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

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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

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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

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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

Decision letter after peer review:

Thank you for submitting your article "A framework for community curation of interspecies interactions literature" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Meredith Schuman as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: Lorena Etcheverry (Reviewer #2).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1. Specify where the contributions go, how curation is done, and how they are made available.

2. Describe or reference the complete data model behind annotations, namely: concepts, methods, eventual algorithms, as well as formats for information storage and retrieval.

3. Expand on how data display and interoperability are implemented, for example, to link related information from new and existing publications. Address the possible use of graph representation to link complex information. (See more detailed comments from Reviewers 2 and 3.)

4. Explain why PHIDO has been generated rather than using existing disease terminologies (such as Mondo or DO).

5. The authors should briefly comment on the possible extension of this approach beyond pathogen-host interactions, which could increase the broader relevance of the study.

Reviewer #1 (Recommendations for the authors):

Many readers may wrongly think they will find tones of centralized information about, e.g. their present-day favorite gene. This does not seem to be the case, leading to a related question: where do the contributed curations go and how are they made available? Is there a final control from within the resource team to filter wrong curations due to bad procedures, or even directly fraudulent data treatments?

On the model extension side, feeding on new interactions proposed by users: it is not clear what kind of follow-up would be made in order to encompass the usage with appropriate growth and amelioration. Are there plans for this? Also, given that most authors are in the industry, a short statement about conflicts of interest would be desirable. Related to this, it is not clear to the reader if new, useful added curations obtained by a user, will be added to the resource and made publicly available. In an ideal world, the ten examples forming the basis of the resource should grow to thousands. I might be missing something, though.

Reviewer #2 (Recommendations for the authors):

Given my area of expertise, I am not in a position to assess the relevance of the work from a biological point of view. However, I feel that some points relating to data management and problem modeling deserve some comment.

In particular, there is no mention of the complete data model of each annotation or the formats in which this information is stored. This omission is probably because this is part of the Canto project. Still, to make this publication self-contained, it would be desirable to include this information or at least a reference to it, especially to measure the changes required.

Something that would also improve the work is more detail on how to use or visualize the data generated. Although there are a few brief lines in the section on "Display and interoperability of data", Figure 4 raises the question of how the system will behave in cases where there are already curated publications that refer to the pathogens and hosts of the new publication to be curated. It would be desirable that they are not treated in isolation but that the system allows them to be linked and then navigated in the network resulting from curating a set of publications.

Finally, in the introduction to the paper, the authors make the reckless assertion that manual biocuration is the only way to reliably represent information about functions and phenotypes. I would question this assertion given the current state of the art in LNP tools. While it is likely that, in many cases, automated annotation or curation using these techniques will not yield such accurate results, I believe it would be desirable to explore these techniques. It is also possible to think of hybrid human-in-the-loop systems where automatic techniques assist experts and simplify repetitive tasks. I believe that the paper should at least include a discussion of these issues.

Reviewer #3 (Recommendations for the authors):

Here are some specific points of confusion, questions, or suggestions for improvement:

Page 5 of the manuscript (page 6 of the full pdf):

– Lines 103-105 talk about changes in pathogenicity and virulence. It would be useful to readers to have a brief explanation of how these differ from each other and why one only applies to the pathogen while the other can apply to either the host or the pathogen.

– bottom of the page talks about "annotation types". The term "annotation type" seems to be used in a way that allows confusion with the entity being annotated. I believe that the authors intend to say that gene, genotype, and metagenotype are types of biological features (to use their term) that are annotated within PHI-Canto, each with its own set of accompanying annotation types as outlined in Table 1. If that is the correct interpretation, then I suggest modifying the text to make this more explicit with a particular focus on the sentence on lines 111-113.

Page 6 of the manuscript

Line 114 – "curators use annotation extensions" is referenced to a GOA paper. Perhaps then the sentence should specify that GO annotation curators use annotation extensions or indicate the reference is an example of this practice, using "e.g." perhaps.

Page 11 of the manuscript

Last paragraph – the text mentions that ECO terms are used to capture evidence. However, the annotation examples in Appendix 1 appear to use a combination of GO evidence codes and terms/phrases that are related to ECO term names. If ECO is being used, why not use the ECO term ids and/or term names across the board?

Page 12 of the manuscript

Top of page – what is the relationship between PHIDO and other existing disease ontologies such as Mondo or DO?

