The increasingly vast amount of data being produced in research communities can be difficult to manage, making it challenging for both humans and computers to organise and connect information from different sources. Currently, software tools that allow authors to curate peer-reviewed life science publications are designed solely for single species, or closely related species that do not interact.

Although most research communities are striving to make their data FAIR (Findable, Accessible, Interoperable and Reusable), it is particularly difficult to curate detailed information based on interactions between two or more species (interspecies), such as pathogen-host interactions. As a result, there was a lack of tools to support multi-species interaction databases, leading to a reliance on labour-intensive curation methods.

To address this problem, Cuzick et al. used the Pathogen-Host Interactions database (PHI-base), which curates knowledge from the text, tables and figures published in over 200 journals, as a case study. A framework was developed that could capture the many observable traits (phenotype annotations) for interactions and link them directly to the combination of genotypes involved in those interactions across multiple scales – ranging from microscopic to macroscopic. This demonstrated that it was possible to build a framework of software tools to enable curation of interactions between species in more detail than had been done before.

Cuzick et al. developed an online tool called PHI-Canto that allows any researcher to curate published pathogen-host interactions between almost any known species. An ontology – a collection of concepts and their relations – was created to describe the outcomes of pathogen-host interactions in a standardised way. Additionally, a new concept called the ‘metagenotype’ was developed which represents the combination of a pathogen and a host genotype and can be easily annotated with the phenotypes arising from each interaction.

The newly curated multi-species FAIR data on pathogen-host interactions will enable researchers in different disciplines to compare and contrast interactions across species and scales. Ultimately, this will assist the development of new approaches to reduce the impact of pathogens on humans, livestock, crops and ecosystems with the aim of decreasing disease while increasing food security and biodiversity. The framework is potentially adoptable by any research community investigating interactions between species and could be adapted to explore other harmful and beneficial interspecies interactions.

Recent technological advancements across the biological sciences have resulted in an increasing volume of peer-reviewed publications reporting experimental data and conclusions. To increase the value of this highly fragmented knowledge, biocurators manually extract the data from publications and represent it in a standardized and interconnected way following the FAIR (Findable, Accessible, Interoperable, and Reusable) Data Principles (International Society for Biocuration, 2018; Wilkinson et al., 2016). Curated functional data is then made available in online databases, either organism- or clade-specific (e.g. model organism databases) or those supporting multiple kingdoms of life (e.g. PHI-base Urban et al., 2022), Alliance of Genomes Resources (Agapite et al., 2020), or UniProt (Bateman et al., 2021). Due to the complexity of the biology and the specificity of the curation requirements, manual biocuration is currently the most reliable way to capture information about function and phenotype in databases and knowledge bases (Wood et al., 2022). For pathogen–host interactions, the original publications do not provide details of specific strains, variants, and their associated genotypes and phenotypes, nor the relative impact on pathogenicity and virulence, in a standardized machine-readable format. The expert curator synergizes knowledge from different representations (text, graphs, images) into clearly defined machine-readable syntax. The development of curation tools with clear workflows supporting the use of biological ontologies and controlled vocabularies has standardized curation efforts, reduced ambiguity in annotation, and improved the maintenance of the curated corpus as biological knowledge evolves (International Society for Biocuration, 2018).

The pathogen–host interaction research communities are an example of a domain of the biological sciences exhibiting a literature deluge (Figure 1). PHI-base (phi-base.org) is an open-access FAIR biological database containing data on bacterial, fungal, and protist genes proven to affect (or not to affect) the outcome of pathogen–host interactions (Rodríguez-Iglesias et al., 2016; Urban et al., 2020; Urban et al., 2022). Since 2005, PHI-base has manually curated phenotype data associated with underlying genome-level changes from peer-reviewed pathogen–host interaction literature. Information is also provided on the target sites of some anti-infective chemistries (Urban et al., 2020). Knowledge related to pathogen–host interaction phenotypes is increasingly relevant, as infectious microbes continually threaten global food security, human health across the life course, farmed animal health and wellbeing, tree health, and ecosystem resilience (Brown et al., 2012; Fisher et al., 2018; Fisher et al., 2012; Fisher et al., 2022; Smith et al., 2019). Rising resistance to antimicrobial compounds, increased globalization, and climate change indicate that infectious microbes will present ever-greater economic and societal threats (Bebber et al., 2013; Chaloner et al., 2021; Cook et al., 2021). In order to curate relevant publications into PHI-base (version 4), professional curators have, since 2011, entered 81 different data types into a text file (Urban et al., 2017). However, increasing publication numbers and data complexity required more robust curation procedures and greater involvement from publication authors.

Figure 1

Download asset Open asset

We were unable to locate any curation frameworks or tools capable of capturing the interspecies interactions required for PHI-base. PomBase, the fission yeast (Schizosaccharomyces pombe) database developed Canto, a web-based tool supporting curation by both professional biocurators and publication authors (Rutherford et al., 2014). Canto already had support for annotating genes from multiple species in the same curation session, but it could not support annotation of the interactions between species, nor the annotation of genes from naturally occurring strains. We extended and customized Canto to support the annotation of multiple strains of multiple species, and the modeling and annotation of interspecies interactions between pathogens and hosts, to create a new tool: PHI-Canto (the Pathogen–Host Interaction Community Annotation Tool). Likewise, there were no existing biomedical ontologies that could accurately describe pathogen–host interaction phenotypes at the depth and breadth required for PHI-base. Infectious disease formation depends on a series of complex and dynamic interactions between pathogenic species and their potential hosts, and also requires the correct biotic and/or abiotic environmental conditions (Scholthof, 2007), as illustrated by the concept of the ‘disease triangle’ (Figure 2). All these interrelated factors must be recorded in order to sufficiently describe a pathogen–host interaction.

