FAIR data in Aspergillus research

How do we make data more useful?

Sibbe Bakker

sibbe.bakker@wur.nl

Mariana Santos Couto Silva

Anna Fensel

Why?

Aspergillus fumigatus–an opportunist fungal pathogen.

A. fumigatus is a growing health concern [1].
A lot of research is being conducted.
- Environmental spread.
- Molecular biology.
There are some un-answered questions in this domain.
- What mechanisms of azole resistance do environmental isolates have?
- How does this spread in through the A fumigatus population?

*A. fumigatus* spreads quickly through its environment [2]

How is this research shared?

Types of datasharing by [3].

How is the data actually shared?¹

What are these data usefull for?

A single study may be understood better using this.
Using the data from the same study for a different question?
Combining specific results?
Finding specific results?

Can we face the open questions in the field?

What do we need to make it useful?

FAIR principles were devised by [7].

DISCUSS DURING PRESENTATION

Findable:
- Data are indexed and searchable.
Accessable:
- Access will be for everyone.
Interoperable:
- Metadata is formally standardised.
Reusable:
- Metadata must be useful.
- Licence must be permissive.

DISCUSS AFTER PRESENTATION?

Findable

The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process.

F1. (Meta)data are assigned a globally unique and persistent identifier

F2. Data are described with rich metadata (defined by R1 below)

F3. Metadata clearly and explicitly include the identifier of the data they describe

F4. (Meta)data are registered or indexed in a searchable resource

Accessible

Once the user finds the required data, they need to know how they can be accessed, possibly including authentication and authorisation.

A1. (Meta)data are retrievable by their identifier using a standardised communications protocol

A1.1 The protocol is open, free, and universally implementable

A1.2 The protocol allows for an authentication and authorisation procedure, where necessary

A2. Metadata are accessible, even when the data are no longer available

Interoperable

The data usually need to be integrated with other data. In addition, the data need to interoperate with applications or workflows for analysis, storage, and processing.

I1. (Meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.

I2. (Meta)data use vocabularies that follow FAIR principles

I3. (Meta)data include qualified references to other (meta)data

Reusable

The ultimate goal of FAIR is to optimise the reuse of data. To achieve this, metadata and data should be well-described so that they can be replicated and/or combined in different settings.

R1. (Meta)data are richly described with a plurality of accurate and relevant attributes

R1.1. (Meta)data are released with a clear and accessible data usage license

R1.2. (Meta)data are associated with detailed provenance

R1.3. (Meta)data meet domain-relevant community standards

The principles refer to three types of entities: data (or any digital object), metadata (information about that digital object), and infrastructure. For instance, principle F4 defines that both metadata and data are registered or indexed in a searchable resource (the infrastructure component).

— GO FAIR, FAIR Principles, https://www.go-fair.org/fair-principles/

What is already available?

Disconnected databases.
Deposition of new data not easy.
No FAIR system.

What is there to do?

What can we do about this?

Come together as a field.
Make and adapt standards as a community.
Be vocal about your wishes.

What is my role in this?

Facilitate working together.
Educate about standards.
Introduce the standards that the community wants.

Solution?

The FAIRDS project [11]

A standardised meta data solution.
Excel templates.
RDF output.

Written by members of SSB.
Will be maintained after the ASPAR_KR project is done.

A quick demo for making FAIR data!

Experimental design is specified in the data format.

Classes available within the `ASPAR_KR` database. Each class `owns’ lower level classess. For example, a sample has associated assays.

Example of data analysis

Analysis in `R`.

sparql_query <- 
  ' # Select the first 5 of everything...
  prefix ppeo: <http://purl.org/ppeo/PPEO.owl#> 
  prefix jm: <http://jermontology.org/ontology/JERMOntology#> 
  prefix fair: <http://fairbydesign.nl/ontology/> 
  prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> 
  prefix schema: <http://schema.org/>
  SELECT ?observation_label ?sample_label 
    (COUNT(?sample_label) as ?n) ?total_cfu ?selection_cfu
  WHERE {
    # Get the samples of interest
    ?observation_unit a ppeo:observation_unit .
    ?observation_unit jm:hasPart ?parts .
    ?parts a jm:Sample .
    ?parts fair:packageName "TwoLayerCulture" .
    ?observation_unit schema:name ?observation_label .
    ?parts schema:name ?sample_label .
    
    # Experimental data
    ?parts fair:total_cfu ?total_cfu .
    ?parts fair:selection_cfu ?selection_cfu .
  } GROUP BY ?observation_unit
  '
result <- rdflib::rdf_query(rdf, sparql_query)
result

# A tibble: 2 × 5
  observation_label    sample_label                    n total_cfu selection_cfu
  <chr>                <chr>                       <dbl>     <dbl>         <dbl>
1 The city of Arnhem   Arnhem Centraal culture         4        66            28
2 The city of Nijmegen Nijmegen stationsplein cul…     4        57            21

result |> 
  dplyr::mutate(resistance_fraction = selection_cfu / total_cfu) |> 
  ggplot2::ggplot(ggplot2::aes(x = observation_label, y = resistance_fraction)) +
  ggplot2::geom_bar(stat="identity")

Linked data potential.

