An Introduction to Ontology for Evolutionary Biology Barry Smith

Who am I? NCBO: National Center for Biomedical Ontology (NIH Roadmap Center) Stanford Medical Informatics University of San Francisco Medical Center The Mayo Clinic University at Buffalo (PI of Dissemination and Ontology Best Practices)

NCBO will offer Technology for uploading, browsing, and using biomedical ontologies Methods to make the online “publication” of ontologies more like that of journal articles Tools to enable the biomedical community to put ontologies to work on a daily basis

http://bioportal.bioontology.org

Hierarchy-to-root view

Who am I? Co-PI Protein Ontology Advisory Boards of Ontology for Biomedical Investigations Cleveland Clinic Semantic Database in Cardiothoracic Surgery Gene Ontology Scientific Advisory Board Advancing Clinico-Genomic Trials on Cancer (ACGT)

W-LOV World’s Longest Ontology Video Introduction to Biomedical Ontologies This 8-lecture course provides a basic introduction to ontology, with special reference to applications in the field of biomedical research. It is designed to be of interest to both philosophers and those with a background in the life sciences. 1. What is an ontology and what is it useful for? 2. Basic Formal Ontology: An upper-level ontology for scientific research 3. Open Biomedical Ontologies (OBO) and the Web Ontology Language (OWL) 4. The OBO Relation Ontology 5. An ontological introduction to biomedicine: Defining organism, function and disease 6. The Gene Ontology (GO), the Foundational Model of Anatomy (FMA) and the Infectious Disease Ontology (IDO) 7. The OBO Foundry: A suite of biomedical ontologies to support reasoning and data integration 8. Further applications http://ontology.buffalo.edu/smith/Ontology_Course.html

How to do biology across the genome? MKVSDRRKFEKANFDEFESALNNKNDLVHCPSITLFESIPTEVRSFYEDEKSGLIKVVKFRTGAMDRKRSFEKVVISVMVGKNVKKFLTFVEDEPDFQGGPISKYLIPKKINLMVYTLFQVHTLKFNRKDYDTLSLFYLNRGYYNELSFRVLERCHEIASARPNDSSTMRTFTDFVSGAPIVRSLQKSTIRKYGYNLAPYMFLLLHVDELSIFSAYQASLPGEKKVDTERLKRDLCPRKPIEIKYFSQICNDMMNKKDRLGDILHIILRACALNFGAGPRGGAGDEEDRSITNEEPIIPSVDEHGLKVCKLRSPNTPRRLRKTLDAVKALLVSSCACTARDLDIFDDNNGVAMWKWIKILYHEVAQETTLKDSYRITLVPSSDGISLLAFAGPQRNVYVDDTTRRIQLYTDYNKNGSSEPRLKTLDGLTSDYVFYFVTVLRQMQICALGNSYDAFNHDPWMDVVGFEDPNQVTNRDISRIVLYSYMFLNTAKGCLVEYATFRQYMRELPKNAPQKLNFREMRQGLIALGRHCVGSRFETDLYESATSELMANHSVQTGRNIYGVDFSLTSVSGTTATLLQERASERWIQWLGLESDYHCSFSSTRNAEDV How to do biology across the genome? http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&id=116006492 sequence of X chromosome in baker’s yeast

