TSMiner: a novel framework for generating time-specific gene regulatory networks from time-series expression profiles

Abstract

Time-series gene expression profiles are the primary source of information on complicated biological processes; however, capturing dynamic regulatory events from such data is challenging. Herein, we present a novel analytic tool, time-series miner (TSMiner), that can construct time-specific regulatory networks from time-series expression profiles using two groups of genes: (i) genes encoding transcription factors (TFs) that are activated or repressed at a specific time and (ii) genes associated with biological pathways showing significant mutual interactions with these TFs. Compared with existing methods, TSMiner demonstrated superior sensitivity and accuracy. Additionally, the application of TSMiner to a time-course RNA-seq dataset associated with mouse liver regeneration (LR) identified 389 transcriptional activators and 49 transcriptional repressors that were either activated or repressed across the LR process. TSMiner also predicted 109 and 47 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways significantly interacting with the transcriptional activators and repressors, respectively. These findings revealed the temporal dynamics of multiple critical LR-related biological processes, including cell proliferation, metabolism and the immune response. The series of evaluations and experiments demonstrated that TSMiner provides highly reliable predictions and increases the understanding of rapidly accumulating time-series omics data.

Toward a hypothesis‐free understanding of how phosphorylation dynamically impacts protein turnover

“…The inference of half-life time for each peptidoform in the nonsteady state can be rather problematic due to the considerations mentioned in Section 1 (Introduction), especially for those peptidoforms carrying PTMs. Since the time-series data analysis algorithms are well-established for comparing expression of the same genes between conditions [59,[71][72][73], we reasoned that we can turn the H/L curvefitting problem into a time-series differential analysis with no prior assumption on PTM site-resolved turnover dynamics (Step 3, Figure 1). Thus, for the present PC12 dataset, after filtering out noisy signals, we performed a comparison against all H/L ratios for the paired P and NP peptidoforms.…”