Evidence map›Paper›PMID 27342648›Full record

ArticleBMC bioinformatics2016

TRaCE+: Ensemble inference of gene regulatory networks from transcriptional expression profiles of gene knock-out experiments.

S M Minhaz Ud-Dean, Sandra Heise, Steffen Klamt, Rudiyanto Gunawan

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Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.0field-weighted citation impact, top 21% of its field
1 · What the graph read from it

What it found

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 12 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors at 3 institutions in 2 countries.

S M Minhaz Ud-DeanInstitute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland.
Sandra HeiseMax Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.
Steffen KlamtMax Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.
Rudiyanto GunawanInstitute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland. rudi.gunawan@chem.ethz.ch.
Max Planck Institute for Dynamics of Complex Technical Systems · DEInstitute for Biomedical Engineering · CHSIB Swiss Institute of Bioinformatics · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

resultsIn 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.

conclusionsTRaCE+ 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 .

Indexed as

AlgorithmsGene Regulatory NetworksEscherichia coliGene Knockout TechniquesTranscriptomeDesign of experimentsGene regulatory networkNetwork inferenceSigned directed graphTransitive reduction

Identifiers

PMID27342648
PMCPMC4919846
OpenAlexW2467831893

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.