MKVSDRRKFEKANFDEFESALNNKNDLVHCPSITLFESIPTEVRSFYEDEKSGLIKVVKFRTGAMDRKRSFEKVVISVMVGKNVKKFLTFVEDEPDFQGGPIPSKYLIPKKINLMVYTLFQVHTLKFNRKDYDTLSLFYLNRGYYNELSFRVLERCHEIASARPNDSSTMRTFTDFVSGAPIVRSLQKSTIRKYGYNLAPYMFLLLHVDELSIFSAYQASLPGEKKVDTERLKRDLCPRKPIEIKYFSQICNDMMNKKDRLGDILHIILRACALNFGAGPRGGAGDEEDRSITNEEPIIPSVDEHGLKVCKLRSPNTPRRLRKTLDAVKALLVSSCACTARDLDIFDDNNGVAMWKWIKILYHEVAQETTLKDSYRITLVPSSDGISLLAFAGPQRNVYVDDTTRRIQLYTDYNKNGSSEPRLKTLDGLTSDYVFYFVTVLRQMQICALGNSYDAFNHDPWMDVVGFEDPNQVTNRDISRIVLYSYMFLNTAKGCLVEYATFRQYMRELPKNAPQKLNFREMRQGLIALGRHCVGSRFETDLYESATSELMANHSVQTGRNIYGVDSFSLTSVSGTTATLLQERASERWIQWLGLESDYHCSFSSTRNAEDVVAGEAASSNHHQKISRVTRKRPREPKSTNDILVAGQKLFGSSFEFRDLHQLRLCYEIYMADTPSVAVQAPPGYGKTELFHLPLIALASKGDVEYVSFLFVPYTVLLANCMIRLGRRGCLNVAPVRNFIEEGYDGVTDLYVGIYDDLASTNFTDRIAAWENIVECTFRTNNVKLGYLIVDEFHNFETEVYRQSQFGGITNLDFDAFEKAIFLSGTAPEAVADAALQRIGLTGLAKKSMDINELKRSEDLSRGLSSYPTRMFNLIKEKSEVPLGHVHKIRKKVESQPEEALKLLLALFESEPESKAIVVASTTNEVEELACSWRKYFRVVWIHGKLGAAEKVSRTKEFVTDGSMQVLIGTKLVTEGIDIKQLMMVIMLDNRLNIIELIQGVGRLRDGGLCYLLSRKNSWAARNRKGELPPKEGCITEQVREFYGLESKKGKKGQHVGCCGSRTDLSADTVELIERMDRLAEKQATASMSIVALPSSFQESNSSDRYRKYCSSDEDSNTCIHGSANASTNASTNAITTASTNVRTNATTNASTNATTNASTNASTNATTNASTNATTNSSTNATTTASTNVRTSATTTASINVRTSATTTESTNSSTNATTTESTNSSTNATTTESTNSNTSATTTASINVRTSATTTESTNSSTSATTTASINVRTSATTTKSINSSTNATTTESTNSNTNATTTESTNSSTNATTTESTNSSTNATTTESTNSNTSAATTESTNSNTSATTTESTNASAKEDANKDGNAEDNRFHPVTDINKESYKRKGSQMVLLERKKLKAQFPNTSENMNVLQFLGFRSDEIKHLFLYGIDIYFCPEGVFTQYGLCKGCQKMFELCVCWAGQKVSYRRIAWEALAVERMLRNDEEYKEYLEDIEPYHGDPVGYLKYFSVKRREIYSQIQRNYAWYLAITRRRETISVLDSTRGKQGSQVFRMSGRQIKELYFKVWSNLRESKTEVLQYFLNWDEKKCQEEWEAKDDTVVVEALEKGGVFQRLRSMTSAGLQGPQYVKLQFSRHHRQLRSRYELSLGMHLRDQIALGVTPSKVPHWTAFLSMLIGLFYNKTFRQKLEYLLEQISEVWLLPHWLDLANVEVLAADDTRVPLYMLMVAVHKELDSDDVPDGRFDILLCRDSSREVGE http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db=nuccore&id=116006492

The GO idea: through annotation of data what cellular component? what molecular function? what biological process? dir.niehs.nih.gov/ microarray/datamining/

three types of data what cellular component? what molecular function? what biological process? dir.niehs.nih.gov/ microarray/datamining/

The GO Idea sphingolipid transporter activity GlyProt MouseEcotope DiabetInGene GluChem

The GO Idea Holliday junction helicase complex GlyProt MouseEcotope DiabetInGene GluChem

The GO Idea sphingolipid transporter activity GlyProt MouseEcotope DiabetInGene GluChem

Benefits of GO rooted in experimental biology links people to data and to literature links data to data (comparability) across species (human, mouse, yeast, fly ...) across granularities (molecule, cell, organ, organism, population) links medicine to biological science serves cumulation of scientific knowledge in algorithmically tractable form

How to extend the GO methodology to other areas of the life sciences? OBO (Open Biomedical Ontologies) created 2001 in Ashburner and Lewis a shared portal for (so far) 60 ontologies http://obo.sourceforge.net with a common OBO flatfile format 16

