Van Landeghem, SofieVan Parys, ThomasDubois, MariekeInze, DirkVan de Peer, Yves2016-05-172016-05-172016-01-05Van Landeghem, S, Van Parys, T, Dubois, M, Inze, D & Van de Peer, Y 2016, 'Diffany : an ontology-driven framework to infer, visualise and analyse differential molecular networks', BMC Bioinformatics, vol. 17, art. 18, pp. 1-12.1471-210510.1186/s12859-015-0863-yhttp://hdl.handle.net/2263/52641Additional file 1: Overview of the Diffany framework. Overview of the Diffany framework and its typical usage in a specific experiment involving the perturbation of an interactome under one or more conditions. (DOCX 183 KB)Additional file 2: List of differentially expressed genes. Dataset of differentially expressed genes, as originally published by [24]. Here, those genes are listed that are differentially expressed in at least one of the 4 time points and in either the more (FDR < 0.05) or less (FDR < 0.1) stringent dataset. This file also depicts the overlap of genes at the different time points. (XLSX 514 KB)Additional file 3: Experimental methodology. Methodological details of the experiments performed on the putative HY5 regulator. (DOCX 22 KB)Additional file 4: Figure showing the experimental validation of the putative HY5 regulator. Detailed analysis of hy5 mutants and WT lines when exposed to mannitol-induced stress, comparing both leaf area as well as expression levels of putative HY5-target genes such as TCH3 and MYB51. (DOCX 472 KB)BACKGROUND : Differential networks have recently been introduced as a powerful way to study the dynamic rewiring capabilities of an interactome in response to changing environmental conditions or stimuli. Currently, such differential networks are generated and visualised using ad hoc methods, and are often limited to the analysis of only one condition-specific response or one interaction type at a time. RESULTS : In this work, we present a generic, ontology-driven framework to infer, visualise and analyse an arbitrary set of condition-specific responses against one reference network. To this end, we have implemented novel ontology-based algorithms that can process highly heterogeneous networks, accounting for both physical interactions and regulatory associations, symmetric and directed edges, edge weights and negation. We propose this integrative framework as a standardised methodology that allows a unified view on differential networks and promotes comparability between differential network studies. As an illustrative application, we demonstrate its usefulness on a plant abiotic stress study and we experimentally confirmed a predicted regulator.en© 2015 Van Landeghem et al. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License.Differential networksOsmotic stress responseSystems biologyArticle