Figure 2

Download asset Open asset

In this study, three key issues were addressed in order to develop the curation framework for interspecies interactions: first, to support the classification of genes as ‘pathogen’ or ‘host,’ and enable the variations of the same gene in different strains to be captured; second, formulating the concept of a ‘metagenotype’ to represent the interaction between specific strains of both a pathogen and a host within a multispecies genotype; and thirdly, developing supporting ontologies and controlled vocabularies, including the generic Pathogen–Host Interaction Phenotype Ontology (PHIPO), to annotate phenotypes connected to genotypes at the level of a single species (pathogen or host) and multiple species (pathogen–host interaction phenotypes). Leading on from these advances, we discuss how the overall curation framework described herein, the concept of annotating metagenotypes, and ongoing generic ontology development, is a suitable approach for adoption and use by a wide range of research communities in the life sciences focused on different types of interspecies interactions occurring within or across kingdoms in different environments and at multiple (micro to macro) scales.

Scalable and accurate curation of data within the scientific literature is of paramount importance due to the increasing quantity of publications and the complexity of experiments within each publication. PHI-base is an example of a freely available, manually curated database, which has been curating literature using professional curators since 2005 (Winnenburg et al., 2006).

Here, we have described the development of PHI-Canto to allow the curation of the interspecies pathogen–host interaction literature by professional curators and publication authors. This curated data is then made available on the new gene-centric version 5 of PHI-base, where all information (i.e. new and existing) on a single gene from several publications is presented on a single page, with links to external resources providing information on interacting genes, proteins, and other entities.

Several adaptations to the original single-species community annotation tool, Canto (Rutherford et al., 2014), were required to convert this tool for interspecies use. Notably, the need to annotate an interaction involving two different organisms necessitated the development of a novel concept, the ‘metagenotype’ (Figure 3), in order to record a combined experimental genotype involving both a pathogen and a host. This is, to our knowledge, the first example of such an approach to interspecies interaction curation.

Curation of pathogen–host interactions in PHI-Canto also necessitated the development of a new phenotype ontology (PHIPO) to annotate pathogen–host interaction phenotypes in sufficient detail across the broad range of host species that were curated in PHI-base (n=234 in version 4.14 of PHI-base). The functional annotation of genes involved in interspecies interactions is a complex and challenging task, requiring ongoing modifications to the Gene Ontology and occasional major refactoring to deprecate legacy terms (Carbon et al., 2021). PHIPO development and maintenance will also be an ongoing task, with both authors and professional curators requesting new terms and edits to existing terms and the ontology structure. Maintenance will be made more sustainable by the incorporation of logical definitions that are aligned across phenotype ontologies in collaboration with the uPheno project (Shefchek et al., 2020).

To improve the efficiency of the curation process, we are suggesting that authors follow an author checklist during manuscript preparation (Appendix 3). This will improve the presentation of key information (e.g. species names, gene identifiers, etc.) in published manuscripts, thus enabling more efficient and comprehensive curation that is human- and machine-readable. The annotation procedures described here using PHI-Canto can be used to extract data buried in small-scale publications and increase the accessibility of the curated article to a wider range of potential users, for example, computational biologists, thereby improving the FAIR status of the data. The current data in PHI-base has been obtained from >200 journals (Figure 7) and, therefore, represents highly fragmented knowledge which is exceptionally difficult to use by professionals in other disciplines. The feasibility of scalable community curation with Canto is evidenced by PomBase (Lock et al., 2020), where Canto S. pombe annotations from over 1000 publications are provided by publication authors, with the data made available within 24 hr of review (https://curation.pombase.org/pombe/stats/annotation).

Figure 7

Download asset Open asset

With regard to our focus on manual curation, we recognize that great progress has been made with machine learning (ML) approaches in recent times. However, Wood et al., 2022 note that the data being curated from publications are ‘categorical, highly complex, and with hundreds of thousands of heterogeneous classes, often not explicitly labeled.’ There are no published examples of ML approaches outperforming an expert curator in accuracy, which is paramount in the medical field. However, curation by experts could provide a highly reliable corpus that could be used for training ML systems. Our aspiration is that ML and expert curators can collaborate in a virtuous cycle whereby expert curators continually review and refine the ML models, while the manual work of finding publications and entity recognition is handled by the ML system.

Our future intentions are twofold: first, a graph-based representation of the data will be enabled by integration with knowledge network generation tools, such as Knetminer (Hassani-Pak et al., 2021), where subgraphs of the knowledge graph could be embedded into each gene-centric page on the PHI-base 5 website. Second, within PHI-Canto, we intend to address the issues associated with maximizing the inherent value of the natural sequence variation between species strains, and the associated altered phenotypic outcomes observed at multiple scales, in different types of interactions and/or environments. PHI-base already contains information on numerous species with multiple experimental strains, and natural sequence variation between strains can result in alterations at the genome level that affect the subsequently observed phenotypes. Strain-specific sequence variation is not captured in the reference proteomes stored by UniProt, even though accession numbers from these proteomes are often used in PHI-Canto. Currently, when a curator enters a gene with a taxonomic identifier below the species rank, PHI-Canto maps the identifier to the corresponding identifier at the species rank (thus removing any strain details from the organism name), and the curator specifies a strain to differentiate gene variants in naturally occurring strains. However, this does not change the taxonomic identifier linked to the UniProtKB accession number (nor its sequence), so the potential for inaccuracy remains. To mitigate this, the future plan is to record the strain-specific sequence of the gene using an accession number from a database from the International Nucleotide Sequence Database Collaboration (Arita et al., 2021).

The release of PHI-Canto to the community will occur gradually through various routes. Community curation will be promoted by working with journals to capture the publication data at the source, at the point of manuscript acceptance. We will also target specific research communities (e.g. those working on a particular pathogen and/or research topic) by inviting authors to curate their own publications. Authors may contact us directly to request support while curating their publications in PHI-Canto.

