enviPath – The environmental contaminant biotransformation pathway resource

The University of Minnesota Biocatalysis/Biodegradation Database and Pathway Prediction System (UM-BBD/PPS) has been a unique resource covering microbial biotransformation pathways of primarily xenobiotic chemicals for over 15 years. This paper introduces the successor system, enviPath (The Environmental Contaminant Biotransformation Pathway Resource), which is a complete redesign and reimplementation of UM-BBD/PPS. enviPath uses the database from the UM-BBD/PPS as a basis, extends the use of this database, and allows users to include their own data to support multiple use cases. Relative reasoning is supported for the refinement of predictions and to allow its extensions in terms of previously published, but not implemented machine learning models. User access is simplified by providing a REST API that simplifies the inclusion of enviPath into existing workflows. An RDF database is used to enable simple integration with other databases. enviPath is publicly available at https://envipath.org with free and open access to its core data.

[1]  Sean Ekins,et al.  Classification of Metabolites with Kernel-Partial Least Squares (K-PLS) , 2007, Drug Metabolism and Disposition.

[2]  Stefan Kramer,et al.  Data-driven extraction of relative reasoning rules to limit combinatorial explosion in biodegradation pathway prediction , 2008, Bioinform..

[3]  S Dimitrov,et al.  Probabilistic assessment of biodegradability based on metabolic pathways: CATABOL System , 2002, SAR and QSAR in environmental research.

[4]  Susumu Goto,et al.  PathPred: an enzyme-catalyzed metabolic pathway prediction server , 2010, Nucleic Acids Res..

[5]  Lior Rokach,et al.  Data Mining And Knowledge Discovery Handbook , 2005 .

[6]  Susumu Goto,et al.  Systematic Analysis of Enzyme-Catalyzed Reaction Patterns and Prediction of Microbial Biodegradation Pathways , 2007, J. Chem. Inf. Model..

[7]  Fangping Mu,et al.  Prediction of oxidoreductase-catalyzed reactions based on atomic properties of metabolites , 2006, Bioinform..

[8]  Boris Karulin,et al.  Ketcher: web-based chemical structure editor , 2011, J. Cheminformatics.

[9]  Lynda B. M. Ellis,et al.  The University of Minnesota Biocatalysis/Biodegradation Database: improving public access , 2009, Nucleic Acids Res..

[10]  Stefan Kotov,et al.  MetaPath: an electronic knowledge base for collating, exchanging and analyzing case studies of xenobiotic metabolism. , 2012, Regulatory toxicology and pharmacology : RTP.

[11]  Hiroyuki Ogata,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..

[12]  Alfonso Valencia,et al.  The environmental fate of organic pollutants through the global microbial metabolism , 2007, Molecular Systems Biology.

[13]  Philip N. Judson,et al.  Using Absolute and Relative Reasoning in the Prediction of the Potential Metabolism of Xenobiotics , 2003, J. Chem. Inf. Comput. Sci..

[14]  Susumu Goto,et al.  Data, information, knowledge and principle: back to metabolism in KEGG , 2013, Nucleic Acids Res..

[15]  Jörg Wicker,et al.  Large classifier systems in bio- and cheminformatics , 2013 .

[16]  Vassily Hatzimanikatis,et al.  Computational framework for predictive biodegradation , 2009, Biotechnology and bioengineering.

[17]  Grigorios Tsoumakas,et al.  Mining Multi-label Data , 2010, Data Mining and Knowledge Discovery Handbook.

[18]  S Dimitrov,et al.  Simulation of chemical metabolism for fate and hazard assessment. II CATALOGIC simulation of abiotic and microbial degradation , 2011, SAR and QSAR in environmental research.

[19]  Stefan Kramer,et al.  Predicting biodegradation products and pathways: a hybrid knowledge- and machine learning-based approach , 2010, Bioinform..

[20]  Lynda B. M. Ellis,et al.  The University of Minnesota pathway prediction system: predicting metabolic logic , 2008, Nucleic Acids Res..

[21]  Nina Jeliazkova,et al.  AMBIT‐SMARTS: Efficient Searching of Chemical Structures and Fragments , 2011, Molecular informatics.