An Introduction to Anatomy Ontologies Phenotype RCN Feb 23, 2012

1. An Introduction to Anatomy OntologiesPhenotype RCN Feb 23, 2012 Melissa Haendel

2. Setting the stage Who are we? What do we need? Why are we here? What is an anatomy ontology? What kinds of anatomy ontologies exist? How are anatomy ontologies used? Anatomical evidence

3. Who are we? Domain Experts: Anatomists, comparative morphologists, developmental biologists, immunologists, neuroscientists, etc. Engineers: have to build tools that can consume ontologies and give the Domain Experts the right results Domain experts: want to query for gene expression and phenotypes across species Ontologists: have to be able to interpret and represent domain knowledge computationally Ontologists: Biologists-gone-informatics, computer scientists and logicians Engineers: Our tool builders

4. We want to enable: • Comparison of structures across different organisms, scales • Standardization of anatomical vocabulary among and between communities • Integration of anatomical data across databases • Query across large amount of data • Automatic reasoning to infer related classes • Error checking • Annotation consistency Therefore, we build ontologies that are intelligible to: Engineers Domain experts Machines Ontologists

5. Anatomical information retrieval from text-based resources Less than ideal.

6. Why build an anatomy ontology? A simple example Number of genes annotated to each of the following brain parts in an ontology: brain 20 part_of hindbrain 15 part_ofrhombomere10 Query brain without ontology 20 Query brain with ontology 45 Ontologies can facilitate grouping and retrieval of data

7. There are many useful ways to classify parts of organisms: • its parts and their arrangement • its relation to other structures what is it: part of; connected to; adjacent to, overlapping? • its shape • its function • its developmental origins • its species or clade • its evolutionary history Cajal 1915, “Accept the view that nothing in nature is useless, even from the human point of view.”

8. An ontology is a classification appendage antenna wing fore wing hindwing

9. Relationships record classifications too ‘leg’ SubClassOfpart_ofsome thoracic segment part_ofsome ‘thoracic segment leg wing

10. Multiple inheritance is very hard to manage by hand • It is difficult to keep track of multiple • classification chains to: • ensure completeness; • avoid redundancy; • Incorrectinheritance of classification criteria from a distant superclass

11. The knowledge in an ontology can make the reasons for classification explicit Any sense organ that functions in the detection of smell is an olfactory sense organ capable_ofsome detection of smell olfactory sense organ sense organ

12. Classifying capable_ofsome detection of smell sense organ nose nose olfactory sense organ capable_ofsome detection of smell sense organ nose

13. Compositionality and avoiding asserted multiple inheritance Let the reasoner do the work! • We can logically define composed classes and create complex definitions from simpler ones • aka: building blocks, cross-products, logical definitions • Descriptions can be composed at any time • Ontology construction time (pre-composition) • Annotation time (post-composition) • Formal necessary and sufficient definitions + a reasoner • Automatic (and therefore manageable) classification • Requires subtype classification, so apart from the root term(s), no term should lack an is_a parent.

14. Example of a post-composed anatomical entity Plasma membrane of spermatocyte • Plasma membrane[GO CC] • Spermatocyte[Cell Ontology] Genus Differentia a plasma membranewhich is part_of a spermatocyte Gene Ontology Basic Formal Ontology Cell Ontology

15. Many perspectives, many ontologies behavior gross anatomy clinical disorders nervous system processes phenotypes evolutionary characters development physiological processes neural crest tissues cells cellular processes cell anatomy proteins reactions chemical entities

16. What kinds of anatomy ontologies exist? Species-centric and multi-species ontologies Species neutral ontologies Mouse • MA (adult) • EMAP / EMAPA (embryonic) Human • FMA (adult) • EHDAA2 (CS1-CS20) Amphibian • AAO • XAO Fish • ZFA (zebrafish) • MFO (medaka) • TAO (teleosts) Nematode • WBbt (c elegans) Arthropod • FBbt (Drosophila) • TGMA (Mosquito) • HAO (hymenoptera) • Arthropod anatomy ontology Plant ontology CARO (common anatomy reference ontology) Uberon (cross-species anatomy) vHOG (vertebrate homologous organs) CL (cell ontology) GO (gene ontology) Phenotype ontologies MP mammalian phenotype HP human phenotype WB worm phenotype

17. Species-centric ontologiesThe Zebrafish Anatomy Ontology Used to record gene expression and phenotypes at different stages of development