In 2004 reform efforts initiated linking GO to other ontologies and data sources via formal relations GO + id: CL:0000062 name: osteoblast def: "A bone-forming cell which secretes an extracellular matrix. Hydroxyapatite crystals are then deposited into the matrix to form bone." is_a: CL:0000055 relationship: develops_from CL:0000008 relationship: develops_from CL:0000375 Cell type = Osteoblast differentiation: Processes whereby an osteoprogenitor cell or a cranial neural crest cell acquires the specialized features of an osteoblast, a bone-forming cell which secretes extracellular matrix. New Definition

OBO Foundry http://obofoundry.org TO TIME GRANULARITY CONTINUANT RELATION TO TIME GRANULARITY CONTINUANT OCCURRENT INDEPENDENT DEPENDENT ORGAN AND ORGANISM Organism (NCBI Taxonomy) Anatomical Entity (FMA, CARO) Organ Function (FMP, CPRO) Phenotypic Quality (PaTO) Biological Process (GO) CELL AND CELLULAR COMPONENT Cell (CL) Cellular Component (FMA, GO) Cellular Function MOLECULE Molecule (ChEBI, SO, RnaO, PrO) Molecular Function Molecular Process OBO Foundry http://obofoundry.org

Ontology Scope URL Custodians Cell Ontology (CL) cell types from prokaryotes to mammals obo.sourceforge.net/cgi- bin/detail.cgi?cell Jonathan Bard, Michael Ashburner, Oliver Hofman Chemical Entities of Bio- logical Interest (ChEBI) molecular entities ebi.ac.uk/chebi Paula Dematos, Rafael Alcantara Common Anatomy Refer- ence Ontology (CARO) anatomical structures in human and model organisms (under development) Melissa Haendel, Terry Hayamizu, Cornelius Rosse, David Sutherland, Foundational Model of Anatomy (FMA) structure of the human body fma.biostr.washington. edu JLV Mejino Jr., Cornelius Rosse Functional Genomics Investigation Ontology (FuGO) design, protocol, data instrumentation, and analysis fugo.sf.net FuGO Working Group Gene Ontology (GO) cellular components, molecular functions, biological processes www.geneontology.org Gene Ontology Consortium Phenotypic Quality (PaTO) qualities of anatomical structures obo.sourceforge.net/cgi -bin/ detail.cgi? attribute_and_value Michael Ashburner, Suzanna Lewis, Georgios Gkoutos Protein Ontology (PrO) protein types and modifications Protein Ontology Consortium Relation Ontology (RO) relations obo.sf.net/relationship Barry Smith, Chris Mungall RNA Ontology (RnaO) three-dimensional RNA RNA Ontology Consortium Sequence Ontology (SO) properties and features of nucleic sequences song.sf.net Karen Eilbeck

Goal: create the ontology resources for evolutionary biology

The ontologies in the OBO Foundry are scientific ontologies

Administrative/database ontologies Highly task-dependent – reusability and compatibility not (always) important Entities may be brought into existence by the ontology itself (convention ...) If there is no field for gender in our database, then persons do not have gender Can be secret, local, temporary Are comparable to software artifacts

Scientific ontologies are comparable to scientific theories must be open, based on consensus must be compatible with neighboring scientific ontologies and with results of scientifc research must be stable, evolve gracefully in tandem with the advance of knowledge must be evidence-based (testable)

Foundry ontologies are scientific ontologies Every representational unit in the ontology must be such that the developers believe it to refer to some entity on the basis of the best current scientific evidence  Important role of instances that we can observe in the laboratory

Ontologies are like science texts – they are representations of what is general in reality aka universals, kinds, types, categories, species, genera, ... aka universals, kinds, types, categories, species, genera, ...

universal vs. instance A central distinction (catalog vs. inventory) (science text vs. diary) (human being vs. Arnold Schwarzenegger)

For scientific ontologies it is generalizations (universals) that are important For databases it is (normally) instances that are important = particulars in reality: mouse #0000000001 tail #000000004 video image #23300014, etc.

