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dc.contributor.author
Ud-Dean, S.M. Minhaz
dc.contributor.author
Heise, Sandra
dc.contributor.author
Klamt, Steffen
dc.contributor.author
Gunawan, Rudiyanto
dc.date.accessioned
2018-09-04T11:23:27Z
dc.date.available
2017-06-12T08:21:12Z
dc.date.available
2018-09-04T11:23:27Z
dc.date.issued
2016-06
dc.identifier.issn
1471-2105
dc.identifier.other
10.1186/s12859-016-1137-z
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/117902
dc.identifier.doi
10.3929/ethz-b-000117902
dc.description.abstract
Background The inference of gene regulatory networks (GRNs) from transcriptional expression profiles is challenging, predominantly due to its underdetermined nature. One important consequence of underdetermination is the existence of many possible solutions to this inference. Our previously proposed ensemble inference algorithm TRaCE addressed this issue by inferring an ensemble of network directed graphs (digraphs) using differential gene expressions from gene knock-out (KO) experiments. However, TRaCE could not deal with the mode of the transcriptional regulations (activation or repression), an important feature of GRNs. Results In this work, we developed a new algorithm called TRaCE+ for the inference of an ensemble of signed GRN digraphs from transcriptional expression data of gene KO experiments. The sign of the edges indicates whether the regulation is an activation (positive) or a repression (negative). TRaCE+ generates the upper and lower bounds of the ensemble, which define uncertain regulatory interactions that could not be verified by the data. As demonstrated in the case studies using Escherichia coli GRN and 100-gene gold-standard GRNs from DREAM 4 network inference challenge, by accounting for regulatory signs, TRaCE+ could extract more information from the KO data than TRaCE, leading to fewer uncertain edges. Importantly, iterating TRaCE+ with an optimal design of gene KOs could resolve the underdetermined issue of GRN inference in much fewer KO experiments than using TRaCE. Conclusions TRaCE+ expands the applications of ensemble GRN inference strategy by accounting for the mode of the gene regulatory interactions. In comparison to TRaCE, TRaCE+ enables a better utilization of gene KO data, thereby reducing the cost of tackling underdetermined GRN inference. TRaCE+ subroutines for MATLAB are freely available at the following website: http://www.cabsel.ethz.ch/tools/trace.html.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
BioMed Central
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Design of experiments
en_US
dc.subject
Gene regulatory network
en_US
dc.subject
Network inference
en_US
dc.subject
Signed directed graph
en_US
dc.subject
Transitive reduction
en_US
dc.title
TRaCE+: Ensemble inference of gene regulatory networks from transcriptional expression profiles of gene knock-out experiments
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
BMC Bioinformatics
ethz.journal.volume
17
en_US
ethz.journal.abbreviated
BMC bioinformatics
ethz.pages.start
252
en_US
ethz.size
14 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.grant
Benchmarking, Assessment and Development of Methods for Biological Network Inference
en_US
ethz.grant
Ensemble Inference of Gene Regulatory Networks
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.identifier.nebis
004240301
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02020 - Dep. Chemie und Angewandte Biowiss. / Dep. of Chemistry and Applied Biosc.::02516 - Inst. f. Chemie- und Bioingenieurwiss. / Inst. Chemical and Bioengineering::03898 - Gunawan, Rudiyanto (ehemalig)
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02020 - Dep. Chemie und Angewandte Biowiss. / Dep. of Chemistry and Applied Biosc.::02516 - Inst. f. Chemie- und Bioingenieurwiss. / Inst. Chemical and Bioengineering::03898 - Gunawan, Rudiyanto (ehemalig)
ethz.grant.agreementno
137614
ethz.grant.agreementno
157154
ethz.grant.fundername
SNF
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
Projektförderung in Mathematik, Natur- und Ingenieurwissenschaften (Abteilung II)
ethz.grant.program
Projektförderung in Mathematik, Natur- und Ingenieurwissenschaften (Abteilung II)
ethz.date.deposited
2017-06-12T08:21:57Z
ethz.source
ECIT
ethz.identifier.importid
imp593654827120822180
ethz.ecitpid
pub:179832
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-12T13:49:01Z
ethz.rosetta.lastUpdated
2018-11-08T01:30:16Z
ethz.rosetta.versionExported
true
ethz.COinS
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