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Filtering of Data-Driven Gene Regulatory Networks Using Drosophila melanogaster as a Case Study
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 k...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8355599/ https://www.ncbi.nlm.nih.gov/pubmed/34394179 http://dx.doi.org/10.3389/fgene.2021.649764 |
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author | Cuesta-Astroz, Yesid Gischkow Rucatti, Guilherme Murgas, Leandro SanMartín, Carol D. Sanhueza, Mario Martin, Alberto J. M. |
author_facet | Cuesta-Astroz, Yesid Gischkow Rucatti, Guilherme Murgas, Leandro SanMartín, Carol D. Sanhueza, Mario Martin, Alberto J. M. |
author_sort | Cuesta-Astroz, Yesid |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8355599 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-83555992021-08-12 Filtering of Data-Driven Gene Regulatory Networks Using Drosophila melanogaster as a Case Study Cuesta-Astroz, Yesid Gischkow Rucatti, Guilherme Murgas, Leandro SanMartín, Carol D. Sanhueza, Mario Martin, Alberto J. M. Front Genet Genetics 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. Frontiers Media S.A. 2021-07-28 /pmc/articles/PMC8355599/ /pubmed/34394179 http://dx.doi.org/10.3389/fgene.2021.649764 Text en Copyright © 2021 Cuesta-Astroz, Gischkow Rucatti, Murgas, SanMartín, Sanhueza and Martin. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Cuesta-Astroz, Yesid Gischkow Rucatti, Guilherme Murgas, Leandro SanMartín, Carol D. Sanhueza, Mario Martin, Alberto J. M. Filtering of Data-Driven Gene Regulatory Networks Using Drosophila melanogaster as a Case Study |
title | Filtering of Data-Driven Gene Regulatory Networks Using Drosophila melanogaster as a Case Study |
title_full | Filtering of Data-Driven Gene Regulatory Networks Using Drosophila melanogaster as a Case Study |
title_fullStr | Filtering of Data-Driven Gene Regulatory Networks Using Drosophila melanogaster as a Case Study |
title_full_unstemmed | Filtering of Data-Driven Gene Regulatory Networks Using Drosophila melanogaster as a Case Study |
title_short | Filtering of Data-Driven Gene Regulatory Networks Using Drosophila melanogaster as a Case Study |
title_sort | filtering of data-driven gene regulatory networks using drosophila melanogaster as a case study |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8355599/ https://www.ncbi.nlm.nih.gov/pubmed/34394179 http://dx.doi.org/10.3389/fgene.2021.649764 |
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