PHI-Canto, PHI-base, and PHIPO were devised and built over the past seven years to serve the research needs of a specific international research community interested in exploring the wide diversity of common and species-specific mechanisms underlying pathogen attack and host defense in plants, animals, humans, and other host organisms caused by fungi, protists and bacteria. However, it should be noted that the underlying developments to Canto’s data model – especially the concept of annotating metagenotypes – could be of use to communities focused on different types of interspecies interactions. Possible future uses of the PHI-Canto schema could include insect–plant interactions (both beneficial and detrimental), endosymbiotic relationships such as mycorrhiza–plant rhizosphere interactions, nodulating bacteria–plant rhizosphere interactions, fungi–fungi interactions, plant–plant interactions or bacteria–insect interactions, and non-pathogenic relationships in natural environments such as bulk soil, rhizosphere, phyllosphere, air, freshwater, estuarine water or seawater, and human–animal, animal–bird, human–insect, animal–insect, bird–insect interactions in various anatomical locations (e.g. gut, lung, and skin). The schema could also be extended to situations where phenotype–genotype relations have been established for predator–prey relationships or where there is competition in herbivore–herbivore, predator–predator or prey–prey relationships in the air, on land, or in the water. Finally, the schema could be used to explore strain-to-strain interactions within a species when different biological properties have been noted. Customizing Canto to use other ontologies and controlled vocabularies is as simple as editing a configuration file, as shown in Source code 1.

Datasets generated for use within the curation framework are available as GitHub links in the manuscript section 'Data availability'. Code is available as GitHub links in the manuscript section 'Code availability'. PHI-Canto curated data is available here https://doi.org/10.5281/zenodo.7428788.

1. 1. Cuzick A
   2. Wood V
   3. Velasquez M
   4. Wilkes JM

   (2022) Zenodo

   PHI-Canto approved curation sessions: December 2022.

   https://doi.org/10.5281/zenodo.7428788

1. 1. Agapite J
   2. Albou LP
   3. Aleksander S
   4. Argasinska J
   5. Arnaboldi V
   6. Attrill H
   7. Bello SM
   8. Blake JA
   9. Blodgett O
   10. Bradford YM
   11. Bult CJ
   12. Cain S
   13. Calvi BR
   14. Carbon S
   15. Chan J
   16. Chen WJ
   17. Cherry JM
   18. Cho J
   19. Christie KR
   20. Crosby MA
   21. Pons JD
   22. Dolan ME
   23. Santos G
   24. Dunn B
   25. Dunn N
   26. Eagle A
   27. Ebert D
   28. Engel SR
   29. Fashena D
   30. Frazer K
   31. Gao S
   32. Gondwe F
   33. Goodman J
   34. Gramates LS
   35. Grove CA
   36. Harris T
   37. Harrison MC
   38. Howe DG
   39. Howe KL
   40. Jha S
   41. Kadin JA
   42. Kaufman TC
   43. Kalita P
   44. Karra K
   45. Kishore R
   46. Laulederkind S
   47. Lee R
   48. MacPherson KA
   49. Marygold SJ
   50. Matthews B
   51. Millburn G
   52. Miyasato S
   53. Moxon S
   54. Mueller HM
   55. Mungall C
   56. Muruganujan A
   57. Mushayahama T
   58. Nash RS
   59. Ng P
   60. Paulini M
   61. Perrimon N
   62. Pich C
   63. Raciti D
   64. Richardson JE
   65. Russell M
   66. Gelbart SR
   67. Ruzicka L
   68. Schaper K
   69. Shimoyama M
   70. Simison M
   71. Smith C
   72. Shaw DR
   73. Shrivatsav A
   74. Skrzypek M
   75. Smith JR
   76. Sternberg PW
   77. Tabone CJ
   78. Thomas PD
   79. Thota J
   80. Toro S
   81. Tomczuk M
   82. Tutaj M
   83. Tutaj M
   84. Urbano JM
   85. Auken KV
   86. Slyke CEV
   87. Wang SJ
   88. Weng S
   89. Westerfield M
   90. Williams G
   91. Wong ED
   92. Wright A
   93. Yook K

   (2020) Alliance of genome resources portal: unified model organism research platform

   Nucleic Acids Research 48:D650–D658.

   https://doi.org/10.1093/nar/gkz813

   • PubMed
   • Google Scholar
2. 1. Arita M
   2. Karsch-Mizrachi I
   3. Cochrane G

   (2021) The international nucleotide sequence database collaboration

   Nucleic Acids Research 49:D121–D124.

   https://doi.org/10.1093/nar/gkaa967

   • PubMed
   • Google Scholar
3. 1. Ashburner M
   2. Ball CA
   3. Blake JA
   4. Botstein D
   5. Butler H
   6. Cherry JM
   7. Davis AP
   8. Dolinski K
   9. Dwight SS
   10. Eppig JT
   11. Harris MA
   12. Hill DP
   13. Issel-Tarver L
   14. Kasarskis A
   15. Lewis S
   16. Matese JC
   17. Richardson JE
   18. Ringwald M
   19. Rubin GM
   20. Sherlock G

   (2000) Gene Ontology: tool for the unification of biology

   Nature Genetics 25:25–29.

