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

Abstract

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. 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 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 is available at https://github.com/ashleymaeconard/TIMEOR.git and http://timeor.brown.edu.

Computational approaches to understand transcription regulation in development

“…This is why most multi-omics approaches use the new modalities to prune the possible TF-target gene relations, which actually reduces the degrees of freedom [98,122,125,126]. Moreover, one can use time-series data to further prune TF-target gene interactions [169], although time-series multi-omics GRN inference tools are still relatively uncommon [170][171][172][173]. In addition, computational methods such as regularization [174] and dropout [175] constrain the problem in such a way that you end up with the simplest fit out of likely possible fits.…”

From multi-omics approaches to personalized medicine in myocardial infarction

“…Paitomics ( 382 ) integrates transcriptomics and metabolomics data, Quickomics ( 383 ) combines transcriptomics and proteomics data, and GeneTrail ( 384 ) encompasses genomics, transcriptomics, miRNAomics, proteomics, epigenetics, and single-cell data integration. TIMEOR ( 385 ) integrates genomics, transcriptomics, and proteomics data to unveil temporal changes in gene regulatory networks. Omics Integrator ( 386 ) uses network optimization algorithms to integrate proteomics, gene expression, and epigenetics data.…”

ExpOmics: a comprehensive web platform empowering biologists with robust multi-omics data analysis capabilities

“…Furthermore, customizable data analysis is often required to address specific biological questions, emphasizing the necessity for user-friendly web platforms to enable efficient investigation of these complex and diverse data and advance life science research (Mougin, et al 2018; O’Donoghue et al 2010). To tackle these challenges, bioinformaticians have made efforts and developed several web platforms to explore and analyze multi-omics data (Chang et al 2018; Cheng et al 2021; Conard et al 2021; Ge et al 2018; Liu et al 2022; Zhou et al 2021).…”

“…However, there still remain some critical issues that urgently need to be addressed. For example, ExpressVis (Liu, et al 2022), eVITTA (Cheng, et al 2021), PANDA-view (Chang, et al 2018), iDEP (Ge, et al 2018), and TIMEOR (Conard, et al 2021) primarily focus on gene and protein expression data, catering to specific species like Homo sapiens and model organisms. Consequently, they lack the capability to handle non-coding gene expression data and data from other organisms, including lncRNA, circRNA, miRNA, and piRNA.…”