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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Because there is no GO category for apple transcripts, we use Arabidopsis Thaliana orthologs in order to associate apple transcripts with GO categories.
References
Brazhnik, P., de la Fuente, A., Mendes, P.: Gene networks: how to put the function in genomics. Trends Biotechnol. 20(11), 467–472 (2002)
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)
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)
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)
Sharan, R., Ideker, T.: Modeling cellular machinery through biological network comparison. Nat. Biotechnol. 24(4), 427–433 (2006)
Ideker, T., Krogan, N.J.: Differential network biology. Mol. Syst. Biol. 8(1), 565 (2012)
Barabási, A.-L., Gulbahce, N., Loscalzo, J.: Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12(1), 56–68 (2011)
Pelechano, V., Steinmetz, L.M.: Gene regulation by antisense transcription. Nat. Rev. Genet. 14(12), 880–893 (2013)
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)
Altay, G., Emmert-Streib, F.: Inferring the conservative causal core of gene regulatory networks. BMC Syst. Biol. 4(1), 132 (2010)
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)
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)
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)
Langfelder, P., Horvath, S.: WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 9(1), 559 (2008)
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)
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)
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)
Altay, G., Asim, M., Markowetz, F., Neal, D.E.: Differential C3NET reveals disease networks of direct physical interactions. BMC Bioinform. 12(1), 296 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Legeay, M., Duval, B., Renou, JP. (2016). Differential Functional Analysis and Change Motifs in Gene Networks to Explore the Role of Anti-sense Transcription. In: Bourgeois, A., Skums, P., Wan, X., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2016. Lecture Notes in Computer Science(), vol 9683. Springer, Cham. https://doi.org/10.1007/978-3-319-38782-6_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-38782-6_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-38781-9
Online ISBN: 978-3-319-38782-6
eBook Packages: Computer ScienceComputer Science (R0)