Ontologies are representations of what is general in reality aka universals, kinds, types, categories, species, genera, ... aka universals, kinds, types, categories, species, genera, ... instances in reality are linked to universals via the instance_of relation

The distinction between universals and instances allows us to provide clear formal definitions of the relations which connect ontology terms A is_a B =def. A is narrower in meaning than B cancer documentation is_a cancer

The distinction between universals and instances allows us to provide clear logical definitions of the relations which connect ontology terms A is_a B =def. every instance of A is an instance of B

part_of A part_of B =def. every instance of A is an instance-level part of some instance of B Mary’s heart instance-level part of Mary cell nucleus part_of cell

is_a part_of Serous Sac Pleural Sac FMA Foundational Model of Anatomy Anatomical Space Anatomical Structure Organ Cavity Subdivision Organ Cavity Organ Organ Part Organ Subdivision Serous Sac Cavity Subdivision Serous Sac Cavity Serous Sac Organ Component Tissue is_a Pleural Sac Pleura(Wall of Sac) Pleural Cavity Parietal Pleura part_of Visceral Pleura Interlobar recess Mediastinal Pleura Mesothelium of Pleura FMA Foundational Model of Anatomy

Kinds of relations <universal, universal>: is_a, part_of, ... <instance, universal>: this cell instance_of the universal cell <instance, instance>: Mary’s heart part_of Mary

Foundry principle for definitions Definitions should be of the following form an A =def. a B which Cs where B is the is_a parent of A and C is some differentia Definitions are rooted in the is_a hierarchy

OBO Relation Ontology 1.0 Foundational Spatial Temporal Participation is_a part_of Spatial located_in contained_in adjacent_to Temporal transformation_of derives_from preceded_by Participation has_participant has_agent “Relations in Biomedical Ontologies”, Genome Biology, April 2005

derives_from time instances ovum zygote derives_from sperm C1 C c1 at t1 C c at t instances time C' c' at t ovum zygote derives_from sperm

pre-RNA  mature RNA child  adult pupa  larva transformation_of same instance c at t1 C c at t C1 time pre-RNA  mature RNA child  adult pupa  larva

transformation_of C2 transformation_of C1 =def. any instance of C2 was at some earlier time an instance of C1 fetus transformation_of embryo larva transformation_of pupa adult transformation_of child

embryological development c at t c at t1 C1 embryological development

fusion two continuants fuse to form a new continuant C1 C c1 at t1

one initial continuant is replaced by two successor continuants c1 at t1 C c at t C2 c2 at t1 fission

one continuant detaches itself from an initial continuant, which itself continues to exist c at t c at t1 C1 c1 at t budding

capture one continuant is absorbed by a second continuant C1 C c1 at t1 C c at t C' c' at t capture

New ‘regulates' relations in GO def: "A relation between a process and a process. A regulates B if the unfolding of A affects the frequency, rate or extent of B. A is called the regulating process, B the regulated process“ A regulates B =def. A is a process type and B is  a process type and every instance of A is such that its unfolding affects the frequency, rate or extent of some instance of B.

Relations proposed for RO 2.0 inheres_in has_input has_function has_quality realization_of directly_descends_from descends_from (CARO) homologous_to (CARO)

An ontology is a representation of universals We learn about universals in reality from looking at the results of scientific experiments as expressed in the form of scientific theories – which describe, not what is particular in reality, but what is general

A photographic image is a representation of an instance http://www.fi.edu/learn/heart/vessels/images/large_xray-of-kidneys-and-aorta.jpg

A photographic image is a representation of an instance We learn about instances in reality by performing scientific experiments on the basis of scientific hypotheses and describing the results in general terms provided (ideally) by ontologies http://www.fi.edu/learn/heart/vessels/images/large_xray-of-kidneys-and-aorta.jpg

Mature OBO Foundry ontologies Cell Ontology (CL) Foundational Model of Anatomy (FMA) Gene Ontology (GO) Phenotypic Quality Ontology (PATO) Relation Ontology (RO) Sequence Ontology (SO)

Foundry ontologies being built ab initio Common Anatomy Reference Ontology (CARO) – and various organism specific anatomy ontologies Ontology for Biomedical Investigations (OBI) Protein Ontology (PRO) RNA Ontology (RnaO) Subcellular Anatomy Ontology (SAO)

