- Plant stress traits are important breeding targets for all crop species. Massive amounts of research dollars are spent generating data to combat plant diseases and environmental stress. Often this data is used to achieve a single goal, and then left in a repository to never be used again. As a scientific community, we should be striving to make all publicly funded data reusable, and interoperable. This goal is achievable only through careful annotation using universal data and metadata standards. One such standard is the use of a standardized vocabulary, or ontology. This paper presents a semi-automated method to define and label plant stresses using a combination of web scraping and ontology design patterns. Standardizing the definitions and linking plant stress with established hierarchies leverages previous work of developed knowledge bases such as taxonomic classifications and other ontologies.
Global climate change and international travel has introduced more and more diseases to previously unaffected regions. The varieties of crops grown in these regions are typically very susceptible, and yield losses are massive. Spraying pesticides is costly, and damaging to the environment. It takes too long to identify, and integrate resistance genes into existing elite varieties using traditional breeding methods.
Many diseases already have a substantial amount of research and data available related to resistance genes, pathways, and quantitative trait loci (QTLs). However, this data is not easily accessible and even when it is, it can often be difficult to interpret.
By standardizing the naming of plant diseases, their host and
pathogen from an ordered taxonomy (e.g. NCBI Taxonomy [
The hierarchy of the Ontology Of Plant Stress (OOPS)
separates plant stress into two general subclasses: biotic stress,
and abiotic stress classes (Fig 1.) The abiotic stress class has two
subclasses: plant stress caused by an excess or deficiency of
some element. The biotic stress class has two children terms,
herbivory stress and plant disease. These upper level hierarchy
terms are manually curated, and can be adjusted, or added to if
the need arises. Initial abiotic stress terms were populated using
existing abiotic stress traits found in the Plant Trait Ontology
(TO [
A top level view of the Ontology of Plant Stress (OOPS). All classes fall under the parent class plant stress. The two child terms under the top level divide plant stress processes into either biotic stress or abiotic stress. Classes highlighted in blue represent classes in which there is no specificity to the host plant experiencing the stress process. Classes highlighted in yellow indicate stresses in which a specific interaction is occurring between the host plant and the stressor. Example stress classes from table 1 and 2 are displayed in grey.
In order to increase automation in development of the
Ontology of Plant Stress, we are using a set of design patterns
that describe different plant stresses compliant with the Dead
Simple OWL Design Patterns (DOS-DPs) format [
Plants can experience stress from exposure to a multitude of
different chemical elements, and the process of experiencing
stress is dependent on the concentration of said element for a
given species or variety of plant in contrast to a reference
entity. Abiotic stresses are divided into subclasses based on the
excess and deficient states of the stressor element. Stresses
caused by exposure to an experimental condition containing too
much of an element fall under the “excess” pattern, whereas
stresses caused by exposure to an experimental condition that is
deficient/lacking a particular element are said to be
“deficient”. The pattern returns an ontology term with the
axioms in Manchester syntax [
"'abiotic plant stress' and 'causally downstream of' some ('plant treatment' and 'has exposure stimulus' some (ELEMENT and 'has quality' some 'increased amount')) and 'occurs in' some PLANT STRUCTURE"
"'abiotic plant stress' and 'causally downstream of' some ('plant treatment' and 'has exposure stimulus' some (ELEMENT and 'has quality' some 'decreased amount')) and 'occurs in' some PLANT STRUCTURE"
In the above axioms, the ‘ELEMENT’ is defined by some
entity which is the agent responsible for the stress. This element
can be anything, but is typically some chemical entity, defined
using Chemical Entities of Biological Interest (ChEBI [
The Biotic stress class has two subclasses: herbivory, and plant disease. The Herbivory stress pattern is under development, and the plant disease stress pattern results in the following axiom.
Disease pattern: "'plant disease process' and ('has participant' some HOST) and 'causally downstream of' some ('plant treatment' and 'has exposure stimulus' some PATHOGEN) and 'occurs in' some PLANT STRUCTURE"
Defining diseases as processes allows the annotation of stage-specific disease symptoms as infection occurs. Plant diseases are defined by three object classes: host, pathogen, and the plant structure where infection occurs. This pattern defines a host as some participant in the process, whereas the pathogen is said to be an exposure stimulus in an environment containing the pathogen. The disease process is said to occur in some plant structure (PO:0009011). This additional requirement allows root diseases to be defined separately from shoot diseases in the case that both are caused by the same pathogen (Table 2). Identification and treatment of diseases depends on the location of the infection. In the cases where the pathogen infection is systemic, whole plant (PO:0000003) is used as the plant structure.
