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

2. Public Metabolomics Repositories

3. 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

4. MS Spectral Databases

5. 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?

6. 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)

7. 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

8. Databases on Metabolite Origins

9. 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

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. 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