Chemical ontologies: what are they, what are they for, and what are the challenges
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Presented at the 2010 German Conference on Chemoinformatics, in Goslar, Germany, 8 November 2010.
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Chemical ontologies: what are they, what are they for, and what are the challenges
- 1. EBI is an Outstation of the European Molecular Biology Laboratory. Chemical Ontologies What are they? What are they for? What are the challenges? Janna Hastings, EBI Chemoinformatics and Metabolism 6th German Conference on Chemoinformatics, Goslar, 8 November 2010
- 2. Problem How do we find the information we need? Data deluge Multiple databases, heterogeneous data Ambiguity, multiple synonyms J. Hastings Chemical Ontology30.01.152 Data lost in
- 3. J. Hastings Chemical Ontology30.01.153 Intelligent systems The answer is 42 I’ll show you why
- 4. Logical inference All men are mortal Socrates is a man Therefore, Socrates is mortal J. Hastings Chemical Ontology30.01.154 finding the implications of what you know
- 5. J. Hastings Chemical Ontology30.01.155 Community terminological standardisation Dictionary: synonyms, definitions Hierarchical organisation Logical model allowing computer inferences beyond what is explicitly encoded Knowledge-based applications
- 6. Ontologies to filter and organise data J. Hastings Chemical Ontology30.01.156 The Web Ontology Language (OWL) Hierarchical organisation Synonyms Cross-references Logical definitions Can be re-used in multiple applications root leaves
- 7. J. Hastings Chemical Ontology30.01.157 ChEBI Ontology Chemical entity Role catecholamines Biological role Application hormone vasodilator agent (R)-adrenaline CHEMINF Ontology DescriptorSoftware library ACD Labs logP -.539 -2.369 logD
- 8. J. Hastings Chemical Ontology30.01.158 Chemical entity carboxylic acid acetylsalicylic acid (aspirin) chlorfenvinfos organophosphorous compound aldehyde organic molecular entity inorganic molecular entity pyridoxal (vitamin B6) sodium chloride Molecular entity Group hydroxy group Chemical substance
- 9. J. Hastings Chemical Ontology30.01.159 Role analgesic acetylsalicylic acid (aspirin) chlorfenvinfos insecticide vitamin pyridoxal (vitamin B6) Biological role Application drug pesticide Chemical role acid sulfuric acid
- 10. J. Hastings Chemical Ontology30.01.1510 Chemical information entity DescriptorSoftware library CDK logP OpenBabel Algorithm Molecular Descriptor implements calculates Substance Descriptor atom count boiling point melting point largest chain fused cycles Hueckel’s aromaticity
- 11. J. Hastings Chemical Ontology30.01.1511 Chemical database Bioactivity database Bioactivity database Metabolism database Metabolism database Pathway database LiteratureLiterature Chemical entities Roles Properties Unified browsing and querying
- 12. Disentangling hierarchies Disambiguating terminology Ontology representation in a complex domain J. Hastings Chemical Ontology30.01.1512 Sounds great, but... What are the challenges?
- 13. Chemicals and roles J. Hastings Chemical Ontology30.01.1513 de Matos, P. et al: Chemical Entities of Biological Interest: an update. NAR Database issue 2010 vitamin hormone neurotransmitter CNS stimulant carboxylic acid peptide trimethylxanthine polycyclic cage has role
- 14. Chemicals and structures J. Hastings Chemical Ontology30.01.1514 J. Hastings, C. Batchelor, C. Steinbeck, S. Schulz: What are chemical structures and their relations? FOIS 2010 chemical entity molecule chemical graph molecular structure has attribute What is the structure of Vancomycin?
- 15. Representing complex structures J. Hastings Chemical Ontology30.01.1515 Chemical classes can be defined by parts of structures and/or properties of structures carboxylic acid cyclic molecule if molecule has part some carboxy group if molecule has property cyclic, i.e. a self-connected cyclic path exists through the molecule’s atoms
- 16. J. Hastings Chemical Ontology30.01.1516 Pre-compute and assert all parts and properties Represent atoms and bonds in ontology Integration of chemoinformatics and ontology toolsIntegration of chemoinformatics and ontology tools J. Hastings et al.: Representing chemicals using OWL, description graphs and rules. OWLED 2010
- 17. Purpose and mode of action J. Hastings Chemical Ontology30.01.1517 epinastine application antiallergic drug is a biological role histamine antagonist is a has rolehas role C. Batchelor, J. Hastings, C. Steinbeck: Ontological dependence, dispositions and institutional reality in chemistry. FOIS 2010 Single molecule Independent of intent Bulk quantity of molecules Depends on human intent (e.g. license, prescription)
- 18. J. Hastings Chemical Ontology30.01.1518 Conditions in bioactivity models Consider aspirin as treatment for a headache Too few individual molecules will have no effect Too many tablets will have unpleasant additional effects Image credit: tell.fll.purdue.edu J. Hastings, C. Steinbeck, L. Jansen, S. Schulz: Substance concentrations as conditions for the realization of dispositions. ISMB Bio-Ontologies SIG 2010
- 19. J. Hastings Chemical Ontology30.01.1519 Christoph Steinbeck Paula de Matos Marcus Ennis Steve Turner Adriano Dekker Kenneth Haug Rafael Alcántara Martin Zara Josephs Pablo Moreno Kalai Jayaseelan Mark Rijnbeek Nico Adams Colin Batchelor, RSC Stefan Schulz, Freiburg Egon Willighagen, Uppsala Michel Dumontier, Carleton Leonid Chepelev, Carleton
Notes de l'éditeur
- Researchers in the increasingly data-overridden scientific domains face ever-growing difficulty in working their way through the mounds of data spread across different resources, interfaces, languages and databases
- We need more and more use of computational tools to intervene between the mountains of distributed, heterogeneous data. We need annotations to shared, controlled IDs, in order to harmonise data across different heterogeneous sources.
