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Refining transcriptional regulatory networks using network evolutionary models and gene histories.

Zhang X, Moret BM - Algorithms Mol Biol (2010)

Bottom Line: In previous work, we used a simple evolutionary model and provided extensive simulation results showing that phylogenetic information, combined with such a model, could be used to gain significant improvements on the performance of current inference algorithms.We show how to adapt our evolutionary approach to this new model and provide detailed simulation results, which show significant improvement on the reference network inference algorithms.We also provide results on biological data (cis-regulatory modules for 12 species of Drosophila), confirming our simulation results.

View Article: PubMed Central - HTML - PubMed

Affiliation: Laboratory for Computational Biology and Bioinformatics, Ecole Polytechnique Fédérale de Lausanne, EPFL-IC-LCBB, INJ230, Station 14, CH-1015 Lausanne, Switzerland.

ABSTRACT

Background: Computational inference of transcriptional regulatory networks remains a challenging problem, in part due to the lack of strong network models. In this paper we present evolutionary approaches to improve the inference of regulatory networks for a family of organisms by developing an evolutionary model for these networks and taking advantage of established phylogenetic relationships among these organisms. In previous work, we used a simple evolutionary model and provided extensive simulation results showing that phylogenetic information, combined with such a model, could be used to gain significant improvements on the performance of current inference algorithms.

Results: In this paper, we extend the evolutionary model so as to take into account gene duplications and losses, which are viewed as major drivers in the evolution of regulatory networks. We show how to adapt our evolutionary approach to this new model and provide detailed simulation results, which show significant improvement on the reference network inference algorithms. Different evolutionary histories for gene duplications and losses are studied, showing that our adapted approach is feasible under a broad range of conditions. We also provide results on biological data (cis-regulatory modules for 12 species of Drosophila), confirming our simulation results.

No MeSH data available.


Performance with extended evolution model and DBI inference method, and true history of gene duplications and losses. (A) Results with higher gene duplication and loss rates; (B) Results with lower gene duplication and loss rates.
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Figure 2: Performance with extended evolution model and DBI inference method, and true history of gene duplications and losses. (A) Results with higher gene duplication and loss rates; (B) Results with lower gene duplication and loss rates.

Mentions: In Fig. 2, we show the results of the experiments with DBNSim used to generate gene-expression data, and DBI as base inference algorithm. All results with DBI inference that we show are on one representative phylogenetic tree with 35 nodes on 7 levels, and the root network has 15 genes. The left plot has a relatively high rate of gene duplication and loss (resulting in 20 duplications and 23 losses along the tree), while the right one has a slightly lower rate (with 19 duplications and 15 losses), again averaged over 10 runs.


Refining transcriptional regulatory networks using network evolutionary models and gene histories.

Zhang X, Moret BM - Algorithms Mol Biol (2010)

Performance with extended evolution model and DBI inference method, and true history of gene duplications and losses. (A) Results with higher gene duplication and loss rates; (B) Results with lower gene duplication and loss rates.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2823753&req=5

Figure 2: Performance with extended evolution model and DBI inference method, and true history of gene duplications and losses. (A) Results with higher gene duplication and loss rates; (B) Results with lower gene duplication and loss rates.
Mentions: In Fig. 2, we show the results of the experiments with DBNSim used to generate gene-expression data, and DBI as base inference algorithm. All results with DBI inference that we show are on one representative phylogenetic tree with 35 nodes on 7 levels, and the root network has 15 genes. The left plot has a relatively high rate of gene duplication and loss (resulting in 20 duplications and 23 losses along the tree), while the right one has a slightly lower rate (with 19 duplications and 15 losses), again averaged over 10 runs.

Bottom Line: In previous work, we used a simple evolutionary model and provided extensive simulation results showing that phylogenetic information, combined with such a model, could be used to gain significant improvements on the performance of current inference algorithms.We show how to adapt our evolutionary approach to this new model and provide detailed simulation results, which show significant improvement on the reference network inference algorithms.We also provide results on biological data (cis-regulatory modules for 12 species of Drosophila), confirming our simulation results.

View Article: PubMed Central - HTML - PubMed

Affiliation: Laboratory for Computational Biology and Bioinformatics, Ecole Polytechnique Fédérale de Lausanne, EPFL-IC-LCBB, INJ230, Station 14, CH-1015 Lausanne, Switzerland.

ABSTRACT

Background: Computational inference of transcriptional regulatory networks remains a challenging problem, in part due to the lack of strong network models. In this paper we present evolutionary approaches to improve the inference of regulatory networks for a family of organisms by developing an evolutionary model for these networks and taking advantage of established phylogenetic relationships among these organisms. In previous work, we used a simple evolutionary model and provided extensive simulation results showing that phylogenetic information, combined with such a model, could be used to gain significant improvements on the performance of current inference algorithms.

Results: In this paper, we extend the evolutionary model so as to take into account gene duplications and losses, which are viewed as major drivers in the evolution of regulatory networks. We show how to adapt our evolutionary approach to this new model and provide detailed simulation results, which show significant improvement on the reference network inference algorithms. Different evolutionary histories for gene duplications and losses are studied, showing that our adapted approach is feasible under a broad range of conditions. We also provide results on biological data (cis-regulatory modules for 12 species of Drosophila), confirming our simulation results.

No MeSH data available.