Analysis of time-series regulatory networks

Ding, Jun; Bar‐Joseph, Ziv

doi:10.1016/j.coisb.2020.07.005

Cited by 11 publications

(2 citation statements)

References 66 publications

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“…The TimiRGeN R package has a suite of longitudinal analysis approaches for analyzing predicted miRNA–mRNA interacting pairs. This includes several correlation- and regression-based methods which are commonly used to analyze longitudinal datasets ( Ding and Bar-Joseph, 2020 ). Cross-correlation analysis is a useful method to determine similarity between two time series ( Fig.…”

Section: Resultsmentioning

confidence: 99%

TimiRGeN: R/Bioconductor package for time series microRNA–mRNA integration and analysis

et al. 2021

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Motivation The analysis of longitudinal datasets and construction of gene regulatory networks provide a valuable means to disentangle the complexity of microRNA-mRNA interactions. However, there are no computational tools that can integrate, conduct functional analysis and generate detailed networks from longitudinal microRNA-mRNA datasets. Results We present TimiRGeN, an R package that uses time point based differential expression results to identify miRNA-mRNA interactions influencing signalling pathways of interest. miRNA-mRNA interactions can be visualised in R or exported to PathVisio or Cytoscape. The output can be used for hypothesis generation and directing in vitro or further in silico work such as gene regulatory network construction. Availability and implementation TimiRGeN is available for download on Bioconductor (https://bioconductor.org/packages/TimiRGeN) and requires R v4.0.2 or newer and BiocManager v3.12 or newer. Supplementary information Supplementary data is available at Bioinformatics online.

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Section: Resultsmentioning

confidence: 99%

TimiRGeN: R/Bioconductor package for time series microRNA–mRNA integration and analysis

et al. 2021

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“…To advance beyond lists, clusters, and enrichment analysis, a complementary strategy, referred to as network science, instead targets the study of interactions between molecular entities, genotypes, and phenotypes [5], [6]. For example, gene regulation effectively acts via a network of interacting genes [7]. Notably, genes that interact with dysregulated genes without being differentially expressed themselves are often overlooked in differential expression studies [8].…”

Section: Introductionmentioning

confidence: 99%

White-box Deep Neural Network Prediction of Genome-Wide Transcriptome Signatures

Magnusson¹,

Jn²,

Gustafsson³

2021

Preprint

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Prediction algorithms for protein or gene structures, including transcription factor binding from sequence information, have been transformative in understanding gene regulation. Here we ask whether human transcriptomic profiles can be predicted solely from the expression of transcription factors (TFs). To this end, we explore whether a neural network (NN) could predict the transcriptome from TFs. Using at least one hidden layer, we find that the expression of 1,600 TFs can explain >95% of variance in 25,000 genes. Using the light-up technique to inspect the trained NN, we find an overrepresentation of known TF-gene regulations. Furthermore, the learned prediction network has a hierarchical organization. A smaller set of around 125 core TFs could explain close to 80% of the variance. Interestingly, reducing the number of TFs below 500 induces a rapid decline in prediction performance. Next, we evaluated the prediction model using transcriptional data from 22 human diseases. The TFs were sufficient to predict the target genes' dysregulation (rho=0.61, P < 10^-216). By inspecting the model, key causative TFs could be extracted for subsequent validation using disease-associated genetic variants. In conclusion, we demonstrate the construction of an interpretable neural network predictor. Analysis of the predictors revealed key TFs that were inducing transcriptional changes during disease.

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Plant Systems Biology: Lessons from Teaching

Lucas

2021

Methods in Molecular Biology

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Analysis of time-series regulatory networks

Cited by 11 publications

References 66 publications

TimiRGeN: R/Bioconductor package for time series microRNA–mRNA integration and analysis

TimiRGeN: R/Bioconductor package for time series microRNA–mRNA integration and analysis

White-box Deep Neural Network Prediction of Genome-Wide Transcriptome Signatures

Plant Systems Biology: Lessons from Teaching

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