Figure 5

The NCBI taxonomy is listed in the databases section, however, it is more of a cv – it's certainly not a database like UniProtKB or PHI-base are. The Evidence and Conclusion Ontology is mentioned in the text but is not in the list of OBO Ontologies. The curated list of strains (line 247, page 11) is not shown as a PHI-base CV, although perhaps the list of strains is stored in the form of the mapping file that is shown in the figure. Is that the case? If so, then perhaps rename that box to make that more clear.

Table 1

– In the gene section, GO annotation type – are the host species and symbiont species extensions meant to indicate the interacting species? Or the species from which the gene comes? I'm assuming it means the interacting species, but this could be made more explicit.

– In the genotype section under "single species phenotype" should it not say "(Pathogen phenotype or Host phenotype)" rather than "and"?

Appendix 1

– In general, I find the header/spacing organization made it difficult to follow where one part ended and the next began. Perhaps giving letters to the sections starting "If you have…" such that there would be Section 1A, 1B, etc. might help. Also perhaps the use of some indentation so that separate sections referring to each publication will be easier to see.

– Section 1, the section on "If you have a metagenotype phenotype recording "a pathogen effector' (corresponds to footnote 5 in table 2)" – annotations for PMID:31804478: I'm confused why in the gene level GO process annotations that the gene is annotated to the specific child GO:0052034 'effector-mediated suppression of host pattern-triggered immunity', however, when the 'protein binding' and 'enzyme inhibitor activity' GO function annotations are made, the part_of annotation extension is to a grandparent of GO:0052034 that is GO:0140590 'effector-mediated suppression of host defenses. I would have thought that the part_of annotations would have been to GO:0052034. Why is this not the case?

Table 1 and Appendix 1 – both refer to the example paper involving mutualism. By definition, in a mutualist relationship, neither partner is a pathogen as disease is not caused. I realize this issue is likely beyond the scope of this paper to discuss, but I wonder about the inclusion of this annotation in the PHI resource since it does not involve a pathogen. Is it because the species has been seen to be a pathogen in other cases? Or is it that PHI includes some non-pathogenic interactions as well? If the first case, it brings to bear how to define something as a pathogen when (as is true for almost all organisms that cause disease in another organism) it only causes disease in some situations and not others (which is of course relevant to the host-pathogen-environment disease triangle mentioned in the text and Figure 1). If it is the second case, might it make sense to think about the scope of the resource as related to its name? Since mechanisms of colonization are often shared between pathogens and beneficial commensals alike, including annotations for symbionts beyond known pathogens would be useful. However, if these are regularly included, more prominent statements to that effect made on the PHI website and in publications would inform users.

Essential revisions:

1. Specify where the contributions go, how curation is done, and how they are made available.

Points – Where the contributions go and how they are made available.

Once data curated in PHI-Canto has been checked by a species expert, the data is added to the Pathogen-Host Interactions Database (PHI-base), which is freely available at www.phi-base.org. Information on PHI-base version 5 is given in this manuscript, and we also cite our recent publication by Urban et al. (2022; doi.org/10.1093/nar/gkab1037), which includes a detailed explanation and screenshots of the new gene-centric web display.

Point – How curation is done.

The approach taken to article curation is fully described in Appendix 2, where the reader is guided through a step-by-step worked example for PHI-Canto for the fully curated article by King et al. (2017), ‘A conserved fungal glycosyltransferase facilitates pathogenesis of plants by enabling hyphal growth on solid surfaces (PMID:29020037)’. In addition, we have published ten tutorial videos on YouTube that cover different aspects of the PHI-Canto curation process, plus three introductory videos on what PHI-base is, the criteria for selecting curatable publications into PHI-base, and the value of curating your publications into PHI-base. These videos are available at https://www.youtube.com/@PHI-base.

In addition to the above, the following detailed author response highlights the areas in the manuscript already covering the requested information and the newly added link to the YouTube channel videos on PHI-Canto and PHI-base.

In the section “Display and interoperability of data” the following original text explains where the contributions go:

“All data curated in PHI-Canto will be displayed in PHI-base version 5, introduced in (Urban et al., 2022).”

This text has now been modified for further clarification.

“All data curated in PHI-Canto will be displayed in the new gene-centric version 5 of PHI-base, introduced in Urban et al., 2022.”

This point is further reinforced in the Figure 4 legend with original text:

“After all annotations have been made, the session is submitted to PHI-base.”

Which has now been amended to:

“After all annotations have been made, the session is submitted into PHI-base version 5.”

