enviPath--The environmental contaminant biotransformation pathway resource

Area of Science:

• Environmental science
• Biotechnology
• Computational biology

Background:

• The University of Minnesota Biocatalysis/Biodegradation Database and Pathway Prediction System (UM-BBD/PPS) has served as a key resource for microbial biotransformation pathways for over 15 years.
• There was a need for a modernized system with extended capabilities and improved accessibility.

Purpose of the Study:

• Introduce enviPath (The Environmental Contaminant Biotransformation Pathway Resource) as the successor to UM-BBD/PPS.
• Describe the redesign, reimplementation, and new features of enviPath.
• Highlight the system's accessibility and integration capabilities.

Main Methods:

• Redesign and reimplementation of the UM-BBD/PPS system.
• Utilized the existing UM-BBD/PPS database as a foundation.
• Incorporated relative reasoning for prediction refinement and integration of machine learning models.
• Implemented a REST API for simplified user access and an RDF database for data integration.

Main Results:

• enviPath offers a comprehensive and redesigned platform for microbial biotransformation pathways.
• The system supports user-added data and multiple use cases.
• Enhanced prediction refinement through relative reasoning and machine learning integration.
• Simplified integration into existing workflows via REST API and RDF database.

Conclusions:

• enviPath represents a significant advancement over UM-BBD/PPS, providing a more robust and user-friendly resource.
• The system facilitates research in environmental contaminant biodegradation and biotransformation.
• enviPath is publicly available, promoting open access to valuable data and tools.