A Bioinformatic Framework to Assess the Genomic Response of Species to Environmental Toxins

The current ecotoxicological methods of identifying sites of biological cannot indicate the presence of hazardous chemicals before significant environmental damage has already occurred. In this thesis, I hypothesize that essential pathways and genes are conserved across the taxa and present a bioinformatic pipeline that uses genomic data for environmental monitoring. This pipeline mines chemical-gene interaction data from publicly available databases, retrieves the corresponding protein sequences and performs homology-based searches in the genome of target organisms. Afterwards, it clusters these protein sequences to obtain groups of orthologous sequences, retrieves related biological pathways and learns a Bayesian Network to show meaningful correlations between them and input chemicals. To demonstrate this pipeline, I ran a test dataset consisting of a group of phylogenetically distant taxa on it. The shared affected pathways suggested by this pipeline correspond with findings from previous studies on the toxic effects of target chemicals.