In the worked curation example in Appendix 2 there are already instructions on how to view the example annotations on our new PHI-base gene-centric display pages:

“The information curated from this publication is available on the new gene centric PHI-base 5 website (http://phi5.phi-base.org, search for PHIG:308 and PHIG:307).”

In the Introduction we introduce PHI-base, its URL and state how the curated data is freely available for use (note: we have since added the additional reference for Urban et al., 2020):

“The pathogen–host interaction research communities are an example of a domain of the biological sciences exhibiting a literature deluge (Figure 1). The Pathogen–Host Interactions Database, 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 (Rodriguez-Iglesias et al., 2016; Urban et al., 2020; Urban et al., 2022).”

Regarding how curation is done, we have a section titled “Summary of the PHI-Canto curation process” which thoroughly explains how to do curation as stated in the opening sentence of the original text:

“The PHI-Canto curation process is outlined in Figure 4, Figure 4 —figure supplement 1, the PHI-Canto user documentation and a detailed worked example is provided in Appendix 2.”

This text has further been updated in the revised manuscript to include information on the location of PHI-Canto video training tutorials:

“The PHI-Canto curation process is outlined in Figure 4, Figure 4 —figure supplement 1, the PHI-Canto user documentation, a detailed worked example provided in Appendix 2 and curation tutorials on the PHI-base YouTube channel (https://www.youtube.com/@PHI-base), under the playlist ‘PHI-Canto tutorial videos’.”

The text at the end of this section states:

“Once the curation process is complete, the curator submits the session for review by a nominated species expert.”

This step will enable quality control of curated data before it is publicly available in PHI-base (which we also noted in Urban et al. 2022).

A link to our comprehensive PHI-Canto user documentation is also provided in the “Code availability” section:

“The source code for PHI-Canto’s user documentation is available on GitHub, at https://github.com/PHI-base/canto-docs. The user documentation is licensed under the MIT license. The published format of the user documentation is available online at https://canto.phi-base.org/docs/index.”

In summary, data curated using PHI-Canto will be displayed in our new PHI-base version 5 gene-centric web display. This data is freely available for searches, for use and/or for downloading into other applications. Prior to display in PHI-base the curated data is checked by species experts. Extensive PHI-Canto training materials are available.

2. Describe or reference the complete data model behind annotations, namely: concepts, methods, eventual algorithms, as well as formats for information storage and retrieval.

We requested feedback from the editor regarding the above revision, as we felt some of the information requested (e.g. ‘eventual algorithms’) was not requested in the written communication by the reviewers and was not relevant. The editor clarified that we should “clearly explain the data models and what is behind them including any algorithms”, but that we should inform them if “any of the examples we listed under data model clarification are not relevant”. We have further described our data model and its related topics below.

Point – entity-relationship models.

We have included a basic entity–relationship model in Figure 3 – supplement 1 illustrating the new entities that were created for PHI-Canto. We have now also included two further Figure 3 supplements that expand on this information, referenced in the ‘Changes to the Canto data model and configuration’ section of the manuscript.

“To implement PHI-Canto several new entities were added to the Canto data model in order to support pathogen–host curation, as well as new configuration options (the new entities are illustrated in Figure 3 —figure supplement 1). These entities were ‘strain’, ‘metagenotype’ and ‘metagenotype annotation’. The complete data model for PHI-Canto is illustrated in Figure 3 ­­­—figure supplements 2 and 3.”

These new supplements are Figure 3 – supplement 2, which illustrates the entity–relationship model for the main Canto database, and Figure 3 – supplement 3, which illustrates the entity–relationship model for a curation session database in Canto.

Point – concepts.

We understand the reviewer’s use of the word ‘concepts’ to be equivalent to our use of the word ‘entities’. The entities that are used in PHI-Canto are included in the Figure 3 supplements as described in ‘Point – entity-relationship models’.

Point – methods.

We note that the annotation process is described at a conceptual level in Figure 4. With regards to the implementation of the process in code, PHI-Canto’s complete source code can be viewed at the Canto repository on GitHub (see the ‘Code Availability section’), where it is made available under the GNU General Public License, version 3 (GPLv3).

Point – eventual algorithms.

With regards to 'eventual algorithms', there is no particular algorithm of note that underlies PHI-Canto's data model, so we did not believe that describing the code that supports PHI-Canto's data model would be relevant. Much of PHI-Canto's code exists simply to validate the data entered by the curator and to store said data in the correct location in the database. The code supporting PHI-Canto's data model is not amenable to description as pseudocode, since it involves multiple source code modules (many from externally developed software libraries) and totals thousands of lines of code.

