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

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.

Evaluation and improvement of the regulatory inference for large co-expression networks with limited sample size

“…As a large regulatory network is often under-determined using a small number of samples, there exists multiple plausible solutions, which cannot be distinguished by the information presented in the sample. This uncertainty in the inference of gene regulatory networks has been termed in some studies as “inferability” [ 54 , 55 ]. Although our study mainly focuses on the network inference methods, special attention should be paid to generate the most informative data when trying to construct the accurate and comprehensive underlying GRNs.…”

Motifs enable communication efficiency and fault-tolerance in transcriptional networks

“…TRNs are represented as directed, signed graphs in which nodes represent genes or transcription factors (TFs) and edges correspond to the regulations of target genes by TFs 31 . Directed edges of TRNs are assigned positive or negative signs, indicating that the TF respectively increases or decreases the rate of transcription when it binds the promoter of the gene [32][33][34] .…”

scmTE: multivariate transfer entropy builds interpretable compact gene regulatory networks by reducing false predictions

“…Many approaches have been developed for network inference using bulk-RNAseq data using ensembles of regression trees (Huynh-Thu, et al, 2010) or repeated subsampling followed by model training (Morgan, et al, 2020). TRaCE+ utilizes ensembled expression profiles from various knock-out (KO) experiments to define the upper and the lower bound of the networks (Ud-Dean, et al, 2016) and requires multiple genes and multiple linear regression (MLR) to avoid indirect relationships (Salleh, et al, 2017). Even with their efforts, above approaches could not effectively remove indirect relationships.…”