- The human mind is an amazing thing: most people are able to correctly answer very quickly when asked the following questions: Are there any footprints on the moon? (YES) Are there any purple dogs on the moon? (NO) (nor bats, nor dinosaurs, nor trees...) How do they do that? They are not taught itineraries of what things are on the moon in high school. Rather they are taught the simple fact that there is no life on the moon at all. From this fact they are able to infer that there are no purple dogs on the moon, because purple dogs are a kind of life form.
- What is an ontology? It is at least all of these things: a community-wide standardised terminology and dictionary of terms in a particular domain; a hierarchically organised map of entities in the domain; a logical model which allows compact representation but logical inference to additional implications; and a tool which supports multiple, knowledge-based applications.
- Ontologies are organised hierarchically from a very general root term to the most specialised leaf terms (utility: grouping items at different levels) They gather together synonyms and other metadata (utility: ‘glue’ for data integration) They provide logical definitions to allow automatic inferences thus providing a compact storage mechanism (utility: automated reasoning and query answering) They therefore provide a sophisticated searching and organising medium for multiple applications And there is one standard (OWL) format for ontology development which is supported by many tools and resources
- This slide illustrates our chemical ontologies (currently in development at the EBI and with collaborators)
- Software libraries implement algorithmsAlgorithms calculate descriptors Descriptors are about chemical entities of various sorts (molecules, substances, atoms...)
- Now, because you have a single ontology on top of multiple annotations across several databases (a standard), you can perform cross-database querying for data related to the same thing. But that’s not all – not only can you query across several databases, but your query is semantic – it *knows* that leukemia is a kind of cancer, and you don’t have to implement a custom search solution in each database capable of inferring this, because the hierarchy and the synonyms lives outside of any one database – in the community-wide shared ontology. Image: different databases, literature resources. Organising ontology: semantic searching, multi-level aggregation.
- What are the challenges?
- Many chemical classification systems do not differentiate between structure-based and role-based classification systems (e.g. MeSH). They therefore say that caffeine IS A `cns stimulant’ in exactly the same way that they say caffeine IS A ‘trimethylxanthine’. Humans can distinguish between these two types of classification and make correct inferences, but it leads to invalid inferences when computers are asked to reason over the classification, since the terms on the left share structural features while those on the right do not; the terms on the left are ‘timeless, condition-less’ properties of the chemical entities while the terms on the right describe context-specific behaviour of chemical entities. We therefore separated the structure-based and role-based classifications and introduced the has-role cross-ontology relationship. A term such as `antibiotic’ is ambiguous in sense between meaning an <activity> (role) and a particular chemical entity which may have that activity.
- In common language (particularly in the realm of databases), chemical ‘structure’ and chemical ‘entity’ are referred to synonymously. For example the GDB database refers to its total size in terms of ‘organic structures’ while calling itself a database of ‘molecules’. However, it is crucial to differentiate these senses in classification, since it is possible to have a chemical entity and not know its structure, or be mistaken about its structure (e.g. vancomycin).
- If you pre-compute all parts of a molecule and all properties, you can make ontology definitions for classes which use those properties BUT your ontology becomes very, very large in asserted parts/propertiesBetter is if, at least for simple properties and parts, the minimal information needed to deduce the relationship can be included in the ontology itself
- Research in our group is investigating the applicability of the new ontology extension description graphs for addition of elements of chemical structures to the ontology to allow structure-based classification to be more automated in easier cases. Difficulty is that this appears to be reinventing a wheel that has already been well invented by the cheminformatics community, and our challenge moving forward is to bring in the cheminformatics libraries and toolkits and integrate them with the ontology ones.
- One of the challenges which we are investigating is to accurately include in the ontology model the relevant conditions under which bioactivity holds. These conditions might be concentrations of the active substance in the organism, or the organism itself. These conditions are often THRESHOLD phenomena, that is, it is not sufficient to merely indicate a fixed border at which an effect starts to take place.