When the effects of a pollutant on an organism are studied from various angles, namely different molecular levels, the information carried by the data as a whole can be more than the sum of its parts. Combining multi-omics datasets into a holistic analysis can increase statistical power, counterbalancing the limited number of individuals, and provide more comprehensive and interpretable results [1,2]. Here, we are interested in the effects of chronic exposure to low-dose radiation on the wildlife in the Chornobyl Exclusion Zone (CEZ). Studied as a sentinel species, tree frog Hyla orientalis populations were sampled in 2018 inside the CEZ and in neighboring non-contaminated areas. To assess ecological risks, several studies have already been conducted to evaluate the potential effects of radiation exposure on the phenotype and molecular changes [3,4]. Single-omics analysis performed on the transcriptome and proteome separately have both identified exposure-specific molecular signatures, with notable alteration in the gene expression of specific pathways such as energy metabolism [3, proteomics study in press]. Given the wealth of information collected over the course of this project, we sought to carry out an analysis that integrates the different omics datasets. In all high-throughput bioinformatics studies, careful pre-processing is crucial before performing any downstream analysis. We focus on mitigating confounding factors - such as the effect of the collection site on gene expression - and handling missing data in the proteomics dataset. We discuss the multi-omics integration methods employed and the results obtained, with the aim of providing new insights into the biological effects of chronic radiation exposure on Chornobyl tree frogs.