DeltaNeTS+: elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks
BACKGROUND: Knowledge on the molecular targets of diseases and drugs is crucial for elucidating disease pathogenesis and mechanism of action of drugs, and for driving drug discovery and treatment formulation. In this regard, high-throughput gene transcriptional profiling has become a leading technol...
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ftpubmed:oai:pubmedcentral.nih.gov:7934467 2023-05-15T15:34:32+02:00 DeltaNeTS+: elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks Noh, Heeju Hua, Ziyi Chrysinas, Panagiotis Shoemaker, Jason E. Gunawan, Rudiyanto 2021-03-04 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7934467/ http://www.ncbi.nlm.nih.gov/pubmed/33663384 https://doi.org/10.1186/s12859-021-04046-2 en eng BioMed Central http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7934467/ http://www.ncbi.nlm.nih.gov/pubmed/33663384 http://dx.doi.org/10.1186/s12859-021-04046-2 © The Author(s) 2021 Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. CC0 PDM CC-BY BMC Bioinformatics Methodology Article Text 2021 ftpubmed https://doi.org/10.1186/s12859-021-04046-2 2021-03-14T01:53:04Z BACKGROUND: Knowledge on the molecular targets of diseases and drugs is crucial for elucidating disease pathogenesis and mechanism of action of drugs, and for driving drug discovery and treatment formulation. In this regard, high-throughput gene transcriptional profiling has become a leading technology, generating whole-genome data on the transcriptional alterations caused by diseases or drug compounds. However, identifying direct gene targets, especially in the background of indirect (downstream) effects, based on differential gene expressions is difficult due to the complexity of gene regulatory network governing the gene transcriptional processes. RESULTS: In this work, we developed a network analysis method, called DeltaNeTS+, for inferring direct gene targets of drugs and diseases from gene transcriptional profiles. DeltaNeTS+ uses a gene regulatory network model to identify direct perturbations to the transcription of genes using gene expression data. Importantly, DeltaNeTS+ is able to combine both steady-state and time-course expression profiles, as well as leverage information on the gene network structure. We demonstrated the power of DeltaNeTS+ in predicting gene targets using gene expression data in complex organisms, including Caenorhabditis elegans and human cell lines (T-cell and Calu-3). More specifically, in an application to time-course gene expression profiles of influenza A H1N1 (swine flu) and H5N1 (avian flu) infection, DeltaNeTS+ shed light on the key differences of dynamic cellular perturbations caused by the two influenza strains. CONCLUSION: DeltaNeTS+ is a powerful network analysis tool for inferring gene targets from gene expression profiles. As demonstrated in the case studies, by incorporating available information on gene network structure, DeltaNeTS+ produces accurate predictions of direct gene targets from a small sample size (~ 10 s). Integrating static and dynamic expression data with transcriptional network structure extracted from genomic information, as enabled by DeltaNeTS+, ... Text Avian flu PubMed Central (PMC) BMC Bioinformatics 22 1 |
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Methodology Article |
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Methodology Article Noh, Heeju Hua, Ziyi Chrysinas, Panagiotis Shoemaker, Jason E. Gunawan, Rudiyanto DeltaNeTS+: elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks |
topic_facet |
Methodology Article |
description |
BACKGROUND: Knowledge on the molecular targets of diseases and drugs is crucial for elucidating disease pathogenesis and mechanism of action of drugs, and for driving drug discovery and treatment formulation. In this regard, high-throughput gene transcriptional profiling has become a leading technology, generating whole-genome data on the transcriptional alterations caused by diseases or drug compounds. However, identifying direct gene targets, especially in the background of indirect (downstream) effects, based on differential gene expressions is difficult due to the complexity of gene regulatory network governing the gene transcriptional processes. RESULTS: In this work, we developed a network analysis method, called DeltaNeTS+, for inferring direct gene targets of drugs and diseases from gene transcriptional profiles. DeltaNeTS+ uses a gene regulatory network model to identify direct perturbations to the transcription of genes using gene expression data. Importantly, DeltaNeTS+ is able to combine both steady-state and time-course expression profiles, as well as leverage information on the gene network structure. We demonstrated the power of DeltaNeTS+ in predicting gene targets using gene expression data in complex organisms, including Caenorhabditis elegans and human cell lines (T-cell and Calu-3). More specifically, in an application to time-course gene expression profiles of influenza A H1N1 (swine flu) and H5N1 (avian flu) infection, DeltaNeTS+ shed light on the key differences of dynamic cellular perturbations caused by the two influenza strains. CONCLUSION: DeltaNeTS+ is a powerful network analysis tool for inferring gene targets from gene expression profiles. As demonstrated in the case studies, by incorporating available information on gene network structure, DeltaNeTS+ produces accurate predictions of direct gene targets from a small sample size (~ 10 s). Integrating static and dynamic expression data with transcriptional network structure extracted from genomic information, as enabled by DeltaNeTS+, ... |
format |
Text |
author |
Noh, Heeju Hua, Ziyi Chrysinas, Panagiotis Shoemaker, Jason E. Gunawan, Rudiyanto |
author_facet |
Noh, Heeju Hua, Ziyi Chrysinas, Panagiotis Shoemaker, Jason E. Gunawan, Rudiyanto |
author_sort |
Noh, Heeju |
title |
DeltaNeTS+: elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks |
title_short |
DeltaNeTS+: elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks |
title_full |
DeltaNeTS+: elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks |
title_fullStr |
DeltaNeTS+: elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks |
title_full_unstemmed |
DeltaNeTS+: elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks |
title_sort |
deltanets+: elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks |
publisher |
BioMed Central |
publishDate |
2021 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7934467/ http://www.ncbi.nlm.nih.gov/pubmed/33663384 https://doi.org/10.1186/s12859-021-04046-2 |
genre |
Avian flu |
genre_facet |
Avian flu |
op_source |
BMC Bioinformatics |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7934467/ http://www.ncbi.nlm.nih.gov/pubmed/33663384 http://dx.doi.org/10.1186/s12859-021-04046-2 |
op_rights |
© The Author(s) 2021 Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
op_rightsnorm |
CC0 PDM CC-BY |
op_doi |
https://doi.org/10.1186/s12859-021-04046-2 |
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BMC Bioinformatics |
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22 |
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1 |
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