Unlike abiotic stresses, plant diseases are processes that are
specific to their host plant. It is understood that certain plant
pathogens are capable of infecting multiple hosts [
Host
Pathogen
The initial set of abiotic stresses were determined by extracting all of the abiotic plant traits from the Plant Trait Ontology. Any time a plant trait was defined as the response to a chemical entity (ChEBI), two stress terms were created: one each for the excess and deficient state of the said chemical entity.
To collect plant disease names, the American
Phytopathology Society (APS) web publication "Common
Names of Plant Diseases" [
To convert human readable pages from APS’s "Common Name of Plant Diseases" resource, an automated process was implemented. The first step of this process is to extract all disease names, source citations, host plant and pathogen from individual host disease pages. The second step corrects troublesome names using a version controlled name map (i.e., nameMap.tsv). The third step links host and pathogen names to NCBI Taxonomy, OBO Relations Ontology (e.g., pathogen of, http://purl.obolibrary.org/obo/RO_0002556) and Plant Ontology for other entities such as host parts (e.g., leaf or root). The relationship, or interaction type, is inferred from the context of the resource and the host parts were extracted from the common name for the disease using a word matching algorithm. The final step exports the results into a tabseparated-value file to make the results available for downstream processing. This process is then repeated to optimize the quality of the name mapping and linking methods.
Given that the APS pages used to extract information were
designed for consumption by humans, the structure of the
information is not consistent. By providing a rapid, automated
process to extract, correct and publish a machine-readable
datasets, we put in place a repeatable process in which
corrections can be made relatively quickly by avoiding
unnecessary manual inputs. For instance, a change in a name
mapping file in Samara will automatically trigger a new scrape
of the APS resource using a Jenkins job running on a server
provided by the Berkeley BBOP [
The constant arms race between plant hosts, and the pathogens that infect them is guided by evolution - the resulting inference being genes that share similar sequence or domains often share similar functions. OOPS utilizes the relatedness of plant stress participants (host and pathogen in the case of disease, and chemical entity in abiotic stress), and will give scientists improved accuracy when forming hypothesis about gene function, or candidate genes that may be linked to plant traits of interest. Standardizing the definition of plant stresses, and using this standard vocabulary in the annotation of genes, genomes, QTL, mutants, and the data gathered via field books from plant breeding or field trial experiments can help in building common semantic queries for hypothesis generation, and provide accuracy in the annotation process. Using existing taxonomic hierarchies, and ontologies, researchers can leverage relatedness between both plant hosts, causative pathogens, and even chemical entities to more accurately predict targets for molecular markers, and identify candidate stress responsive gene functions. These standards will also help aggregate existing data, and assist in future-proofing new data to ensure that the massive amounts of both phenotypic and genotypic data being generated can be interoperable instead of being used for an singular task, and dumped into a repository to collect dust.
The real innovation and advancement of this work is the emphasis on automation. Much of the accuracy of the disease terms require information from a subject matter expert. These experts are often not familiar with ontologies and various formats like OWL and ontology editing tools, and would require extensive training and guidance in order to contribute. Therefore, the use of design patterns to automate ontology development, term addition, and edits, allows curators, and contributors to maintain OOPS using just a flat list. This lowered bar for ontology curation reduces effort in training new contributors, additional curators, and the overall overhead for maintenance. Efforts to simplify the construction and maintenance will also improve community involvement and adoption.
Construction of an ontology requires expert domain knowledge to ensure accuracy of the resulting hierarchy. OOPS is no exception. Plant stress spans the entirety of the plant science field, and a single person cannot hope to understand and capture all of the instances of plant stress. That is part of the benefits of using these automated tools for developing an ontology; when issues arise, or additional parental classes are needed to further group stress, they can simply be added to the upper level hierarchy list, and the reasoner can place child terms using the appropriate pattern.
As it currently stands, OOPS is available on GitHub (https://github.com/Planteome/ontology-of-plant-stress). However, it is under construction, and no stable release is available at this time.
make OOPS more robust and functional, we are planning to
implement a table editing tool that will be accessible to the
public. Some form of version control (likely GitHub) will be
used to produce robust versioning of stress term edits.
Reaching out to subject matter experts, such as CGIAR
Research Centers will be key to accurate plant disease
descriptions. Reaching out to APS will be important for
widespread adoption, and community efforts needed to stay up
to date on plant disease nomenclature, and identification. For
instance, we imagine a collaboration in which APS updates the
Common Names of Plant Diseases [