Ontologies in planning phase Environment Ontology (EnvO) Infectious Disease Ontology (IDO) Biobank/Biorepository Ontology Food Ontology Allergy Ontology Vaccine Ontology Still needed: Organism Taxonomy

continuants vs. occurrents independent vs. dependent entities RELATION TO TIME GRANULARITY CONTINUANT OCCURRENT INDEPENDENT DEPENDENT ORGAN AND ORGANISM Organism (NCBI Taxonomy) Anatomical Entity (FMA, CARO) Organ Function (FMP, CPRO) Phenotypic Quality (PaTO) Biological Process (GO) CELL AND CELLULAR COMPONENT Cell (CL) Cellular Component (FMA, GO) Cellular Function MOLECULE Molecule (ChEBI, SO, RnaO, PrO) Molecular Function Molecular Process continuants vs. occurrents independent vs. dependent entities

Organism-Level Process CONTINUANT OCCURRENT INDEPENDENT DEPENDENT ORGAN AND ORGANISM Organism (NCBI Taxonomy) Anatomical Entity (FMA, CARO) Organ Function (FMP, CPRO) Phenotypic Quality (PaTO) Organism-Level Process (GO) CELL AND CELLULAR COMPONENT Cell (CL) Cellular Component (FMA, GO) Cellular Function Cellular Process MOLECULE Molecule (ChEBI, SO, RNAO, PRO) Molecular Function Molecular Process RELATION TO TIME GRANULARITY rationale of OBO Foundry coverage (homesteading principle)

Basic Formal Ontology (BFO) Continuant Occurrent (Process) Independent Continuant (molecule, cell, organ, organism) Dependent Continuant (quality, function, disease) Functioning Side-Effect, Stochastic Process, ... ..... ..... .... .....

..... ..... .... ..... Gene Ontology Continuant Occurrent (Process) Independent Continuant Cellular Component Dependent Continuant Molecular Function Biological Process ..... ..... .... .....

PATO Phenotype Ontology Continuant Occurrent (Process) Independent Continuant (molecule, cell, organ, organism) PATO phenotypic quality ontology Functioning Side-Effect, Stochastic Process, ... ..... ..... .... .....

An example of a PATO quality The particular redness of the left eye of a single individual fly An instance of a quality universal The color ‘red’ A quality universal Note: the eye does not instantiate ‘red’ PATO represents quality universals: color, temperature, texture, shape …

Qualities are dependent entities Qualities require (depend on) bearers, which are independent continuants Example: A shape requires a physical object as its bearer If the physical object ceases to exist (e.g. it decomposes), then the shape ceases to exist

the particular case of redness (of a particular fly eye) the universal red instance_of an instance of an eye (in a particular fly) the universal eye has_bearer

What a quality is NOT Qualities are not measurements Instances of qualities exist independently of their measurements Qualities can have zero or more measurements These are not the names of qualities: percentage process abnormal high Open problem: how relate qualities such as length to measurement values?

How to do anatomy ontology Functional: cardiovascular system, nervous system Spatial: head, trunk, limb Developmental: endoderm, germ ring, lens placode Structural: tissue, organ, cell Stage: developmental staging series

CARO – Common Anatomy Reference Ontology for the first time provides guidelines for model organism researchers who wish to achieve comparability of annotations based on anatomy and development

RELATION TO TIME GRANULARITY CONTINUANT OCCURRENT INDEPENDENT DEPENDENT ORGAN AND ORGANISM Organism Placeholder: NCBI Taxonomy Anatomical Entity (FMA, CARO) Organ Function (FMP, CPRO) Phenotypic Quality (PaTO) Biological Process (GO) CELL AND CELLULAR COMPONENT Cell (CL) Cellular Component (FMA, GO) Cellular Function MOLECULE Molecule (ChEBI, SO, RnaO, PrO) Molecular Function Molecular Process CARO will work well only when linked via cross-products to an organism (species) ontology

Single backbone is_a hierarchy Cow is_a Chordate is_a Vertebrate is_a Mammal is_a Animal is_a Organism