   https://doi.org/10.1038/75556

   • Google Scholar
4. 1. Bateman A
   2. Martin MJ
   3. Orchard S
   4. Magrane M
   5. Agivetova R
   6. Ahmad S
   7. Alpi E
   8. Bowler-Barnett EH
   9. Britto R
   10. Bursteinas B
   11. Bye-A-Jee H
   12. Coetzee R
   13. Cukura A
   14. Da Silva A
   15. Denny P
   16. Dogan T
   17. Ebenezer T
   18. Fan J
   19. Castro LG
   20. Garmiri P
   21. Georghiou G
   22. Gonzales L
   23. Hatton-Ellis E
   24. Hussein A
   25. Ignatchenko A
   26. Insana G
   27. Ishtiaq R
   28. Jokinen P
   29. Joshi V
   30. Jyothi D
   31. Lock A
   32. Lopez R
   33. Luciani A
   34. Luo J
   35. Lussi Y
   36. MacDougall A
   37. Madeira F
   38. Mahmoudy M
   39. Menchi M
   40. Mishra A
   41. Moulang K
   42. Nightingale A
   43. Oliveira CS
   44. Pundir S
   45. Qi G
   46. Raj S
   47. Rice D
   48. Lopez MR
   49. Saidi R
   50. Sampson J
   51. Sawford T
   52. Speretta E
   53. Turner E
   54. Tyagi N
   55. Vasudev P
   56. Volynkin V
   57. Warner K
   58. Watkins X
   59. Zaru R
   60. Zellner H
   61. Bridge A
   62. Poux S
   63. Redaschi N
   64. Aimo L
   65. Argoud-Puy G
   66. Auchincloss A
   67. Axelsen K
   68. Bansal P
   69. Baratin D
   70. Blatter MC
   71. Bolleman J
   72. Boutet E
   73. Breuza L
   74. Casals-Casas C
   75. de Castro E
   76. Echioukh KC
   77. Coudert E
   78. Cuche B
   79. Doche M
   80. Dornevil D
   81. Estreicher A
   82. Famiglietti ML
   83. Feuermann M
   84. Gasteiger E
   85. Gehant S
   86. Gerritsen V
   87. Gos A
   88. Gruaz-Gumowski N
   89. Hinz U
   90. Hulo C
   91. Hyka-Nouspikel N
   92. Jungo F
   93. Keller G
   94. Kerhornou A
   95. Lara V
   96. Le Mercier P
   97. Lieberherr D
   98. Lombardot T
   99. Martin X
   100. Masson P
   101. Morgat A
   102. Neto TB
   103. Paesano S
   104. Pedruzzi I
   105. Pilbout S
   106. Pourcel L
   107. Pozzato M
   108. Pruess M
   109. Rivoire C
   110. Sigrist C
   111. Sonesson K
   112. Stutz A
   113. Sundaram S
   114. Tognolli M
   115. Verbregue L
   116. Wu CH
   117. Arighi CN
   118. Arminski L
   119. Chen C
   120. Chen Y
   121. Garavelli JS
   122. Huang H
   123. Laiho K
   124. McGarvey P
   125. Natale DA
   126. Ross K
   127. Vinayaka CR
   128. Wang Q
   129. Wang Y
   130. Yeh LS
   131. Zhang J
   132. Ruch P
   133. Teodoro D
   134. The UniProt Consortium

   (2021) Uniprot: the universal protein knowledgebase in 2021

   Nucleic Acids Research 49:D480–D489.

   https://doi.org/10.1093/nar/gkaa1100

   • Google Scholar
5. 1. Bebber DP
   2. Ramotowski MAT
   3. Gurr SJ

   (2013) Crop pests and pathogens move polewards in a warming world

   Nature Climate Change 3:985–988.

   https://doi.org/10.1038/nclimate1990

   • Google Scholar
6. 1. Breen S
   2. Williams SJ
   3. Winterberg B
   4. Kobe B
   5. Solomon PS

   (2016) Wheat PR-1 proteins are targeted by necrotrophic pathogen effector proteins

   The Plant Journal 88:13–25.

   https://doi.org/10.1111/tpj.13228

   • PubMed
   • Google Scholar
7. 1. Brown GD
   2. Denning DW
   3. Gow NAR
   4. Levitz SM
   5. Netea MG
   6. White TC

   (2012) Hidden killers: human fungal infections

   Science Translational Medicine 4:165rv13.

   https://doi.org/10.1126/scitranslmed.3004404

   • PubMed
   • Google Scholar
8. 1. Carbon S
   2. Douglass E
   3. Good BM
   4. Unni DR
   5. Harris NL
   6. Mungall CJ
   7. Basu S
   8. Chisholm RL
   9. Dodson RJ
   10. Hartline E
   11. Fey P
   12. Thomas PD
   13. Albou LP
   14. Ebert D
   15. Kesling MJ
   16. Mi H
   17. Muruganujan A
   18. Huang X
   19. Mushayahama T
   20. LaBonte SA
   21. Siegele DA
   22. Antonazzo G
   23. Attrill H
   24. Brown NH
   25. Garapati P
   26. Marygold SJ
   27. Trovisco V
   28. dos Santos G
   29. Falls K
   30. Tabone C
   31. Zhou P
   32. Goodman JL
   33. Strelets VB
   34. Thurmond J
   35. Garmiri P
   36. Ishtiaq R
   37. Rodríguez-López M
   38. Acencio ML
   39. Kuiper M
   40. Lægreid A
   41. Logie C
   42. Lovering RC
   43. Kramarz B
   44. Saverimuttu SCC
   45. Pinheiro SM
   46. Gunn H
   47. Su R
   48. Thurlow KE
   49. Chibucos M
   50. Giglio M
   51. Nadendla S
   52. Munro J
   53. Jackson R
   54. Duesbury MJ
   55. Del-Toro N
   56. Meldal BHM
   57. Paneerselvam K
   58. Perfetto L
   59. Porras P
   60. Orchard S
   61. Shrivastava A
   62. Chang HY
   63. Finn RD
   64. Mitchell AL
   65. Rawlings ND
   66. Richardson L
   67. Sangrador-Vegas A
   68. Blake JA
   69. Christie KR
   70. Dolan ME
   71. Drabkin HJ
   72. Hill DP
   73. Ni L
   74. Sitnikov DM
   75. Harris MA
   76. Oliver SG
   77. Rutherford K
   78. Wood V
   79. Hayles J
   80. Bähler J
   81. Bolton ER
   82. De Pons JL
   83. Dwinell MR
   84. Hayman GT
   85. Kaldunski ML
   86. Kwitek AE
   87. Laulederkind SJF
   88. Plasterer C
   89. Tutaj MA
   90. Vedi M
   91. Wang SJ
   92. D’Eustachio P
   93. Matthews L
   94. Balhoff JP
   95. Aleksander SA
   96. Alexander MJ
   97. Cherry JM
   98. Engel SR
   99. Gondwe F
   100. Karra K
   101. Miyasato SR
   102. Nash RS
   103. Simison M
   104. Skrzypek MS
   105. Weng S
   106. Wong ED
   107. Feuermann M
   108. Gaudet P
   109. Morgat A
   110. Bakker E
   111. Berardini TZ
   112. Reiser L
   113. Subramaniam S
   114. Huala E
   115. Arighi CN
   116. Auchincloss A
   117. Axelsen K
   118. Argoud-Puy G
   119. Bateman A
   120. Blatter MC
   121. Boutet E
   122. Bowler E
   123. Breuza L
   124. Bridge A
   125. Britto R
   126. Bye-A-Jee H
   127. Casas CC
   128. Coudert E
   129. Denny P
   130. Estreicher A
   131. Famiglietti ML
   132. Georghiou G
   133. Gos A
   134. Gruaz-Gumowski N
   135. Hatton-Ellis E
   136. Hulo C
   137. Ignatchenko A
   138. Jungo F
   139. Laiho K
   140. Le Mercier P
   141. Lieberherr D
   142. Lock A
   143. Lussi Y
   144. MacDougall A
   145. Magrane M
   146. Martin MJ
   147. Masson P
   148. Natale DA
   149. Hyka-Nouspikel N
   150. Orchard S
   151. Pedruzzi I
   152. Pourcel L
   153. Poux S
   154. Pundir S
   155. Rivoire C
   156. Speretta E
   157. Sundaram S
   158. Tyagi N
   159. Warner K
   160. Zaru R
   161. Wu CH
   162. Diehl AD
   163. Chan JN
   164. Grove C
   165. Lee RYN
   166. Muller HM
   167. Raciti D
   168. Van Auken K
   169. Sternberg PW
   170. Berriman M
   171. Paulini M
   172. Howe K
   173. Gao S
   174. Wright A
   175. Stein L
   176. Howe DG
   177. Toro S
   178. Westerfield M
   179. Jaiswal P
   180. Cooper L
   181. Elser J
   182. The Gene Ontology Consortium

