OmicIntegrator: A Simple and Versatile Tool for Meta-Analysis

Abstract

We developed OmicIntegrator, a broadly adaptable pipeline designed to standardize and integrate publicly available transcriptomic, proteomic, and phosphoproteomic datasets. We applied this workflow to Arabidopsis thaliana etiolated seedlings to identify protein kinases and phosphatases relevant to skotomorphogenic development, a phase during which seedlings rely on tightly regulated signaling networks to ensure survival in darkness. This meta-analysis provided a comprehensive view of gene and protein expression, revealing discrepancies between transcript and protein abundance, suggesting post-transcriptional and post-translational regulation. By integrating multiple datasets, OmicIntegrator reduces experimental bias and enables the detection of phosphorylation events that may be missed in single-condition studies. Distinct phosphorylation patterns were detected across different protein kinase families. Motif enrichment analysis showed a strong overrepresentation of RxxS motifs among phosphosites in protein phosphatases and microtubule-associated proteins, consistent with potential regulation by calcium-dependent protein kinases (CPKs). Across omics layers, CPK3 and CPK9 repeatedly emerged as prominent candidates, highlighting them as priorities for future functional studies in skotomorphogenesis. Overall, our results demonstrate the power of OmicIntegrator as a flexible framework to contextualize signaling landscapes and identify robust patterns and candidate genes and for generating testable hypotheses from integrated multi-omics data in plant developmental biology.