How should an ontology of species be constructed? cow is_a organism species cow is_a population of interbreeding organisms (Mayr, biological theory of species)

Alternatively: species cow instance_of population of interbreeding organisms (Mayr, biological theory of species as modified by Ghiselin)

All OBO Foundry ontologies work in the same way we have data (biosample, haplotype, clinical data, survey data, ...) we need to make this data available for semantic search and algorithmic processing we create a consensus-based ontology for annotating the data we use cross-products to compose more complex terms and assertions

The Environment Ontology

The Hole Story http://www.pnas.org/misc/archive011904.html

http://www.sacsplash.org/cimages/Solitarybee.jpg

Double Hole Structure of the Occupied Niche http://ontology.buffalo.edu/bio/niche-smith.htm

Tenant, medium and retainer the medium of the bear’s niche is a circumscribed body of air medium might be body of water, cytosol, nasal mucosa, epithelium, endocardium, synovial tissue ...

The Empty Niche

Four Basic Niche Types (Niche as generalized hole) 1: a womb; an egg; a house (better: the interior thereof) 2: a snail’s shell; 3: the niche of a pasturing cow; 4: the niche around a circling buzzard (fiat boundary)

Elton – niche as role the ‘niche’ of an animal means its place in the biotic environment, its relations to food and enemies. [...] When an ecologist says ‘there goes a badger’ he should include in his thoughts some definite idea of the animal’s place in the community to which it belongs, just as if he had said ‘there goes the vicar’ (Elton 1927, pp. 63f.)

G.E. Hutchinson: niche as volume in a functionally defined space the niche = an n-dimensional hyper-volume whose dimensions correspond to resource gradients over which species are distributed http://www.geobabble.org/~hnw/esri99/

G.E. Hutchinson (1957, 1965) http://www.geobabble.org/~hnw/esri99/

http://www.stankievech.net/projectsFrame.html

Hypervolume niche = a location in an attribute space defined by a specific constellation of environmental variables such as degree of slope, exposure to sunlight, soil fertility, foliage density, salinity...

The Environment Ontology Genomic Standards Consortium National Environment Research Council (UK) Barcode of Life Project Encyclopedia of Life Project

EnvO macroscopic (geographical) mesoscopic (behavioral) combines the spatial and Hutchinsonian perspectives to create a consensus controlled vocabulary for representing macroscopic (geographical) mesoscopic (behavioral) microscopic (cellular, molecular …) environments

Applications of EnvO in biology

Environment = totality of circumstances external to a living organism or group of organisms pH evapotranspiration turbidity available light predominant vegetation predatory pressure nutrient limitation …

How EnvO currently works for information retrieval Retrieve all experiments on organisms obtained from: deep-sea thermal vents arctic ice cores rainforest canopy alpine melt zone Retrieve all data on organisms sampled from: hot and dry environments cold and wet environments a height above 5,000 meters Retrieve all the omic data from soil organisms subject to: moderate heavy metal contamination

Scale: From microbiological to geographic Data on locations of organisms/samples, sources of museum artifacts ... Environments have spatial locations Data on organism interactions, e.g. on bacterial infection – how the interior of one organism or organism part serves as environment for another organism

The Environment Ontology OBO Foundry Genomic Standards Consortium National Environment Research Council (UK) Barcode of Life Project Encyclopedia of Life Project

Family, Community, Deme, Population RELATION TO TIME GRANULARITY CONTINUANT OCCURRENT INDEPENDENT DEPENDENT COMPLEX OF ORGANISMS Family, Community, Deme, Population Organ Function (FMP, CPRO) Population Phenotype Population Process ORGAN AND ORGANISM Organism (NCBI Taxonomy) (FMA, CARO) Phenotypic Quality (PaTO) Biological Process (GO) CELL AND CELLULAR COMPONENT Cell (CL) Cell Com-ponent (FMA, GO) Cellular Function MOLECULE Molecule (ChEBI, SO, RnaO, PrO) Molecular Function Molecular Process E N V I R O N M E N T