   (2021) The Gene Ontology resource: enriching a gold mine

   Nucleic Acids Research 49:D325–D334.

   https://doi.org/10.1093/nar/gkaa1113

   • PubMed
   • Google Scholar
9. 1. Chaloner TM
   2. Gurr SJ
   3. Bebber DP

   (2021) Plant pathogen infection risk tracks global crop yields under climate change

   Nature Climate Change 11:710–715.

   https://doi.org/10.1038/s41558-021-01104-8

   • Google Scholar
10. 1. Cook NM
    2. Chng S
    3. Woodman TL
    4. Warren R
    5. Oliver RP
    6. Saunders DG

    (2021) High frequency of fungicide resistance-associated mutations in the wheat yellow rust pathogen Puccinia striiformis f. sp. tritici

    Pest Management Science 77:3358–3371.

    https://doi.org/10.1002/ps.6380

    • PubMed
    • Google Scholar
11. Software

    1. Cuzick A
    2. Seager J

    (2022a) PHI-base experimental conditions Ontology, version 9ee8e15

    Github.

    https://github.com/PHI-base/phi-eco
12. Software

    1. Cuzick A
    2. Seager J

    (2022b) PHI-base disease list, version 6eafb25

    Github.

    https://github.com/PHI-base/phido
13. Software

    1. Cuzick A
    2. Seager J

    (2022c) PHIPO extension Ontology, version e95208e

    Github.

    https://github.com/PHI-base/phipo_ext
14. Software

    1. Cuzick A
    2. Seager J
    3. Urban M

    (2022d) PHI-base data repository, version 44ecb7f

    Github.

    https://github.com/PHI-base/data
15. 1. Durinx C
    2. McEntyre J
    3. Appel R
    4. Apweiler R
    5. Barlow M
    6. Blomberg N
    7. Cook C
    8. Gasteiger E
    9. Kim J-H
    10. Lopez R
    11. Redaschi N
    12. Stockinger H
    13. Teixeira D
    14. Valencia A

    (2016) Identifying ELIXIR core data resources

    F1000Research 5:ELIXIR-2422.

    https://doi.org/10.12688/f1000research.9656.2

    • PubMed
    • Google Scholar
16. 1. Federhen S
    2. Clark K
    3. Barrett T
    4. Parkinson H
    5. Ostell J
    6. Kodama Y
    7. Mashima J
    8. Nakamura Y
    9. Cochrane G
    10. Karsch-Mizrachi I

    (2014) Toward richer metadata for microbial sequences: replacing strain-level NCBI taxonomy taxids with Bioproject, Biosample and Assembly records

    Standards in Genomic Sciences 9:1275–1277.

    https://doi.org/10.4056/sigs.4851102

    • PubMed
    • Google Scholar
17. 1. Fisher MC
    2. Henk DA
    3. Briggs CJ
    4. Brownstein JS
    5. Madoff LC
    6. McCraw SL
    7. Gurr SJ

    (2012) Emerging fungal threats to animal, plant and ecosystem health

    Nature 484:186–194.

    https://doi.org/10.1038/nature10947

    • PubMed
    • Google Scholar
18. 1. Fisher MC
    2. Hawkins NJ
    3. Sanglard D
    4. Gurr SJ

    (2018) Worldwide emergence of resistance to antifungal drugs challenges human health and food security

    Science 360:739–742.

    https://doi.org/10.1126/science.aap7999

    • PubMed
    • Google Scholar
19. 1. Fisher MC
    2. Alastruey-Izquierdo A
    3. Berman J
    4. Bicanic T
    5. Bignell EM
    6. Bowyer P
    7. Bromley M
    8. Brüggemann R
    9. Garber G
    10. Cornely OA
    11. Gurr SJ
    12. Harrison TS
    13. Kuijper E
    14. Rhodes J
    15. Sheppard DC
    16. Warris A
    17. White PL
    18. Xu J
    19. Zwaan B
    20. Verweij PE

    (2022) Tackling the emerging threat of antifungal resistance to human health

    Nature Reviews. Microbiology 20:557–571.

    https://doi.org/10.1038/s41579-022-00720-1

    • PubMed
    • Google Scholar
20. Book

    1. Flor HH

    (1956)

    The complementary genic systems in flax and flax rust

    In: Demerec M, editors. Advances in Genetics. Academic Press. pp. 29–54.