Linked data can be used to easily use information collected by others.

Lessons so far…

FAIR data is important for A. fumigatus research.

Without it, an overview of the field is hard to obtain.
Community awareness needs to increase.
Existing standards (FAIRDS) must be adapted.

Now lets hear what you think!

Where will you publish your data?

Questions?

If you need more information?

Check out the git page.

Thanks for

Jasper Koehorst and Bart Nijsen
Developing FAIRDS.
Mariana and Anna
Supervision.
Members of the genetics chair group
For the good discussions.

Additonal slides

Extra slides for extra questions

How does `RDF` work?

RDF files are plain text of various formats.
The basis is the triple.

@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
<https://example.com/data#statement> rdf:type "triple" .

This…

@base <http://example.org/> .
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix rel: <http://www.perceive.net/schemas/relationship/> .
<#green-goblin>
    rel:enemyOf <#spiderman> ;
    a foaf:Person ;    # in the context of the Marvel universe
    foaf:name "Green Goblin" .
<#spiderman>
    rel:enemyOf <#green-goblin> ;
    a foaf:Person ;
    foaf:name "Spiderman", "Человек-паук"@ru .

Turns into this …

How does `SPARQL` work?

SPARQL is a programming language for analysis of RDF.

@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix rel: <http://www.perceive.net/schemas/relationship/> .
PREFIX ex: <http//example.org/>
SELECT ?person ?enemy
WHERE
  {
    
    ?person a foaf:Person ;
      foaf:name ?name ;
      rel:enemyOf ?enemy .
    FILTER(STRSTARTS(?name, "Green")) .
  }

should return: “Green goblin” ex:spiderman

Examples of `ASPAR_KR` alternatives

alternative	+	-
seek4science	* Supports ISA * Sharing of templates online.	* A bit more complicated to contribute to. * Not available locally via excel sheets.
FAIRshare	* Locally available	* Limited in scope to genomics or immunology. * Not clear how to expand it.

References

Parums DV (2022) Editorial: The World Health Organization (WHO) Fungal Priority Pathogens List in Response to Emerging Fungal Pathogens During the COVID-19 Pandemic. Med Sci Monit 28:e939088-1-e939088-3. https://doi.org/10.12659/MSM.939088

Keller N (2017) Heterogeneity Confounds Establishment of “a” Model Microbial Strain. mBio 8:e00135–17. https://doi.org/10.1128/mBio.00135-17

Spinster (2012) English: Tim Berners-Lee, the inventor of the Web and Linked Data initiator, suggested a 5-star deployment scheme for Open Data. This scheme contains the five steps that this entails.

Gonçalves P, Melo A, Dias M, et al (2021) Azole-Resistant Aspergillus fumigatus Harboring the TR34/L98H Mutation: First Report in Portugal in Environmental Samples. Microorganisms 9(1, 1):57. https://doi.org/10.3390/microorganisms9010057

Burks C, Darby A, Londoño LG, Momany M, Brewer MT (2021) Azole-resistant Aspergillus Fumigatus in the environment: Identifying key reservoirs and hotspots of antifungal resistance. PLOS Pathogens 17(7):e1009711. https://doi.org/10.1371/journal.ppat.1009711

Cao D, Wang F, Yu S, et al (2021) Prevalence of Azole-Resistant Aspergillus fumigatus is Highly Associated with Azole Fungicide Residues in the Fields. Environ Sci Technol 55(5):3041–3049. https://doi.org/10.1021/acs.est.0c03958

Wilkinson MD, Dumontier M, Aalbersberg IjJ, et al (2016) The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3(1, 1):160018. https://doi.org/10.1038/sdata.2016.18

Barrett T, Clark K, Gevorgyan R, et al (2012) BioProject and BioSample databases at NCBI: Facilitating capture and organization of metadata. Nucleic Acids Research 40(D1):D57–D63. https://doi.org/10.1093/nar/gkr1163

Sewell TR, Zhu J, Rhodes J, et al (2019) Nonrandom Distribution of Azole Resistance across the Global Population of Aspergillus fumigatus. mBio 10(3):e00392–19. https://doi.org/10.1128/mBio.00392-19

10.

Weber M, Schaer J, Walther G, et al (2018) FunResDB—A web resource for genotypic susceptibility testing of Aspergillus fumigatus. Med Mycol 56(1):117–120. https://doi.org/10.1093/mmy/myx015

11.

Nijsse B, Schaap PJ, Koehorst JJ (2023) FAIR data station for lightweight metadata management and validation of omics studies. GigaScience 12:giad014. https://doi.org/10.1093/gigascience/giad014

Footnotes

Statements from [4], [5], [6]