Ontologies for biological data

Maja Magel

Charlie Pauvert

2024-07-18

Learning objectives

• define an ontology and explain its features
• name at least two widely used ontologies in the life sciences
• navigate an ontology service and find specific ontology terms

Why bother with ontologies?

• increase findability of your dataset
• improve machine-readability of your datasets
• help others correctly categorize & re-use your datasets > recontextualization
• required by data repositories
• first step towards open linked data and knowledge graph representations?

Having fun with ontologies

Let’s talk about…

“pie”

Having fun with ontologies

• Were you thinking of the baked goods, such as cake [ENVO:02000063]?
• unfortunately there was no well described ontology term for pie
• Or were you thinking of the pie [44315] gene that encodes the pineapple eye protein (fruit fly) [PR:Q9VKW2]?

• add picture of a blueberry pie or blueberry cake?
• blueberry pie hyperlink to picture:
• blue berry pie picture attribution: Kelly Sue DeConnick - originally posted to Flickr as Blueberry Pie with Almond Crumb Topping licensed under Creative Commons Attribution-Share Alike 2.0 Generic

fruit fly picture attribution: Drosophila melanogaster feeding on banana by Sanjay Acharya licensed under Creative Commons Attribution-Share Alike 4.0 International

Blueberry Pie with Almond Crumb Topping by Kelly Sue DeConnick from Flickr under CC BY-SA version 2.0 Generic and Drosophila melanogaster feeding on banana by Sanjay Acharya under CC BY-SA 4.0 International

Ontology definition

• List of terms, usually taken from the scientific literature

• Ontology terms:

  • have curated textual definitions and synonyms

  • are arranged in a hierarchy from general to specific

  • have defined relationships with others terms (e.g., is_a, has_condition)

  • have persistent identifiers

  • can be cross-referenced with other resources (ontology or not)

• Ontology should reflect existing knowledge

Adapted from Osumi-Sutherland et al. (2023) and Leonelli (2016, 125, 133)

Exercise: an ontology?

Task: Choose one of the definitions below and, if necessary add any missing features.

An ontology is:

• A dictionary with persistent identifiers

• A network of scientific definitions

• A controlled vocabulary with synonyms

Formative assessment of ontology understanding

• A dictionary with persistent identifiers (missing hierarchy of terms, [relationships])

• A network of scientific definitions (missing curation and missing identifier)

• A controlled vocabulary with synonyms (missing definitions, persistent identifier and missing hierarchy)

Cross references as well

Search ontology terms

Search terms in ontologies

You can find terms in ontologies using the search bar of:

• BioPortal

• Ontology Lookup Service v4

• others: The OBO Foundry, OntoBee mainly for developers

Ontology Lookup Service

OLS is the official ontology service of the EMBL-EBI

https://www.ebi.ac.uk/ols4

• EMBL: European Molecular Biology Laboratory
• EMBL-EBI: EMBL’s European Bioinformatics Institute

Demonstration: navigating OLS for term “intestines” in UBERON, ENVO

• search intestines
• restrict search to ontologies “UBERON, ENVO”
• point out that first hit /= from requested ontology
• exact match > term not showing up
• correct spelling to “Intestine”
• explain ontology vs. present in and “imported” demonstration:
• point out: term identifier & copy function
• definition & synonyms
• term relations / class relations
• persistent identifier

Ontology definition as demonstrated with the term “intestine”

Results, we found the term

intestine [UBERON:0000160]

in both ontologies UBERON and ENVO (as imported term from UBERON)

The correct writing convention for ontology terms and their term identifiers is: > term [ontology-acronym:sequence-number], e.g. intestine [UBERON:0000160]

Exercise: Ontology browser

Task: using Ontology Lookup Service v4

• Look-up the following keywords in that order via the search bar: pond, ear and leaf

• Select a term for each using these ontologies:

  • Uber-anatomy ontology (UBERON)

  • Plant Ontology (PO)