18. Ontologies built for one species will not work for others http://ccm.ucdavis.edu/bcancercd/22/mouse_figure.html http://fme.biostr.washington.edu:8080/FME/index.html

19. Multi-species anatomy ontologies The Plant Ontology Seed plants (Angiosperms and Gymnosperms) Pteridophytes (Ferns and Lycopods) Bryophytes (Mosses, Hornworts and Liverworts) Algae Challenge is in representing diversity in anatomy, morphology, life cycles, growth patterns Bowman et al, Cell, 2007

20. Example of complexity arising from multiple species-contexts cell nucleate cell enucleate cell not applicable in all contexts erythrocyte

21. Example of complexity arising from multiple species-contexts cell species ontologies attached at appropriate level nucleate cell enucleate cell CL:0000232 erythrocyte … … CL:0000592 CL:0000562 nucleate erythrocyte enucleate erythrocyte zebrafish nucleate erythrocyte human erythrocyte FMA:81100 ZFA:0009256

22. Using reasoners to detect errors only_in_taxon UBERON: bone Vertebrata disjoint with is_a is_a Drosophila melanogaster Homo sapiens UBERON: tibia is_a is_a ✗ part_of part_of Fruit fly FBbt ‘tibia’ Human FMA ‘tibia’ Developmental Biology, Scott Gilbert, 6th ed.

23. The Gene Ontology has an anatomy ontology zebrafish Look ma, no pons! human

24. Phenotype ontologies also have inherent anatomy WBbt C. elegans phenotype Designed primarily for annotation of phenotypes within a single species

25. Representingdifferent levels of granularity GO lateral line development neuromast development ? hair cell development neuromast part_of lateral line ? hair cell part_ofneuromast cilium development cilium part_ofhair cell part_ofneuromast

26. The problem:Data Silos is_a (SubClassOf) part_of GO develops_from FMA surrounded_by multicellularorganismal process EHDAA2 organ system solid organ pharyngeal region respiratory gaseous exchange respiratory primordium respiratory system parenchymatous organ lung bud respiratory system process lung MA Lower respiratory tract lobular organ thoracic cavity organ system MPO abnormal respiratory system morphology thoracic cavity organ respiratory system pleural sac lung abnormal lung morphology lung abnormal pulmonary acinus morphology pulmonary acinus abnormal pulmonary alveolus morphology alveolar sac lung alveolus

27. How to synchronize anatomy ontologies Three approaches: • Mapping • Direct reconciliation • Synchronization using imports/MIREOT

28. There are issues with mappings

29. Zebrafish terms are is_asubtypes of teleost terms Reconciliation and linking between TAO and ZFA Teleost Anatomy Ontology Zebrafish Anatomy is_a Logic implemented via Xrefs- difficult to keep synchronized

30. The Common Anatomy Reference Ontology CARO is a structural classification based on granularity From the bottom up: Cell component Cell Portion of tissue Multi-tissue structure From the top down: Organism subdivision Anatomical system Acellular structures Note: CARO is being updated to be more interoperable, include logical definitions, and functional differentia

31. Synchronization by import across ontologies CARO VAO Present TAO Modularized ontology One can import a whole ontology or just portions of another ontology MIREOT: Minimum information to reference an external ontology term

32. Uberon – a multi-species ontology for phenomics and evo-devo analyses Uberon.org

33. Uberon classes generalize species-specific ones, and connect to other ontologies via a variety of relations is_a (SubClassOf) anatomical structure part_of develops_from capable_of endoderm is_a (taxon equivalent) only_in_taxon organ part foregut swim bladder organ endoderm of forgut NCBITaxon: Actinopterygii respiration organ respiratory primordium GO: respiratory gaseous exchange pulmonary acinus alveolus lung lung primordium NCBITaxon: Mammalia alveolus of lung alveolar sac lung bud FMA: pulmonary alveolus FMA:lung MA:lung alveolus MA:lung EHDAA: lung bud

34. OntoFox: a Web Server for MIREOTing • Good things: • Based on MIREOT principle • Web-based data input and output • Output OWL file can be directly imported in your ontology • No programming needed • Programmatically accessible • Improvements: • Integration into ontology editing tools • More customizable http://ontofox.hegroup.org

35. Proposed model moving forward • Maintain series of ontologies at different taxonomic levels - euk, plant, metazoan, vertebrate, mollusc, arthropod, insect, mammal, human, drosophila • Each ontology imports/MIREOTs relevant subset of ontology “above” it - this is recursive • Subtypes are only introduced as needed • Work together on commonalities at appropriate level above your ontology

