Gene expression is a tightly regulated process that involves multiple layers of control, including transcriptional, post-transcriptional, and translational regulation. To gain a comprehensive understanding of gene expression dynamics and its functional implications, it is crucial to compare translatomic, transcriptomic, and proteomic data. The two most common analysis methods, Ribo-seq and RNC-Seq, were used to analyze the translatome of the same sample, whose datasets were downloaded from the TranslatomeDB database. The resulting translatome maps obtained for three cell lines (HBE, A549, and MCF-7) using these two methods were comparatively analyzed. The two methods of translatome analysis were shown to provide comparable results and can be used interchangeably. The obtained mRNA translation patterns were annotated in the transcriptome and proteome context for the same sample, which may become the basis for the reconstruction of the molecular mechanisms of pathological process development in the future.

Integrating proteomic and metabolomic data remains challenging due to the many-to-many relationships between metabolites and proteins and the spatial constraints of cellular compartmentalization. To address this, we developed PMconv, a web-based application for bidirectional, knowledge-based mapping of proteomic and metabolomic datasets. Leveraging curated associations from the Human Metabolome Database (HMDB) and protein interaction data from STRING, PMconv infers potential biochemical connections between experimentally detected molecules and pathway-annotated partners. The tool supports interactive network visualization and exports compartment annotations from the Human Protein Atlas to facilitate spatial contextualization of inferred interactions. PMconv is designed as an exploratory resource for hypothesis generation and feature engineering in multi-omics research, with the explicit understanding that knowledge-derived associations require experimental validation for compartment-specific interpretation.