Parsimonious reconstruction of network evolution

Patro, R., Sefer, E., Malin, J., Marçais, G., Navlakha, S., Kingsford, C. (September 2012) Parsimonious reconstruction of network evolution. Algorithms for Molecular Biology, 7 (25). (Public Dataset)

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URL: https://www.ncbi.nlm.nih.gov/pubmed/22992218
DOI: 10.1186/1748-7188-7-25

Abstract

BACKGROUND: Understanding the evolution of biological networks can provide insight into how their modular structure arises and how they are affected by environmental changes. One approach to studying the evolution of these networks is to reconstruct plausible common ancestors of present-day networks, allowing us to analyze how the topological properties change over time and to posit mechanisms that drive the networks' evolution. Further, putative ancestral networks can be used to help solve other difficult problems in computational biology, such as network alignment. RESULTS: We introduce a combinatorial framework for encoding network histories, and we give a fast procedure that, given a set of gene duplication histories, in practice finds network histories with close to the minimum number of interaction gain or loss events to explain the observed present-day networks. In contrast to previous studies, our method does not require knowing the relative ordering of unrelated duplication events. Results on simulated histories and real biological networks both suggest that common ancestral networks can be accurately reconstructed using this parsimony approach. A software package implementing our method is available under the Apache 2.0 license at http://cbcb.umd.edu/kingsford-group/parana. CONCLUSIONS: Our parsimony-based approach to ancestral network reconstruction is both efficient and accurate. We show that considering a larger set of potential ancestral interactions by not assuming a relative ordering of unrelated duplication events can lead to improved ancestral network inference.networks can be accurately reconstructed using this parsimony approach. © 2011 Springer-Verlag.

Item Type: Paper
Subjects: bioinformatics > computational biology > algorithms
evolution
bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > genes, structure and function > gene network
CSHL Authors:
Communities: CSHL labs > Navlakha lab
Depositing User: Matthew Dunn
Date: 19 September 2012
Date Deposited: 08 Nov 2019 15:23
Last Modified: 08 Nov 2019 15:23
PMCID: PMC3492119
Related URLs:
Dataset ID:
  • Software package: http://cbcb.umd.edu/kingsford-group/parana
URI: https://repository.cshl.edu/id/eprint/38685

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