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

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article numbere3052
JournalPeerJ
Volume2017
Issue number2
DOIs
Publication statusPublished - 2017

Keywords

  • Differential analysis
  • Gene regulatory network
  • Graphlet
  • Metric

ASJC Scopus subject areas

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

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    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