Presentation is loading. Please wait.

Presentation is loading. Please wait.

An Introduction to Anatomy Ontologies Phenotype RCN Feb 23, 2012 Melissa Haendel.

Similar presentations

Presentation on theme: "An Introduction to Anatomy Ontologies Phenotype RCN Feb 23, 2012 Melissa Haendel."— Presentation transcript:

1 An Introduction to Anatomy Ontologies Phenotype RCN Feb 23, 2012 Melissa Haendel

2 Setting the stage 1.Who are we? What do we need? Why are we here? 2.What is an anatomy ontology? 3.What kinds of anatomy ontologies exist? 4.How are anatomy ontologies used? 5.Anatomical evidence

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

4 Therefore, we build ontologies that are intelligible to: Domain experts Machines  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 We want to enable: Ontologists Engineers

5 OMIM Query# Records “large bone”785 “enlarged bone”156 “big bone”16 “huge bones”4 “massive bones”28 “hyperplastic bones”12 “hyperplastic bone”40 “bone hyperplasia”134 “increased bone growth”612 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_of rhombomere 10 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 fore wing fore wing hind wing

9 Relationships record classifications too leg part_of some ‘thoracic segment wing ‘leg’ SubClassOf part_of some thoracic segment

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

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 sense organ capable_of some detection of smell olfactory sense organ

12 nose sense organ nose capable_of some detection of smell Classifying sense organ capable_of some detection of smell olfactory sense organ nose

13 Compositionality and avoiding asserted multiple inheritance 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. Let the reasoner do the work!

14 Example of a post-composed anatomical entity Plasma membrane of spermatocyte Plasma membrane [GO CC] Spermatocyte [Cell Ontology] a plasma membrane which is part_of a spermatocyte Gene OntologyBasic Formal Ontology Cell Ontology GenusDifferentia

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

16 What kinds of anatomy ontologies exist? 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 Species-centric and multi-species ontologies Species neutral ontologies 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 ontologies The 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

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

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

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

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

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

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

25 Representing different levels of granularity lateral line development ? ? cilium part_of hair cell part_of neuromast hair cell part_of neuromast neuromast part_of lateral line GO cilium development hair cell development neuromast development

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

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

28 There are issues with mappings Class AClass BIn Bioportal?Useful? FMA extensor retinaculum of wrist MA retinaYesNo FMA portion of bloodMA bloodNoYes ZFA MaculaMA maculaYesNo ZFA aortic archMA arch of aortaYesDubious ZFA hypophysisMA pitiuitaryNoYes FMA tibiaFBbt tibiaYesNo FMA colonGAZ Colón, PanamaYesNo PATO maleChebi maleate 2(-)YesNo

29 Zebrafish terms are is_a subtypes of teleost terms is_a Zebrafish Anatomy Teleost Anatomy Ontology Reconciliation and linking between TAO and ZFA 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 One can import a whole ontology or just portions of another ontology MIREOT: Minimum information to reference an external ontology term CARO VAO Present TAOModularized ontology

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

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

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

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

37 Not all classification is useful Be practical: Build ontologies for what you need and for what can be reused 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

38 Ontologies can help reconcile annotation inconsistencies

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

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

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

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

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

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

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

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  Wasila Dahdul  Paula Mabee  Chris Mungall  David Osumi-Sutherland  Alan Ruttenberg  Erik Segerdell  Carlo Torniai  Matt Yoder  Jie Zheng  AND numerous others Larson, October 1987

Download ppt "An Introduction to Anatomy Ontologies Phenotype RCN Feb 23, 2012 Melissa Haendel."

Similar presentations

Ads by Google