Environment of population RELATION TO TIME GRANULARITY CONTINUANT INDEPENDENT COMPLEX OF ORGANISMS Family, Community, Deme, Population Environment of population ORGAN AND ORGANISM Organism (NCBI Taxonomy) (FMA, CARO) Environment of single organism CELL AND CELLULAR COMPONENT Cell (CL) Cell Com-ponent (FMA, GO) Environment of cell MOLECULE Molecule (ChEBI, SO, RnaO, PrO) Molecular environment E N V I R O N M E N T

RELATION TO TIME GRANULARITY CONTINUANT INDEPENDENT COMPLEX OF ORGANISMS Family, Community, Deme, Population Environment of population ORGAN AND ORGANISM Organism (NCBI Taxonomy) (FMA, CARO) Environment of single organism* CELL AND CELLULAR COMPONENT Cell (CL) Cell Com-ponent (FMA, GO) Environment of cell MOLECULE Molecule (ChEBI, SO, RnaO, PrO) Molecular environment E N V I R O N M E N T * The sum total of the conditions and elements that make up the surroundings and influence the development and actions of an individual. * http://books.nap.edu/openbook.php?record_id=10464&page=120

biome / biotope, territory, habitat, neighborhood, ... RELATION TO TIME GRANULARITY CONTINUANT INDEPENDENT COMPLEX OF ORGANISMS biome / biotope, territory, habitat, neighborhood, ... work environment, home environment; host/symbiont environment; ... ORGAN AND ORGANISM CELL AND CELLULAR COMPONENT extracellular matrix; chemokine gradient; ... MOLECULE hydrophobic surface; virus localized to cellular substructure; active site on protein; pharmacophore ... E N V I R O N M E N T

OBO Foundry principle of orthogonality designed to foster division of labor methodology for coordination designed to support cross-linkage between orthogonal ontologies

The methodology of cross-products compound terms in ontologies to be defined as cross-products of simpler terms: elevated blood glucose cross-product of PATO: increased concentration with FMA: blood and CheBI: glucose. factoring out of ontologies into discipline-specific modules (orthogonality)

The methodology of cross-products enforcing use of OBO’s Relation Ontology in linking terms drawn from Foundry ontologies creates a systematic approach to term-formation makes the results algorithmically processible in virtue of the logical definitions provided by the RO ensures that the ontologies in the Foundry are networked together

Questions for an Evolution Ontology Granularity: evolution of proteins, RNA, of organisms … (PRO-Evo, RNAO, …) Organism / niche (ENVO) Derivation / homology Evolution and development (CARO / RO) Co-evolution (single organism vs. multiple organism) (GO, IDO)

How to build an ontology work with scientists with data annotation needs to create an initial top-level classification find ~50 most commonly used terms corresponding to universals in reality; address links to other ontologies arrange these terms into an informal is_a hierarchy according to the universality principle A is_a B  every instance of A is an instance of B draw on the main BFO divisions and relations from RO, filling in missing terms needed to complete the hierarchy recruit domain scientists with data annotation needs to help populate the lower levels of the hierarchy and foster data integration

Principle of Low Hanging Fruit Include even absolutely trivial assertions (assertions you know to be universally true) cellular development process is_a cellular process cell death is_a death pneumococcal bacterium is_a bacterium Computers need to be led by the hand

which of these terms already exist in OBO Foundry ontologies? gene allele allelic variation gene pool genotype phenotype population trait speciation homology mutation inheritance organism extinction

compare: legends for maps ontologies are like legends for maps http://www.ags.gov.ab.ca/GRAPHICS/uranium/athabasca_group_map_with_legend.jpg

compare: legends for maps common legends allow (cross-border) integration compare: legends for maps http://www.ags.gov.ab.ca/GRAPHICS/uranium/athabasca_group_map_with_legend.jpg

common legends help human beings use and understand complex representations of reality help human beings create useful complex representations of reality help computers process complex representations of reality help glue data together

Why do we need rules/standards for good ontology? Ontologies must be intelligible both to humans (for annotation and curation) and to machines (for reasoning and error-checking): the lack of rules for classification leads to human error and blocks automatic reasoning and error-checking Intuitive rules facilitate training of curators and annotators Common rules allow alignment with other ontologies e.g. menopause part-of aging, aging part-of death Database searching is one of the important kinds of reasoning we want to enable