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PeerJ. 2017 Feb 28;5:e3052. doi: 10.7717/peerj.3052. eCollection 2017.

[No title available]

PeerJ

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

Affiliations

  1. Computational Biology Laboratory (DLab), Fundacion Ciencia y Vida, Santiago, Chile; Centro Interdisciplinario de Neurociencia de Valparaíso, Valparaiso, Chile.
  2. Computational Biology Laboratory (DLab), Fundacion Ciencia y Vida, Santiago, Chile; Facultad de Ciencias Biologicas, Universidad Andres Bello, Santiago, Chile.
  3. Computational Biology Laboratory (DLab), Fundacion Ciencia y Vida , Santiago , Chile.

PMID: 28265516 PMCID: PMC5333545 DOI: 10.7717/peerj.3052

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

Keywords: Differential analysis; Gene Regulatory Network; Graphlet; Metric

Conflict of interest statement

Tomas Perez-Acle is an Academic Editor for PeerJ.

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