Thousands of different contaminants released into the environment – including pharmaceuticals, personal care products, and pesticides – negatively affect ecosystem and human health. Microbes can help by breaking down these chemicals naturally through a process known as biotransformation, but microbial biotransformation behavior within complex communities is challenging to interpret. Sequencing total DNA from an environment is commonly used to monitor microbial communities, but the link between DNA sequencing information and the observed breakdown of contaminants in the environment remains unpredictable. To address this knowledge gap, we curated an open research data (ORD) catalogue of microbial biotransformation genes and generated a collection of 192 specialized hidden Markov (HMM) models: (doi: 10.5281/zenodo.14871403). These ORD resources enable users to rapidly query DNA and/or RNA sequencing data to predict the biotransformation potential of microbial communities. Overall, this work makes a key contribution to the ORD platform enviPath (envipath.org) by supporting its longevity and connection to biological datasets, expanding its global user base, and promoting technologies for environmental remediation.