bioRxivBioinformaticsPreprint
Automated generation of context-specific Gene Regulatory Networks with a weighted approach in D. melanogaster
Leandro Murgas SaavedraiD, Sebastián Contreras-RiquelmeiD, J. Eduardo MartíneziD, Camilo VillamániD, Rodrigo SantibáñeziD, Alberto J. M. MartíniD
Abstract
MotivationThe regulation of gene expression is a key factor in the development and maintenance of life in all organisms. This process is carried out mainly through the action of transcription factors (TFs), although other actors such as ncRNAs are involved. In this work, we propose a new method to construct Gene Regulatory Networks (GRNs) depicting regulatory events in a certain context for Drosophila melanogaster. Our approach is based on known relationships between epigenetics and the activity of transcription factors. ResultsWe developed method, Tool for Weighted Epigenomic Networks in D. melanogaster (Fly T-WEoN), which generates GRNs starting from a reference network that contains all known gene regulations in the fly. Regulations that are unlikely taking place are removed by applying a series of knowledge-based filters. Each of these filters is implemented as an independent module that considers a type of experimental evidence, including DNA methylation, chromatin accessibility, histone modifications, and gene expression. Fly T-WEoN is based on heuristic rules that reflect current knowledge on gene regulation in D. melanogaster obtained from literature. Experimental data files can be generated with several standard procedures and used solely when and if available.Fly T-WEoN is available as a Cytoscape application that permits integration with other tools, and facilitates downstream network analysis. In this work, we first demonstrate the reliability of our method to then provide a relevant application case of our tool: early development of D. melanogaster. AvailabilityFly T-WEoN, together with its step-by-step guide is available at https://weon.readthedocs.io Contactalberto.martin@umayor.cl
- organism: Drosophila
- assay: ChIP-seq
- assay: DNA methylation
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: statistical model
- approach: experimental assay
- Citations
- 0
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- NetREX: Network Rewiring using EXpression - Towards Context Specific Regulatory Networks
Yijie Wang, Dong-Yeon Cho, … Teresa M. Przytycka·bioRxiv·Apr 24, 2017
- Inferring gene-regulatory networks using epigenomic priors
Thomas E. Bartlett, Melodie Li, … Qiulin Huang·iSciencePeer-reviewed·Feb 28, 2026
- Expanding the fly eye gene regulatory network: from Drosophila to the hoverfly Episyrphus balteatus.
T. Navarro, Jesús Torres, … Fernando Casares·PLoS GeneticsPeer-reviewed·Feb 26, 2026
- An efficient gene regulatory network inference algorithm for early Drosophila melanogaster embryogenesis
Hirotaka Matsumoto, Hisanori Kiryu, … Itoshi Nikaido·bioRxiv·Nov 2, 2017
- Reference Regulatory Element-Guided Gene Expression Analysis for Mechanistic Inference of Gene Regulatory Networks
Lixin Ren, Ishita Debnath, Zhana Duren·bioRxiv·Jul 16, 2026
- Binding profiles for 954 Drosophila and C. elegans transcription factors revealtissue specific regulatory relationships
Michelle M. Kudron, Louis Gevirtzman, … R Waterston·bioRxiv·Jan 18, 2024