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

BMC Bioinformatics

Methodology Article

Rudiyanto Gunawan¹  S.M. Minhaz Ud-Dean¹  Sandra Heise²  Steffen Klamt² 

[1] Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland;Swiss Institute of Bioinformatics, Lausanne, Switzerland;Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany;

关键词: Gene regulatory network;    Network inference;    Design of experiments;    Signed directed graph;    Transitive reduction;   

DOI  :  10.1186/s12859-016-1137-z

 received in 2016-03-01, accepted in 2016-06-12,  发布年份 2016

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s). 2016

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