    • Google Scholar
21. 1. Giglio M
    2. Tauber R
    3. Nadendla S
    4. Munro J
    5. Olley D
    6. Ball S
    7. Mitraka E
    8. Schriml LM
    9. Gaudet P
    10. Hobbs ET
    11. Erill I
    12. Siegele DA
    13. Hu JC
    14. Mungall C
    15. Chibucos MC

    (2019) ECO, the Evidence & Conclusion Ontology: community standard for evidence information

    Nucleic Acids Research 47:D1186–D1194.

    https://doi.org/10.1093/nar/gky1036

    • Google Scholar
22. 1. Hassani-Pak K
    2. Singh A
    3. Brandizi M
    4. Hearnshaw J
    5. Parsons JD
    6. Amberkar S
    7. Phillips AL
    8. Doonan JH
    9. Rawlings C

    (2021) Knetminer: a comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species

    Plant Biotechnology Journal 19:1670–1678.

    https://doi.org/10.1111/pbi.13583

    • PubMed
    • Google Scholar
23. 1. Houterman PM
    2. Ma L
    3. van Ooijen G
    4. de Vroomen MJ
    5. Cornelissen BJC
    6. Takken FLW
    7. Rep M

    (2009) The effector protein Avr2 of the xylem-colonizing fungus Fusarium oxysporum activates the tomato resistance protein I-2 Intracellularly

    The Plant Journal 58:970–978.

    https://doi.org/10.1111/j.1365-313X.2009.03838.x

    • PubMed
    • Google Scholar
24. 1. Huntley RP
    2. Harris MA
    3. Alam-Faruque Y
    4. Blake JA
    5. Carbon S
    6. Dietze H
    7. Dimmer EC
    8. Foulger RE
    9. Hill DP
    10. Khodiyar VK
    11. Lock A
    12. Lomax J
    13. Lovering RC
    14. Mutowo-Meullenet P
    15. Sawford T
    16. Van Auken K
    17. Wood V
    18. Mungall CJ

    (2014) A method for increasing expressivity of Gene Ontology annotations using a compositional approach

    BMC Bioinformatics 15:155.

    https://doi.org/10.1186/1471-2105-15-155

    • PubMed
    • Google Scholar
25. 1. Huntley RP
    2. Sawford T
    3. Mutowo-Meullenet P
    4. Shypitsyna A
    5. Bonilla C
    6. Martin MJ
    7. O’Donovan C

    (2015) The GOA database: Gene Ontology annotation updates for 2015

    Nucleic Acids Research 43:D1057–D1063.

    https://doi.org/10.1093/nar/gku1113

    • PubMed
    • Google Scholar
26. 1. International Society for Biocuration

    (2018) Biocuration: distilling data into knowledge

    PLOS Biology 16:e2002846.

    https://doi.org/10.1371/journal.pbio.2002846

    • PubMed
    • Google Scholar
27. 1. Jackson RC
    2. Balhoff JP
    3. Douglass E
    4. Harris NL
    5. Mungall CJ
    6. Overton JA

    (2019) ROBOT: a tool for automating ontology workflows

    BMC Bioinformatics 20:407.

    https://doi.org/10.1186/s12859-019-3002-3

    • PubMed
    • Google Scholar
28. 1. Jackson R
    2. Matentzoglu N
    3. Overton JA
    4. Vita R
    5. Balhoff JP
    6. Buttigieg PL
    7. Carbon S
    8. Courtot M
    9. Diehl AD
    10. Dooley DM
    11. Duncan WD
    12. Harris NL
    13. Haendel MA
    14. Lewis SE
    15. Natale DA
    16. Osumi-Sutherland D
    17. Ruttenberg A
    18. Schriml LM
    19. Smith B
    20. Stoeckert Jr. CJ
    21. Vasilevsky NA
    22. Walls RL
    23. Zheng J
    24. Mungall CJ
    25. Peters B

    (2021) OBO foundry in 2021: Operationalizing open data principles to evaluate ontologies

    Database 2021:ebaab069.

    https://doi.org/10.1093/database/baab069

    • Google Scholar
29. 1. Jones JDG
    2. Dangl JL

    (2006) The plant immune system

    Nature 444:323–329.

    https://doi.org/10.1038/nature05286

    • PubMed
    • Google Scholar
30. 1. Kanyuka K
    2. Igna AA
    3. Solomon PS
    4. Oliver RP

    (2022) The rise of necrotrophic effectors

    The New Phytologist 233:11–14.

    https://doi.org/10.1111/nph.17811

    • PubMed
    • Google Scholar
31. 1. King R
    2. Urban M
    3. Lauder RP
    4. Hawkins N
    5. Evans M
    6. Plummer A
    7. Halsey K
    8. Lovegrove A
    9. Hammond-Kosack K
    10. Rudd JJ

    (2017) A conserved fungal Glycosyltransferase facilitates pathogenesis of plants by enabling Hyphal growth on solid surfaces

    PLOS Pathogens 13:e1006672.

    https://doi.org/10.1371/journal.ppat.1006672

    • PubMed
    • Google Scholar
32. 1. Lock A
    2. Harris MA
    3. Rutherford K
    4. Hayles J
    5. Wood V

    (2020) Community curation in Pombase: enabling fission yeast experts to provide detailed, standardized, sharable annotation from research publications

    Database 2020:baaa028.

