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NetREX-CF integrates incomplete transcription factor data with gene expression to reconstruct gene regulatory networks

The inference of Gene Regulatory Networks (GRNs) is one of the key challenges in systems biology. Leading algorithms utilize, in addition to gene expression, prior knowledge such as Transcription Factor (TF) DNA binding motifs or results of TF binding experiments. However, such prior knowledge is ty...

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Autores principales: Wang, Yijie, Lee, Hangnoh, Fear, Justin M., Berger, Isabelle, Oliver, Brian, Przytycka, Teresa M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9684490/
https://www.ncbi.nlm.nih.gov/pubmed/36418514
http://dx.doi.org/10.1038/s42003-022-04226-7
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author Wang, Yijie
Lee, Hangnoh
Fear, Justin M.
Berger, Isabelle
Oliver, Brian
Przytycka, Teresa M.
author_facet Wang, Yijie
Lee, Hangnoh
Fear, Justin M.
Berger, Isabelle
Oliver, Brian
Przytycka, Teresa M.
author_sort Wang, Yijie
collection PubMed
description The inference of Gene Regulatory Networks (GRNs) is one of the key challenges in systems biology. Leading algorithms utilize, in addition to gene expression, prior knowledge such as Transcription Factor (TF) DNA binding motifs or results of TF binding experiments. However, such prior knowledge is typically incomplete, therefore, integrating it with gene expression to infer GRNs remains difficult. To address this challenge, we introduce NetREX-CF—Regulatory Network Reconstruction using EXpression and Collaborative Filtering—a GRN reconstruction approach that brings together Collaborative Filtering to address the incompleteness of the prior knowledge and a biologically justified model of gene expression (sparse Network Component Analysis based model). We validated the NetREX-CF using Yeast data and then used it to construct the GRN for Drosophila Schneider 2 (S2) cells. To corroborate the GRN, we performed a large-scale RNA-Seq analysis followed by a high-throughput RNAi treatment against all 465 expressed TFs in the cell line. Our knockdown result has not only extensively validated the GRN we built, but also provides a benchmark that our community can use for evaluating GRNs. Finally, we demonstrate that NetREX-CF can infer GRNs using single-cell RNA-Seq, and outperforms other methods, by using previously published human data.
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spelling pubmed-96844902022-11-25 NetREX-CF integrates incomplete transcription factor data with gene expression to reconstruct gene regulatory networks Wang, Yijie Lee, Hangnoh Fear, Justin M. Berger, Isabelle Oliver, Brian Przytycka, Teresa M. Commun Biol Article The inference of Gene Regulatory Networks (GRNs) is one of the key challenges in systems biology. Leading algorithms utilize, in addition to gene expression, prior knowledge such as Transcription Factor (TF) DNA binding motifs or results of TF binding experiments. However, such prior knowledge is typically incomplete, therefore, integrating it with gene expression to infer GRNs remains difficult. To address this challenge, we introduce NetREX-CF—Regulatory Network Reconstruction using EXpression and Collaborative Filtering—a GRN reconstruction approach that brings together Collaborative Filtering to address the incompleteness of the prior knowledge and a biologically justified model of gene expression (sparse Network Component Analysis based model). We validated the NetREX-CF using Yeast data and then used it to construct the GRN for Drosophila Schneider 2 (S2) cells. To corroborate the GRN, we performed a large-scale RNA-Seq analysis followed by a high-throughput RNAi treatment against all 465 expressed TFs in the cell line. Our knockdown result has not only extensively validated the GRN we built, but also provides a benchmark that our community can use for evaluating GRNs. Finally, we demonstrate that NetREX-CF can infer GRNs using single-cell RNA-Seq, and outperforms other methods, by using previously published human data. Nature Publishing Group UK 2022-11-23 /pmc/articles/PMC9684490/ /pubmed/36418514 http://dx.doi.org/10.1038/s42003-022-04226-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Wang, Yijie
Lee, Hangnoh
Fear, Justin M.
Berger, Isabelle
Oliver, Brian
Przytycka, Teresa M.
NetREX-CF integrates incomplete transcription factor data with gene expression to reconstruct gene regulatory networks
title NetREX-CF integrates incomplete transcription factor data with gene expression to reconstruct gene regulatory networks
title_full NetREX-CF integrates incomplete transcription factor data with gene expression to reconstruct gene regulatory networks
title_fullStr NetREX-CF integrates incomplete transcription factor data with gene expression to reconstruct gene regulatory networks
title_full_unstemmed NetREX-CF integrates incomplete transcription factor data with gene expression to reconstruct gene regulatory networks
title_short NetREX-CF integrates incomplete transcription factor data with gene expression to reconstruct gene regulatory networks
title_sort netrex-cf integrates incomplete transcription factor data with gene expression to reconstruct gene regulatory networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9684490/
https://www.ncbi.nlm.nih.gov/pubmed/36418514
http://dx.doi.org/10.1038/s42003-022-04226-7
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