Abstract
Gene Regulatory Networks (GRNs) allow the study of regulation of gene expression of whole genomes. Among the most relevant advantages of using networks to depict this key process, there is the visual representation of large amounts of information and the application of graph theory to generate new knowledge. Nonetheless, despite the many uses of GRNs, it is still difficult and expensive to assign Transcription Factors (TFs) to the regulation of specific genes. ChIP-Seq allows the determination of TF Binding Sites (TFBSs) over whole genomes, but it is still an expensive technique that can only be applied one TF at a time and requires replicates to reduce its noise. Once TFBSs are determined, the assignment of each TF and its binding sites to the regulation of specific genes is not trivial, and it is often performed by carrying out site-specific experiments that are unfeasible to perform in all possible binding sites. Here, we addressed these relevant issues with a two-step methodology using Drosophila melanogaster as a case study. First, our protocol starts by gathering all transcription factor binding sites (TFBSs) determined with ChIP-Seq experiments available at ENCODE and FlyBase. Then each TFBS is used to assign TFs to the regulation of likely target genes based on the TFBS proximity to the transcription start site of all genes. In the final step, to try to select the most likely regulatory TF from those previously assigned to each gene, we employ GENIE3, a random forest-based method, and more than 9,000 RNA-seq experiments from D. melanogaster. Following, we employed known TF protein-protein interactions to estimate the feasibility of regulatory events in our filtered networks. Finally, we show how known interactions between co-regulatory TFs of each gene increase after the second step of our approach, and thus, the consistency of the TF-gene assignment. Also, we employed our methodology to create a network centered on the Drosophila melanogaster gene Hr96 to demonstrate the role of this transcription factor on mitochondrial gene regulation.
Highlights
The control of gene expression is one of the key processes that allow living organisms to adapt to their environment
We first show how our approach improves the consistency of Transcription Factors (TFs)-gene assignment created by assigning TFs to genes if a TF Binding Sites (TFBSs) is near the gene
We demonstrated that the consistency of TF-gene assignment improves by increasing the number of TFs targeting the same gene that are known to interact between them
Summary
The control of gene expression is one of the key processes that allow living organisms to adapt to their environment. Different regulatory mechanisms determine which gene is expressed and what amount of the product encoded is generated. Transcription Factors (TFs) are deemed to be the most relevant players in the control of transcription, but there are other types of regulation that include ncRNAs and other proteins (Ramírez-Clavijo and Montoya-Ortíz, 2013). When studying the regulation in whole genomes, gene regulation is often represented as a network where nodes represent genes. In this type of network called Gene Regulatory Network (GRN), connections between genes indicate that the product of a gene regulates the expression of another gene, and their direction is important
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