    https://doi.org/10.1093/database/baaa028

    • PubMed
    • Google Scholar
33. 1. Montecchi-Palazzi L
    2. Beavis R
    3. Binz P-A
    4. Chalkley RJ
    5. Cottrell J
    6. Creasy D
    7. Shofstahl J
    8. Seymour SL
    9. Garavelli JS

    (2008) The PSI-MOD community standard for representation of protein modification data

    Nature Biotechnology 26:864–866.

    https://doi.org/10.1038/nbt0808-864

    • PubMed
    • Google Scholar
34. 1. Musen MA
    2. Team P

    (2015) The protege project: A look back and a look forward

    AI Matters 1:4–12.

    https://doi.org/10.1145/2757001.2757003

    • PubMed
    • Google Scholar
35. 1. Oughtred R
    2. Rust J
    3. Chang C
    4. Breitkreutz B-J
    5. Stark C
    6. Willems A
    7. Boucher L
    8. Leung G
    9. Kolas N
    10. Zhang F
    11. Dolma S
    12. Coulombe-Huntington J
    13. Chatr-Aryamontri A
    14. Dolinski K
    15. Tyers M

    (2021) The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions

    Protein Science 30:187–200.

    https://doi.org/10.1002/pro.3978

    • PubMed
    • Google Scholar
36. 1. Rodríguez-Iglesias A
    2. Rodríguez-González A
    3. Irvine AG
    4. Sesma A
    5. Urban M
    6. Hammond-Kosack KE
    7. Wilkinson MD

    (2016) Publishing FAIR data: an exemplar methodology utilizing PHI-base

    Frontiers in Plant Science 7:641.

    https://doi.org/10.3389/fpls.2016.00641

    • PubMed
    • Google Scholar
37. 1. Rutherford KM
    2. Harris MA
    3. Lock A
    4. Oliver SG
    5. Wood V

    (2014) Canto: an online tool for community literature curation

    Bioinformatics 30:1791–1792.

    https://doi.org/10.1093/bioinformatics/btu103

    • PubMed
    • Google Scholar
38. 1. Schoch CL
    2. Ciufo S
    3. Domrachev M
    4. Hotton CL
    5. Kannan S
    6. Khovanskaya R
    7. Leipe D
    8. Mcveigh R
    9. O’Neill K
    10. Robbertse B
    11. Sharma S
    12. Soussov V
    13. Sullivan JP
    14. Sun L
    15. Turner S
    16. Karsch-Mizrachi I

    (2020) NCBI Taxonomy: a comprehensive update on curation, resources and tools

    Database 2020:baaa062.

    https://doi.org/10.1093/database/baaa062

    • PubMed
    • Google Scholar
39. 1. Scholthof K-BG

    (2007) The disease triangle: pathogens, the environment and society

    Nature Reviews. Microbiology 5:152–156.

    https://doi.org/10.1038/nrmicro1596

    • PubMed
    • Google Scholar
40. 1. Shefchek KA
    2. Harris NL
    3. Gargano M
    4. Matentzoglu N
    5. Unni D
    6. Brush M
    7. Keith D
    8. Conlin T
    9. Vasilevsky N
    10. Zhang XA
    11. Balhoff JP
    12. Babb L
    13. Bello SM
    14. Blau H
    15. Bradford Y
    16. Carbon S
    17. Carmody L
    18. Chan LE
    19. Cipriani V
    20. Cuzick A
    21. Della Rocca M
    22. Dunn N
    23. Essaid S
    24. Fey P
    25. Grove C
    26. Gourdine J-P
    27. Hamosh A
    28. Harris M
    29. Helbig I
    30. Hoatlin M
    31. Joachimiak M
    32. Jupp S
    33. Lett KB
    34. Lewis SE
    35. McNamara C
    36. Pendlington ZM
    37. Pilgrim C
    38. Putman T
    39. Ravanmehr V
    40. Reese J
    41. Riggs E
    42. Robb S
    43. Roncaglia P
    44. Seager J
    45. Segerdell E
    46. Similuk M
    47. Storm AL
    48. Thaxon C
    49. Thessen A
    50. Jacobsen JOB
    51. McMurry JA
    52. Groza T
    53. Köhler S
    54. Smedley D
    55. Robinson PN
    56. Mungall CJ
    57. Haendel MA
    58. Munoz-Torres MC
    59. Osumi-Sutherland D

    (2020) The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species

    Nucleic Acids Research 48:D704–D715.

    https://doi.org/10.1093/nar/gkz997

    • PubMed
    • Google Scholar
41. 1. Smith KM
    2. Machalaba CC
    3. Seifman R
    4. Feferholtz Y
    5. Karesh WB

    (2019) Infectious disease and economics: the case for considering multi-sectoral impacts

    One Health 7:100080.

    https://doi.org/10.1016/j.onehlt.2018.100080

    • PubMed
    • Google Scholar
42. 1. Urban M.
    2. Pant R
    3. Raghunath A
    4. Irvine AG
    5. Pedro H
    6. Hammond-Kosack KE

    (2015) The pathogen-host interactions database (PHI-base): additions and future developments

    Nucleic Acids Research 43:D645–D655.

    https://doi.org/10.1093/nar/gku1165

    • Google Scholar
43. 1. Urban M
    2. Cuzick A
    3. Rutherford K
    4. Irvine A
    5. Pedro H
    6. Pant R
    7. Sadanadan V
    8. Khamari L
    9. Billal S
    10. Mohanty S
    11. Hammond-Kosack KE

    (2017) PHI-base: a new interface and further additions for the multi-species pathogen-host interactions database

    Nucleic Acids Research 45:D604–D610.

    https://doi.org/10.1093/nar/gkw1089

    • PubMed
    • Google Scholar
44. 1. Urban M
    2. Cuzick A
    3. Seager J
    4. Wood V
    5. Rutherford K
    6. Venkatesh SY
    7. De Silva N
    8. Martinez MC
    9. Pedro H
    10. Yates AD
    11. Hassani-Pak K
    12. Hammond-Kosack KE

