in recent years. Direct methods directly reconstruct a network of pairwise regulatory interactions while module-based methods predict a set of regulators for modules of coexpressed genes treated as a single unit. To date, there has been no systematic comparison of the relative strengths and weaknesses of both types of methods.

Results: We have compared a recently developed module-based algorithm, LeMoNe (Learning Module Networks), to a mutual information based direct algorithm, CLR (Context Likelihood of Relatedness), using benchmark expression data and databases of known transcriptional regulatory interactions for Escherichia coli and Saccharomyces cerevisiae. A global comparison using recall versus precision curves hides the topologically distinct nature of the inferred networks and is not informative about the specific subtasks for which each method is most suited. Analysis of the degree distributions and a regulator specific comparison show that CLR is 'regulator-centric', making true predictions for a higher number of regulators, while LeMoNe is 'target-centric', recovering a higher number of known targets for fewer regulators, with limited overlap in the predicted interactions between both methods. Detailed biological examples in E. coli and S. cerevisiae are used to illustrate these differences and to prove that each method is able to infer parts of the network where the other fails. Biological validation of the inferred networks cautions against over-interpreting recall and precision values computed using incomplete reference networks.

Conclusion: Our results indicate that module-based and direct methods retrieve largely distinct parts of the underlying transcriptional regulatory networks. The choice of algorithm should therefore be based on the particular biological problem of interest and not on global metrics which cannot be transferred between organisms. The development of sound statistical methods for integrating the predictions of different reverse-engineering strategies emerges as an important challenge for future research.},
  author       = {Michoel, Tom and De Smet, Riet and Joshi, Anagha Madhusudan and Van de Peer, Yves and Marchal, Kathleen},
  issn         = {1752-0509},
  journal      = {BMC Systems Biology},
  keyword      = {CELLS,ALGORITHMS,CEREVISIAE,YEAST,ORGANIZATION,GENE-EXPRESSION DATA,ESCHERICHIA-COLI},
  language     = {eng},
  number       = {49},
  pages        = {1--13},
  title        = {Comparative analysis of module-based versus direct methods for reverse-engineering transcriptional regulatory networks},
  url          = {http://dx.doi.org/10.1186/1752-0509-3-49},
  volume       = {3},
  year         = {2009},
}

Chicago
Michoel, Tom, Riet De Smet, Anagha Madhusudan Joshi, Yves Van de Peer, and Kathleen Marchal. 2009. “Comparative Analysis of Module-based Versus Direct Methods for Reverse-engineering Transcriptional Regulatory Networks.” BMC Systems Biology 3 (49): 1–13.
APA
Michoel, T., De Smet, R., Joshi, A. M., Van de Peer, Y., & Marchal, K. (2009). Comparative analysis of module-based versus direct methods for reverse-engineering transcriptional regulatory networks. BMC Systems Biology, 3(49), 1–13.
Vancouver
1.
Michoel T, De Smet R, Joshi AM, Van de Peer Y, Marchal K. Comparative analysis of module-based versus direct methods for reverse-engineering transcriptional regulatory networks. BMC Systems Biology. 2009;3(49):1–13.
MLA
Michoel, Tom, Riet De Smet, Anagha Madhusudan Joshi, et al. “Comparative Analysis of Module-based Versus Direct Methods for Reverse-engineering Transcriptional Regulatory Networks.” BMC Systems Biology 3.49 (2009): 1–13. Print.