Data Exchange & Public Reference Data Computational Metabolomics, Schloss Dagstuhl, Germany Nov. 29-Dec. 4, 2015 David Wishart, University of Alberta

Public Metabolomics Repositories

Public Repositories Currently focused on capturing experimental data from metabolomics experiments Well defined file formats, good capture of meta-data, sustainable funding, aiming for permanence Relatively little “reference” data concerning pure compounds (no pure compound spectral data) – “Missing the reference layer” Struggling with data exchange and common formats

MS Spectral Databases

MS Spectral Databaes Currently focused on capturing referential data from pure compounds Fills the need that the public repositories are missing Variable file formats, limited capture of meta-data, non-sustainable funding, largely managed by single labs or individuals, limited exchange between centres except through targeted data harvesting Is there an opportunity to link these resources with the public repositories?

What Are We Missing? Updated and current data exchange standards (MSI is 10+ years old) Easy-to-use formats for entering meta-data Methods for keeping meta-data current with changing technologies Coordinated efforts in reference data collection and meta-data descriptions Databases on metabolite origins (linking metabolites to species or sources) Common methods formats for describing or classifying chemicals/metabolites (an ontology) Common formats and methods for exchanging pathway data (putting biology into metabolomics)

Databases on Metabolite Origins – Why? All other fields of omics track information of what genes/proteins/transcripts come from which species – unfortunately we don’t Many metabolites are gene-specific Many compounds come from specific sources – Source information is important for xenobiotics Avoids problems of false discovery and mis-identification Helps link metabolites to biology, helps with multi-omics integration

Databases on Metabolite Origins http://www.hmdb.ca http://www.T3DB.ca http://www.foodb.ca http://www.drugbank.ca

The Human Metabolome Database (HMDB) A web-accessible resource containing detailed information on 41,993 “quantified”, “detected” and “expected” metabolites Includes many food components & metabolites 100’s of drug metabolites 1000’s of xenobiotics >10,000 reference spectra Supports sequence, spectral, structure and text searches as well as compound browsing Full data downloads http://www.hmdb.ca

The Drug Database (DrugBank v. 4.3) 1602 small molecule drugs >5000 experimental drugs Detailed ADMET, MOA and pharmacokinetic data >1000 drugs with metabolizing enzyme data >1200 drug metabolites >600 MS+NMR spectra >4200 unique drug targets 208 data fields/drug Supports sequence, spectral, structure and text searches as well as compound browsing Full data downloads http://www.drugbank.ca

The Toxic Exposome Database (T3DB) Comprehensive data on toxic compounds (drugs, pesticides, herbicides, endocrine disruptors, drugs, solvents, carcinogens, etc.) Detailed mechanisms, binding constants, target info, lots of ToxCast data >3600 toxic compounds >1900 reference spectra ~2100 toxic targets Supports sequence, spectral, structure, text searches as well as compound browsing Full data downloads http://www.t3db.ca

The Food Database (FooDB) 26,619 compounds, 25,579 structures with 24,843 descriptions 171,359 synonyms ~700,000 concentration values 31,791 references 1376 cmpds with health effects 2692 cmpds with flavour data Content data on 907 raw or processed foods Supports structure & text searches >100 data fields/compound Full data downloads www.foodb.ca

Chemical Ontologies – Why? Every other field of omics has ontologies to describe genes & proteins – we don’t Helps link metabolites and chemistry to biology, helps with multi-omics integration Gives metabolomics researchers a common vocabulary Need to work on a shared standard

Classyfire Server A webserver (and database) designed to facilitate chemical classification and chemical description via structure alone 4800 chemical and chemical class definitions Accepts InChI or SMILES strings and generates classification in <1 s

Pathway Integration – Why? Pathways are a unique strength to metabolomics (it’s already beyond network “hairball” diagrams) Visualization is important in omics, helps improve integration possibilities Helps link metabolites and chemistry to biology, helps with multi-omics integration Need to work on a shared standard

PathWhiz Webserver designed to permit creation of colourful, biologically accurate pathway diagrams that are machine readable and interactive Supports BioPAX, SBML and SBGN conversion as well as SVG and PNG image generation Google Maps-style viewer http://smpdb.ca/pathwhiz

The Small Molecule Pathway Database (SMPDB) All pathways in SMPDB are generated via PathWhiz >700 small molecule pathways linked to HMDB, MetaboAnalyst Depicts cell compartments, organelles, protein locations, 4o structures SMPDB is able to map gene chip & metabolomic data Converts gene, protein or chemical lists to pathways or disease diagnoses http://www.smpdb.ca