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CORN—Condition Orientated Regulatory Networks: bridging conditions to gene networks

A transcriptional regulatory network (TRN) is a collection of transcription regulators with their associated downstream genes, which is highly condition-specific. Understanding how cell states can be programmed through small molecules/drugs or conditions by modulating the whole gene expression syste...

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Autores principales: Leung, Ricky Wai Tak, Jiang, Xiaosen, Zong, Xueqing, Zhang, Yanhong, Hu, Xinlin, Hu, Yaohua, Qin, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9677472/
https://www.ncbi.nlm.nih.gov/pubmed/36124777
http://dx.doi.org/10.1093/bib/bbac402
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author Leung, Ricky Wai Tak
Jiang, Xiaosen
Zong, Xueqing
Zhang, Yanhong
Hu, Xinlin
Hu, Yaohua
Qin, Jing
author_facet Leung, Ricky Wai Tak
Jiang, Xiaosen
Zong, Xueqing
Zhang, Yanhong
Hu, Xinlin
Hu, Yaohua
Qin, Jing
author_sort Leung, Ricky Wai Tak
collection PubMed
description A transcriptional regulatory network (TRN) is a collection of transcription regulators with their associated downstream genes, which is highly condition-specific. Understanding how cell states can be programmed through small molecules/drugs or conditions by modulating the whole gene expression system granted us the potential to amend abnormal cells and cure diseases. Condition Orientated Regulatory Networks (CORN, https://qinlab.sysu.edu.cn/home) is a library of condition (small molecule/drug treatments and gene knockdowns)-based transcriptional regulatory sub-networks (TRSNs) that come with an online TRSN matching tool. It allows users to browse condition-associated TRSNs or match those TRSNs by inputting transcriptomic changes of interest. CORN utilizes transcriptomic changes data after specific conditional treatment in cells, and in vivo transcription factor (TF) binding data in cells, by combining TF binding information and calculations of significant expression alterations of TFs and genes after the conditional treatments, TRNs under the effect of different conditions were constructed. In short, CORN associated 1805 different types of specific conditions (small molecule/drug treatments and gene knockdowns) to 9553 TRSNs in 25 human cell lines, involving 204TFs. By linking and curating specific conditions to responsive TRNs, the scientific community can now perceive how TRNs are altered and controlled by conditions alone in an organized manner for the first time. This study demonstrated with examples that CORN can aid the understanding of molecular pathology, pharmacology and drug repositioning, and screened drugs with high potential for cancer and coronavirus disease 2019 (COVID-19) treatments.
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spelling pubmed-96774722022-11-21 CORN—Condition Orientated Regulatory Networks: bridging conditions to gene networks Leung, Ricky Wai Tak Jiang, Xiaosen Zong, Xueqing Zhang, Yanhong Hu, Xinlin Hu, Yaohua Qin, Jing Brief Bioinform Problem Solving Protocol A transcriptional regulatory network (TRN) is a collection of transcription regulators with their associated downstream genes, which is highly condition-specific. Understanding how cell states can be programmed through small molecules/drugs or conditions by modulating the whole gene expression system granted us the potential to amend abnormal cells and cure diseases. Condition Orientated Regulatory Networks (CORN, https://qinlab.sysu.edu.cn/home) is a library of condition (small molecule/drug treatments and gene knockdowns)-based transcriptional regulatory sub-networks (TRSNs) that come with an online TRSN matching tool. It allows users to browse condition-associated TRSNs or match those TRSNs by inputting transcriptomic changes of interest. CORN utilizes transcriptomic changes data after specific conditional treatment in cells, and in vivo transcription factor (TF) binding data in cells, by combining TF binding information and calculations of significant expression alterations of TFs and genes after the conditional treatments, TRNs under the effect of different conditions were constructed. In short, CORN associated 1805 different types of specific conditions (small molecule/drug treatments and gene knockdowns) to 9553 TRSNs in 25 human cell lines, involving 204TFs. By linking and curating specific conditions to responsive TRNs, the scientific community can now perceive how TRNs are altered and controlled by conditions alone in an organized manner for the first time. This study demonstrated with examples that CORN can aid the understanding of molecular pathology, pharmacology and drug repositioning, and screened drugs with high potential for cancer and coronavirus disease 2019 (COVID-19) treatments. Oxford University Press 2022-09-17 /pmc/articles/PMC9677472/ /pubmed/36124777 http://dx.doi.org/10.1093/bib/bbac402 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Problem Solving Protocol
Leung, Ricky Wai Tak
Jiang, Xiaosen
Zong, Xueqing
Zhang, Yanhong
Hu, Xinlin
Hu, Yaohua
Qin, Jing
CORN—Condition Orientated Regulatory Networks: bridging conditions to gene networks
title CORN—Condition Orientated Regulatory Networks: bridging conditions to gene networks
title_full CORN—Condition Orientated Regulatory Networks: bridging conditions to gene networks
title_fullStr CORN—Condition Orientated Regulatory Networks: bridging conditions to gene networks
title_full_unstemmed CORN—Condition Orientated Regulatory Networks: bridging conditions to gene networks
title_short CORN—Condition Orientated Regulatory Networks: bridging conditions to gene networks
title_sort corn—condition orientated regulatory networks: bridging conditions to gene networks
topic Problem Solving Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9677472/
https://www.ncbi.nlm.nih.gov/pubmed/36124777
http://dx.doi.org/10.1093/bib/bbac402
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