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dc.contributor.author
Noh, Heeju
dc.contributor.author
Hua, Ziyi
dc.contributor.author
Chrysinas, Panagiotis
dc.contributor.author
Shoemaker, Jason E.
dc.contributor.author
Gunawan, Rudiyanto
dc.date.accessioned
2021-03-15T07:30:09Z
dc.date.available
2021-03-15T05:25:27Z
dc.date.available
2021-03-15T07:30:09Z
dc.date.issued
2021-03-04
dc.identifier.issn
1471-2105
dc.identifier.other
10.1186/s12859-021-04046-2
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/474407
dc.identifier.doi
10.3929/ethz-b-000474407
dc.description.abstract
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+, is crucial toward personalized medicine, where treatments can be tailored to individual patients. DeltaNeTS+ can be freely downloaded from http://www.github.com/cabsel/deltanetsplus.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Springer Nature
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
DeltaNeTS+: elucidating the mechanism of drugs and diseases using gene expression and transcriptional regulatory networks
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
BMC Bioinformatics
ethz.journal.volume
22
en_US
ethz.journal.issue
1
en_US
ethz.pages.start
108
en_US
ethz.size
19 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.scopus
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2021-03-15T05:25:33Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-03-15T07:30:20Z
ethz.rosetta.lastUpdated
2021-03-15T07:30:20Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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