TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data

Abstract

SummaryUncovering how transcription factors (TFs) regulate their targets at the DNA, RNA and protein levels over time is critical to define gene regulatory networks (GRNs) in normal and diseased states. RNA-seq has become a standard method to measure gene regulation using an established set of analysis steps. However, none of the currently available pipeline methods for interpreting ordered genomic data (in time or space) use time series models to assign cause and effect relationships within GRNs, are adaptive to diverse experimental designs, or enable user interpretation through a web-based platform. Furthermore, methods which integrate ordered RNA-seq data with transcription factor binding data are urgently needed. Here, we present TIMEOR (Trajectory Inference and Mechanism Exploration with Omics data in R), the first web-based and adaptive time series multi-omics pipeline method which infers the relationship between gene regulatory events across time. TIMEOR addresses the critical need for methods to predict causal regulatory mechanism networks between TFs from time series multi-omics data. We used TIMEOR to identify a new link between insulin stimulation and the circadian rhythm cycle. TIMEOR is available at https://github.com/ashleymaeconard/TIMEOR.git.

Neuromolecular and behavioral effects of ethanol deprivation inDrosophila

“…Analysis of the RNAseq data was done using Trajectory Inference and Mechanism Exploration with Omics data in R (TIMEOR) [57], which included DESeq [173] followed by Wald’s test, which tests the probability of finding the same (or higher) log-fold change in expression between two groups by chance. TIMEOR was run in the command line and through the web-interface (via RShiny) [174] to process all replicates for all three timepoints, automatically cluster genes based on inferred gene trajectory dynamics, and produce temporal and per time point gene ontology (GO) analysis results.…”

“…We compiled a list of differentially expressed genes (using the less conservative p<0.05 cut-off) from each condition (Data S9). The resulting 430 differentially expressed genes were then automatically clustered into nine groups representing similar gene dynamic trajectories, using an unbiased clustermap [56] within the Trajectory Inference and Mechanism Exploration with Omics data in R (TIMEOR) analysis software [57] ( Figure 5A, S6, Data S10). Clusters 3, 4 and 5 had significantly enriched gene ontology (GO) terms.…”

Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data

“…dream (differential expression for repeated measures) [60]: This dynamic method has been implemented within a variance Partition Bioconductor package, incorporated with limma and voom. And this method has been applied for the large-scale of cohort studies [63,86] with multiple condition groups (e.g., four different brain regions) in a multiseries of longitudinally-measured time course RNA-Seq data. This method is based on linear mixed models to account for arbitrary multiple random effects for a given particular gene, varying variances terms across genes, precision weight functions considering random effects for samples within-individual, and small sample issues for hypothetical testing with Kenward-Roger approximation.…”

dynUGENE: an R package for uncertainty-aware gene regulatory network inference, simulation, and visualization

“…We build on dynGENIE3 because it satisfies all three of our model desiderata. Existing extensions include TIMEOR and BENIN which both incorporate heterogeneous data to improve network inference accuracy (Wonkap and Butler, 2020; Conard et al, 2020). Here, we take a different approach and instead account for uncertainty in dynGENIE3, allowing for stochastic gene expression simulations and parsimonious model selection.…”