36. Leveraging an integrated set of ontologies cross-ontology link (sample) caro /uberon/all cell tissue import metazoa skeleton nervous system gut gonad appendage circulatory system gland mesoderm respiratory airway larva muscle tissue skeletal tissue mollusca arthropoda vertebrata trachea bone mantle mushroom body limb fin vertebra tibia shell cuticle vertebral column foot antenna mesonephros parietal bone cephalopod drosophila teleost mammalia amphibia tentacle neuron types XYZ weberianossicle mammary gland tibiafibula brachial lobe mouse human zebrafish NO pons

37. Not all classification is useful About thirty years ago there was much talk that geologists ought only to observe and not theorise; and I well remember some one saying that at this rate a man might as well go into a gravel-pit and count the pebbles and describe the colours. C. Darwin Be practical: Build ontologies for what you need and for what can be reused

38. Ontologies can help reconcile annotation inconsistencies

39. Semantic Similarity of Phenotypes MP ZFA+PATO FBbt+PATO FMA+PATO "Linking Human Diseases to Animal Models Using Ontology-Based Phenotype Annotation." PLoSBiol 7(11): e1000247. doi:10.1371/journal.pbio.1000247 Washington NL, Haendel MA, Mungall CJ, Ashburner M, Westerfield M, Lewis SE

40. Querying for genes in similar structures across species polychaeteparapodia ascidian ampulla A B Vertebrata tetrapod limbs Ascidians ampullae Echinodermata tube feet sea urchin tube feet mouse limb Arthropoda C D E Annelida parapodia Mollusca Distal-less orthologs participate in distal-proximal pattern formation and appendage morphogenesis Panganibanet al., PNAS, 1997

41. Anatomy ontologies in 2012 • Identify key points of integration between ontologies • Modularize based on domain or taxon • Import and reuse rather than cross-referencing or “aligning” • Let the reasoner help do the work • Work together to distribute work Reproduced with permission, Jason Freeny http://web.mac.com/moistproduction/flash/index.html

42. Anatomical evidence: what is it, and why do we care about it?

43. What is evidence? ECO:000000X Imaging assay evidence Synaptolaemuscingulatus AMNH 91095 Phenotype (character) annotation: S. Cingulatus: mesethmoid narrow OBI:Specimen Drawing about anatomical entity OBI:Conclusion (textual entity) OBI:Image material_processing is_output is_output is_input Draw prepared specimen OBI:imaging assay Brian, 2008, maybe in Venezuela cleared and stained for cartilage and bone OBI: Interpreting Data- phenotypic assessment is_input OBI:processed specimen Sidlauskas and Vari, Zoological Journal of the Linnean Society, 2008, 154, 70–210

44. Anatomical evidence is cumulative and synergistic ECO:0000080 phylogenetic evidence Synaptolaemuscingulatus AMNH 91095 mesethmoid narrow ECO:0000071 morphological similarity evidence Caenotropusmaculosus USNM 231545 mesethmoid narrow . is_input is_output Schizodonfasciatus INPA 21606 mesethmoid wide . phylogeny Brian, 2008 . OBI:Conclusion . Phylogeny construction using PAUP* 4.0 Beta 10 OBI: Interpreting Data

45. The means to the end matters ECO:0000080 phylogenetic evidence Synaptolaemuscingulatus AMNH 91095 Mesethmoid ECO:0000071 sequence similarity evidence Caenotropusmaculosus USNM 231545 mesethmoid narrow . is_input is_output Schizodonfasciatus INPA 21606 mesethmoid wide . phylogeny Brian, 2008 . OBI:Conclusion . Phylogeny construction using PAUP* 4.0 Beta 10 OBI: Interpreting Data

46. So what should one do about evidence? • Keep in mind that as you record your phenotype data, the means by which you obtained it can matter later one • Others may want to use your data, and they too will care • You may find that how you know what you know depends on the means to the end • You can work with ECO and OBI to get the terms you need for your work

47. Acknowledgments • Jonathan Bard • Marcus Chibucos • WasilaDahdul • Paula Mabee • Chris Mungall • David Osumi-Sutherland • Alan Ruttenberg • Erik Segerdell • Carlo Torniai • Matt Yoder • JieZheng • AND numerous others Larson, October 1987