Advertisement

Differential Functional Analysis and Change Motifs in Gene Networks to Explore the Role of Anti-sense Transcription

  • Marc Legeay
  • Béatrice DuvalEmail author
  • Jean-Pierre Renou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9683)

Abstract

Several transcriptomic studies have shown the widespread existence of anti-sense transcription in cell. Anti-sense RNAs may be important actors in transcriptional control, especially in stress response processes. The aim of our work is to study gene networks, with the particularity to integrate in the process anti-sense transcripts. In this paper, we first present a method that highlights the importance of taking into account anti-sense data into functional enrichment analysis. Secondly, we propose the differential analysis of gene networks built with and without anti-sense actors in order to discover interesting change motifs that involve the anti-sense transcripts. For more reliability, our network comparison only studies the conservative causal part of a network, inferred by the C3NET method. Our work is realized on transcriptomic data from apple fruit.

References

  1. 1.
    Brazhnik, P., de la Fuente, A., Mendes, P.: Gene networks: how to put the function in genomics. Trends Biotechnol. 20(11), 467–472 (2002)CrossRefGoogle Scholar
  2. 2.
    Bansal, M., Belcastro, V., Ambesi-Impiombato, A., di Bernardo, D.: How to infer gene networks from expression profiles. Mol. Syst. Biol. 3(1), 78 (2007)Google Scholar
  3. 3.
    Marbach, D., Costello, J.C., Küffner, R., Vega, N.M., Prill, R.J., Camacho, D.M., Allison, K.R., The DREAM5 Consortium, Kellis M., Collins J.J., Stolovitzky G.: Wisdom of crowds for robust gene network inference. Nat. Methods 9(8), 796–804 (2012)Google Scholar
  4. 4.
    Emmert-Streib, F., Glazko, G., Altay, G., de Matos, S.R.: Statistical inference and reverse engineering of gene regulatory networks from observational expression data. Bioinform. Comput. Biol. 3, 8 (2012)Google Scholar
  5. 5.
    Sharan, R., Ideker, T.: Modeling cellular machinery through biological network comparison. Nat. Biotechnol. 24(4), 427–433 (2006)CrossRefGoogle Scholar
  6. 6.
    Ideker, T., Krogan, N.J.: Differential network biology. Mol. Syst. Biol. 8(1), 565 (2012)Google Scholar
  7. 7.
    Barabási, A.-L., Gulbahce, N., Loscalzo, J.: Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12(1), 56–68 (2011)CrossRefGoogle Scholar
  8. 8.
    Pelechano, V., Steinmetz, L.M.: Gene regulation by antisense transcription. Nat. Rev. Genet. 14(12), 880–893 (2013)CrossRefGoogle Scholar
  9. 9.
    Celton, J.-M., Gaillard, S., Bruneau, M., Pelletier, S., Aubourg, S., Martin-Magniette, M.-L., Navarro, L., Laurens, F., Renou, J.-P.: Widespread anti-sense transcription in apple is correlated with siRNA production and indicates a large potential for transcriptional and/or post-transcriptional control. New Phytologist, 287–299 (2014)Google Scholar
  10. 10.
    Altay, G., Emmert-Streib, F.: Inferring the conservative causal core of gene regulatory networks. BMC Syst. Biol. 4(1), 132 (2010)CrossRefGoogle Scholar
  11. 11.
    Yi, H., Richards, E.J.: A cluster of disease resistance genes in Arabidopsis is coordinately regulated by transcriptional activation and RNA silencing. Plant Cell 19, 2929–2939 (2007)CrossRefGoogle Scholar
  12. 12.
    Maere, S., Heymans, K., Kuiper, M.: BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 21(16), 3448–3449 (2005)CrossRefGoogle Scholar
  13. 13.
    Friedel, S., Usadel, B., von Wiren, N., Sreenivasulu, N.: Reverse Engineering: A Key Component of Systems Biology to Unravel Global Abiotic Stress Cross-Talk. Front. Plant Sci. 3, 294 (2012)CrossRefGoogle Scholar
  14. 14.
    Langfelder, P., Horvath, S.: WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 9(1), 559 (2008)CrossRefGoogle Scholar
  15. 15.
    Margolin, A.A., Nemenman, I., Basso, K., Wiggins, C., Stolovitzky, G., Favera, R.D., Califano, A.: ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinform. 7(Suppl 1), S7 (2006)CrossRefGoogle Scholar
  16. 16.
    Zhang, X., Liu, K., Liu, Z.-P., Duval, B., Richer, J.-M., Zao, X.-M., Hao, J.-K., Chen, L.: NARROMI: a noise and redundancy reduction technique improves accuracy of gene regulatory network inference. Bioinformatics 29(1), 106–113 (2012)CrossRefGoogle Scholar
  17. 17.
    Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D., Amin, N., Schwikowski, B., Ideker, T.: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13(11), 2498–2504 (2003)CrossRefGoogle Scholar
  18. 18.
    Altay, G., Asim, M., Markowetz, F., Neal, D.E.: Differential C3NET reveals disease networks of direct physical interactions. BMC Bioinform. 12(1), 296 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Marc Legeay
    • 1
    • 2
  • Béatrice Duval
    • 1
    Email author
  • Jean-Pierre Renou
    • 2
  1. 1.LERIA - Université d’Angers - UNAMAngersFrance
  2. 2.Institut de Recherche en Horticulture et Semences (IRHS)UMR1345 INRA-Université d’Angers-AgroCampus Ouest, Centre Angers-NantesBeaucouzéFrance

Personalised recommendations