LoTo: A graphlet based method for the comparison of local topology between gene regulatory networks

Alberto J. Martin, Sebastián Contreras-Riquelme, Calixto Dominguez, Tomas Perez-Acle

Resultado de la investigación: Article

1 Cita (Scopus)

Resumen

One of the main challenges of the post-genomic era is the understanding of how gene expression is controlled. Changes in gene expression lay behind diverse biological phenomena such as development, disease and the adaptation to different environmental conditions. Despite the availability of well-established methods to identify these changes, tools to discern how gene regulation is orchestrated are still required. The regulation of gene expression is usually depicted as a Gene Regulatory Network (GRN) where changes in the network structure (i.e., network topology) represent adjustments of gene regulation. Like other networks, GRNs are composed of basic building blocks; small induced subgraphs called graphlets. Here we present LoTo, a novel method that using Graphlet Based Metrics (GBMs) identifies topological variations between different states of a GRN. Under our approach, different states of a GRN are analyzed to determine the types of graphlet formed by all triplets of nodes in the network. Subsequently, graphlets occurring in a state of the network are compared to those formed by the same three nodes in another version of the network. Once the comparisons are performed, LoTo applies metrics from binary classification problems calculated on the existence and absence of graphlets to assess the topological similarity between both network states. Experiments performed on randomized networks demonstrate that GBMs are more sensitive to topological variation than the same metrics calculated on single edges. Additional comparisons with other common metrics demonstrate that our GBMs are capable to identify nodes whose local topology changes between different states of the network. Notably, due to the explicit use of graphlets, LoTo captures topological variations that are disregarded by other approaches. LoTo is freely available as an online web server at http://dlab.cl/loto.

Idioma originalEnglish
Número de artículoe3052
PublicaciónPeerJ
Volumen2017
N.º2
DOI
EstadoPublished - 2017

Huella dactilar

Gene Regulatory Networks
Gene expression
topology
Genes
Topology
Biological Phenomena
Gene Expression
gene expression
gene expression regulation
Gene Expression Regulation
genes
methodology
genomics
environmental factors
Servers
gene regulatory networks
Availability
Experiments

ASJC Scopus subject areas

  • Neuroscience(all)
  • Medicine(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Citar esto

Martin, A. J., Contreras-Riquelme, S., Dominguez, C., & Perez-Acle, T. (2017). LoTo: A graphlet based method for the comparison of local topology between gene regulatory networks. PeerJ, 2017(2), [e3052]. https://doi.org/10.7717/peerj.3052
Martin, Alberto J. ; Contreras-Riquelme, Sebastián ; Dominguez, Calixto ; Perez-Acle, Tomas. / LoTo : A graphlet based method for the comparison of local topology between gene regulatory networks. En: PeerJ. 2017 ; Vol. 2017, N.º 2.
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Martin, AJ, Contreras-Riquelme, S, Dominguez, C & Perez-Acle, T 2017, 'LoTo: A graphlet based method for the comparison of local topology between gene regulatory networks', PeerJ, vol. 2017, n.º 2, e3052. https://doi.org/10.7717/peerj.3052

LoTo : A graphlet based method for the comparison of local topology between gene regulatory networks. / Martin, Alberto J.; Contreras-Riquelme, Sebastián; Dominguez, Calixto; Perez-Acle, Tomas.

En: PeerJ, Vol. 2017, N.º 2, e3052, 2017.

Resultado de la investigación: Article

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T2 - A graphlet based method for the comparison of local topology between gene regulatory networks

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AU - Contreras-Riquelme, Sebastián

AU - Dominguez, Calixto

AU - Perez-Acle, Tomas

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AB - One of the main challenges of the post-genomic era is the understanding of how gene expression is controlled. Changes in gene expression lay behind diverse biological phenomena such as development, disease and the adaptation to different environmental conditions. Despite the availability of well-established methods to identify these changes, tools to discern how gene regulation is orchestrated are still required. The regulation of gene expression is usually depicted as a Gene Regulatory Network (GRN) where changes in the network structure (i.e., network topology) represent adjustments of gene regulation. Like other networks, GRNs are composed of basic building blocks; small induced subgraphs called graphlets. Here we present LoTo, a novel method that using Graphlet Based Metrics (GBMs) identifies topological variations between different states of a GRN. Under our approach, different states of a GRN are analyzed to determine the types of graphlet formed by all triplets of nodes in the network. Subsequently, graphlets occurring in a state of the network are compared to those formed by the same three nodes in another version of the network. Once the comparisons are performed, LoTo applies metrics from binary classification problems calculated on the existence and absence of graphlets to assess the topological similarity between both network states. Experiments performed on randomized networks demonstrate that GBMs are more sensitive to topological variation than the same metrics calculated on single edges. Additional comparisons with other common metrics demonstrate that our GBMs are capable to identify nodes whose local topology changes between different states of the network. Notably, due to the explicit use of graphlets, LoTo captures topological variations that are disregarded by other approaches. LoTo is freely available as an online web server at http://dlab.cl/loto.

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