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1000 Titel
  • Filtering of Data-Driven Gene Regulatory Networks Using Drosophila melanogaster as a Case Study
1000 Autor/in
  1. Cuesta-Astroz, Yesid |
  2. Gischkow Rucatti, Guilherme |
  3. Murgas, Leandro |
  4. SanMartín, Carol D. |
  5. Sanhueza, Mario |
  6. Martin, Alberto J. M. |
1000 Verlag
  • Frontiers Media S.A.
1000 Erscheinungsjahr 2021
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2021-07-28
1000 Erschienen in
1000 Quellenangabe
  • 12:649764
1000 Copyrightjahr
  • 2021
1000 Embargo
  • 2022-01-30
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.3389/fgene.2021.649764 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8355599/ |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Abstract/Summary
  • <jats:p>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 <jats:italic>Drosophila melanogaster</jats:italic> 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 <jats:italic>D. melanogaster</jats:italic>. 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 <jats:italic>Drosophila melanogaster</jats:italic> gene <jats:italic>Hr96</jats:italic> to demonstrate the role of this transcription factor on mitochondrial gene regulation.</jats:p>
1000 Sacherschließung
lokal transcription factor targets
lokal transcriptional regulation
lokal Genetics
lokal gene regulatory network
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/uri/Q3Vlc3RhLUFzdHJveiwgWWVzaWQ=|https://frl.publisso.de/adhoc/uri/R2lzY2hrb3cgUnVjYXR0aSwgR3VpbGhlcm1l|https://frl.publisso.de/adhoc/uri/TXVyZ2FzLCBMZWFuZHJv|https://frl.publisso.de/adhoc/uri/U2FuTWFydMOtbiwgQ2Fyb2wgRC4=|https://frl.publisso.de/adhoc/uri/U2FuaHVlemEsIE1hcmlv|https://frl.publisso.de/adhoc/uri/TWFydGluLCBBbGJlcnRvIEouIE0u
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1000 Erstellt am 2024-05-22T00:57:24.080+0200
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1000 Objekt bearb. Wed May 22 14:57:11 CEST 2024
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