BioRemPP: a web application to identify the potential of bacteria, fungi and plants for bioremediation of environmental priority pollutants
bioremediation; environmental pollutants; genomic analysis; metabolic pathways; environmental toxicology
Bioremediation is a sustainable approach to mitigate environmental pollution caused by industrial, agricultural, and urban activities. This study introduces BioRemPP (Bioremediation Potential Profile), a platform designed for analyzing genomic, metabolic, functional, and toxicological data related to the bioremediation potential of bacteria, fungi, and plants to break down priority pollutants. The platform integrates curated data from databases such as KEGG, PubChem, ChEBI, HADEG, and ToxCSM, as well as environmental agencies and regulatory frameworks. BioRemPP uses Python libraries like Pandas and NumPy for data manipulation, scikit-learn for pattern recognition, and Matplotlib and Plotly for visualization. Built with the Dash framework, the interface offers a dynamic and intuitive user experience. User-submitted input data, structured as KEGG Ortholog identifiers and sample IDs, undergo processing to map relationships with environmental pollutants. The platform integrates data on 324 compounds and 986 genes, establishing connections between priority pollutants, enzymes, degradation pathways, and toxicity profiles. The platform processes the data into seven result sections, and outputs include tables and visualizations detailing compounds, genes, and metabolic pathways related to pollutant degradation potential. Interactive features, including 20 chart types, enable multi-dimensional data analysis.