  • Environmental Ontology (ENVO)

• Report the term and the term identifier in the pad

• pond [ENVO:00000033] (the correct term is not far down the list)

• ear [UBERON:0001690] (many “early” that can be filtered with the exact match option)

• leaf [PO:0025034] (tricky because it is an imported term)

La Grenouillère by Auguste Renoir (1869) Wikimedia

Exercise: visualize features

• Navigate to 1-2 of the following terms:

  • pond [ENVO:00000033]

  • ear [UBERON:0001690]

  • leaf [PO:0025034]

• Inspect the term page

• Associate parts of the OLS interface with the expected features of an ontology

• Let’s test your understanding of the features team vs. team

Restitution can be made with everyone closer to the screen displaying one of the terms and pointing to each of the ontology features. OR: Gamify the association features: the lecturer points to a feature on the page and the teams have to quickly press a “Buzzer/knock on the table” and then answer. 1 point to the correct team. continue until all features have been named:

• curated textual definitions
• synonyms
• hierarchy from general to specific
• relationships with others terms (e.g., is_a, has_condition)
• persistent identifiers
• cross-referenced with other resources (ontology or not)

What questions do you have?

Learning objectives achieved?

• define an ontology and explain its features
• navigate an ontology service and find specific ontology terms
• name at least two widely used ontologies in the life sciences

Exercise: Find your ontology terms & connect the dots

Can we start the next lesson on selecting ontology terms for YOUR dataset descriptions?

TASK: Pick any 3 terms from your dataset description.

• Note them in the pad.
• Browse the OLS and find 1-2 suitable ontology terms for each.
• Add the ontology term identifiers to the pad.
• You have 5 minutes.
• Share your experiences.
• Who used an ontology besides UBERON, ENVO and PO? And why?

Exercise: Find your ontology terms & connect the dots 2

Connect the dots to repository metadata fields

• Select a suitable ENA checklist for your dataset and match your terms to one of the metadata fields.
• Write down the checklist identifier & metadata field.
• Are your terms fitting the metadata field requirements (field format, restriction)?
• Does the info box tell you to use specific ontologies?
• 10 more minutes

Environmental metadata according to established metadata standards

MIxS lists three mandatory environmental metadata fields that expect ontology terms.

Metadata field	Abbreviation	Definition
broad-scale environmental context	env_broad_scale	global correlation; ecosystem
local environmental context	env_local_scale	in local vicinity; causal influences
environmental medium	env_medium	immediate surroundings of your sample during sampling

Metadata field	Abbreviation	Recommended use of subclasses from
broad-scale environmental context	env_broad_scale	biome [ENVO:00000428]
local environmental context	env_local_scale	deeper hierarchy than broad-scale (UBERON terms accepted)
environmental medium	env_medium	environmental material [ENVO:00010483]

Exercise: Env* metadata

Task alone or by pairs:

• Browse ENVO or UBERON (see previous table)

• List ontology terms fitting your data

• Fill out the following template on the pad:

  • env_broad_scale

  • env_local_scale

  • env_medium

• trouble finding an appropriate term downstream of the recommended class? See instructions of using other ontologies with the MIxS standard

• Depending on the time limit: Execute Deep dive task OR ask if anyone thinks this is an efficient way of collecting & adapting your metadata?
• Goal:

check the transition to the next lesson, include small exercise or table of stating 3 terms and identifying an ontology term (free-style) - allow to use any ontology -

Get more help on ENVO’s wiki page about microbial samples!

References

Leonelli, Sabina. 2016. Data-Centric Biology: A Philosophical Study. Chicago ; London: The University of Chicago Press.

Osumi-Sutherland, David, Nicole Vasilevsky, Alex Diehl, Nico Matentzoglu, Matt Brush, Matt Yoder, Carlo Toriniai, et al. 2023. “Introduction to Ontologies.” https://oboacademy.github.io/obook/explanation/intro-to-ontologies/#key-features-of-well-structured-ontologies.