    (2020) PHI-base: the pathogen-host interactions database

    Nucleic Acids Research 48:D613–D620.

    https://doi.org/10.1093/nar/gkz904

    • Google Scholar
45. 1. Urban M
    2. Cuzick A
    3. Seager J
    4. Wood V
    5. Rutherford K
    6. Venkatesh SY
    7. Sahu J
    8. Iyer SV
    9. Khamari L
    10. De Silva N
    11. Martinez MC
    12. Pedro H
    13. Yates AD
    14. Hammond-Kosack KE

    (2022) PHI-base in 2022: a multi-species phenotype database for pathogen-host interactions

    Nucleic Acids Research 50:D837–D847.

    https://doi.org/10.1093/nar/gkab1037

    • Google Scholar
46. 1. Wilkinson MD
    2. Dumontier M
    3. Aalbersberg IJJ
    4. Appleton G
    5. Axton M
    6. Baak A
    7. Blomberg N
    8. Boiten J-W
    9. da Silva Santos LB
    10. Bourne PE
    11. Bouwman J
    12. Brookes AJ
    13. Clark T
    14. Crosas M
    15. Dillo I
    16. Dumon O
    17. Edmunds S
    18. Evelo CT
    19. Finkers R
    20. Gonzalez-Beltran A
    21. Gray AJG
    22. Groth P
    23. Goble C
    24. Grethe JS
    25. Heringa J
    26. ’t Hoen PAC
    27. Hooft R
    28. Kuhn T
    29. Kok R
    30. Kok J
    31. Lusher SJ
    32. Martone ME
    33. Mons A
    34. Packer AL
    35. Persson B
    36. Rocca-Serra P
    37. Roos M
    38. van Schaik R
    39. Sansone S-A
    40. Schultes E
    41. Sengstag T
    42. Slater T
    43. Strawn G
    44. Swertz MA
    45. Thompson M
    46. van der Lei J
    47. van Mulligen E
    48. Velterop J
    49. Waagmeester A
    50. Wittenburg P
    51. Wolstencroft K
    52. Zhao J
    53. Mons B

    (2016) The FAIR guiding principles for scientific data management and stewardship

    Scientific Data 3:160018.

    https://doi.org/10.1038/sdata.2016.18

    • PubMed
    • Google Scholar
47. 1. Winnenburg R
    2. Baldwin TK
    3. Urban M
    4. Rawlings C
    5. Köhler J
    6. Hammond-Kosack KE

    (2006) PHI-base: a new database for pathogen host interactions

    Nucleic Acids Research 34:D459–D464.

    https://doi.org/10.1093/nar/gkj047

    • PubMed
    • Google Scholar
48. 1. Wood V
    2. Sternberg PW
    3. Lipshitz HD

    (2022) Making biological knowledge useful for humans and machines

    Genetics 220:iyac001.

    https://doi.org/10.1093/genetics/iyac001

    • PubMed
    • Google Scholar
49. 1. Yates AD
    2. Allen J
    3. Amode RM
    4. Azov AG
    5. Barba M
    6. Becerra A
    7. Bhai J
    8. Campbell LI
    9. Carbajo Martinez M
    10. Chakiachvili M
    11. Chougule K
    12. Christensen M
    13. Contreras-Moreira B
    14. Cuzick A
    15. Da Rin Fioretto L
    16. Davis P
    17. De Silva NH
    18. Diamantakis S
    19. Dyer S
    20. Elser J
    21. Filippi CV
    22. Gall A
    23. Grigoriadis D
    24. Guijarro-Clarke C
    25. Gupta P
    26. Hammond-Kosack KE
    27. Howe KL
    28. Jaiswal P
    29. Kaikala V
    30. Kumar V
    31. Kumari S
    32. Langridge N
    33. Le T
    34. Luypaert M
    35. Maslen GL
    36. Maurel T
    37. Moore B
    38. Muffato M
    39. Mushtaq A
    40. Naamati G
    41. Naithani S
    42. Olson A
    43. Parker A
    44. Paulini M
    45. Pedro H
    46. Perry E
    47. Preece J
    48. Quinton-Tulloch M
    49. Rodgers F
    50. Rosello M
    51. Ruffier M
    52. Seager J
    53. Sitnik V
    54. Szpak M
    55. Tate J
    56. Tello-Ruiz MK
    57. Trevanion SJ
    58. Urban M
    59. Ware D
    60. Wei S
    61. Williams G
    62. Winterbottom A
    63. Zarowiecki M
    64. Finn RD
    65. Flicek P

    (2022) Ensembl Genomes 2022: an expanding genome resource for non-vertebrates

    Nucleic Acids Research 50:D996–D1003.

    https://doi.org/10.1093/nar/gkab1007

    • Google Scholar

Author details

1. Alayne Cuzick

   Strategic area: Protecting Crops and the Environment, Rothamsted Research, Harpenden, United Kingdom

   Contribution

   Conceptualization, Data curation, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   alayne.cuzick@rothamsted.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8941-3984

2. James Seager

   Strategic area: Protecting Crops and the Environment, Rothamsted Research, Harpenden, United Kingdom

   Contribution

   Resources, Software, Validation, Visualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7487-610X

3. Valerie Wood

   Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Data curation, Supervision, Validation, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6330-7526

4. Martin Urban

   Strategic area: Protecting Crops and the Environment, Rothamsted Research, Harpenden, United Kingdom

   Contribution

   Resources, Visualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2440-4352

5. Kim Rutherford

   Department of Biochemistry, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Resources, Software, Supervision, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6277-726X

6. Kim E Hammond-Kosack

   Strategic area: Protecting Crops and the Environment, Rothamsted Research, Harpenden, United Kingdom

   Contribution

   Conceptualization, Supervision, Funding acquisition, Methodology, Project administration, Writing – review and editing

   For correspondence

   kim.hammond-kosack@rothamsted.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9699-485X

• 492

  views

• 55

  downloads

• 4

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.