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

Uncovering how transcription factors regulate their targets at DNA, RNA and protein levels over time is critical to define gene regulatory networks (GRNs) and assign mechanisms in normal and diseased states. RNA-seq is a standard method measuring gene regulation using an established set of analysis stages. 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 integrating ordered RNA-seq data with protein–DNA binding data to distinguish direct from indirect interactions are urgently needed.

Brown University researchers have developed 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 determine causal regulatory mechanism networks by leveraging time-series RNA-seq, motif analysis, protein–DNA binding data, and protein-protein interaction networks. TIMEOR’s user-catered approach helps non-coders generate new hypotheses and validate known mechanisms. We used TIMEOR to identify a novel link between insulin stimulation and the circadian rhythm cycle.

TIMEOR enables users to interrogate and reconstruct gene regulatory networks

(A) Users specify a time-series RNA-seq data set in the Pre-process Stage in which the data are automatically retrieved, normalized, corrected, filtered and aligned to the selected organism reference genome using multiple alignment methods for comparison. Importantly, users may also input the count matrix directly, skipping to normalization and correction in step 3 of stage A. (B) The resulting count matrix is passed to Primary Analysis where multiple differential expression (DE) methods are run to produce a time-series clustermap of gene DE trajectories over time. (C) DE results are passed to Secondary Analysis for gene ontology, pathway, network, motif, protein–DNA binding data analysis, and gene regulatory network reconstruction. TF: transcription factor.