Point – Information storage and retrieval methods.

Text has been added to the Methods section ‘Changes to the Canto data model and configuration’ providing a general description of how PHI-Canto stores its data.

“PHI-Canto stores its data in a series of relational databases using the SQLite database engine. A primary database stores data shared across all curation sessions, and each curation session also has its own database to store data related to a single publication (such as genes, genotypes, metagenotypes, etc.). PHI-Canto can export its data as a JSON file or more specialized formats, for example the GO Annotation File (GAF) format.”

3. Expand on how data display and interoperability are implemented, for example, to link related information from new and existing publications. Address the possible use of graph representation to link complex information. (See more detailed comments from Reviewers 2 and 3.)

Point – How data display and interoperability are implemented.

The PHI-base version 5 gene centric pages will display all curated information for a gene from multiple publications – both from new and existing publications.

For example

The curated data is made available within the new gene centric version 5 of PHI-base, where on a single gene page all published information from multiple publications, i.e. both new and existing are presented. For interoperability within PHI-base version 5, if the first host target is known for a pathogen gene/protein/other entities, there is a direct link-out to this related gene centric PHI-base page. Similarly, if there is already or will occur in the future a double gene and/or multi-gene functional analysis for either the pathogen or the host or both, then there is/will be a direct link-out(s) to this/these related gene centric PHI-base page(s).

We have amended the text in the Discussion to describe this further

“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.”

Point – Addressing the use of graph representation.

With regards to a graph representation of the data, we are aware of the examples the reviewer described, and we agree that this type of representation could be preferable. However, our data model is currently constrained by the developers of Canto (Rutherford et al., 2014; doi: 10.1093/bioinformatics/btu103), who use a relational data model and currently have no plans to implement a graph data model or a graph representation. We acknowledge that query languages like GraphQL can provide a graph-based interface to an existing relational data model, but we believe this would require a significant technological investment. For PHI-base, we plan to enable a graph representation of the data by integrating with existing knowledge graph tools, such as KnetMiner (www.knetminer.com; doi.org/10.1111/pbi.13583), which will provide graph-based queries on PHI-base (albeit only on select species for which knowledge graphs will be provided, i.e. Arabidopsis, rice, wheat, eight plant and human infecting fungal ascomycete pathogens, and two non-pathogenic yeast species). We will also use KnetMiner integration to embed subgraphs of the complete knowledge graph into the gene-centric pages on the PHI-base 5 website.

We have amended the text in our discussion to include a reference to graph-based representation.

“Our future intentions are two-fold: firstly, 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.”

4. Explain why PHIDO has been generated rather than using existing disease terminologies (such as Mondo or DO).

Point – Why we have developed PHIDO.

We would like to clarify that PHIDO is not intended to compete with existing disease ontologies: it is instead being used as a placeholder, until the time when its terms can be replaced with terms from existing disease ontologies. PHIDO was an expedient solution, in the sense that it provided the fastest way for us to test the process of curating diseases with PHI-Canto. This is because we only had to convert the existing list of disease names already in PHI-base into a controlled vocabulary, thus removing the need to wait for maintainers of other ontologies to add terms for us (as reported in Urban et al., 2022).

Additionally, we were required to use terms from PHIDO due to the lack of representation for plant and animal diseases in existing ontologies or vocabularies. Plant disease, in particular, is very underrepresented, with the ontologies we surveyed having either inappropriate semantics (e.g. the Plant Trait Ontology focusing on traits related to disease, rather than the diseases themselves) or still being in development (e.g. the Plant Stress Ontology). The majority of source ontologies used by MONDO are human-centric, and DO is exclusively for human disease, yet human disease represents only part of the focus of PHI-base (~35%). Furthermore, our choice of vocabularies is limited by the fact that Canto currently only supports ontologies in OBO format (for historical reasons).

We have begun the process of harmonizing disease names in PHI-base with terms from existing disease ontologies – such as MONDO, DO, and the National Cancer Institute Thesaurus – with the ultimate aim of using terms from those ontologies in curation, instead of terms from PHIDO. As general vocabularies for animal and plant disease emerge or are identified, we will extend this procedure to those diseases.

We have also added additional text to the manuscript to make this clearer as recorded below in our detailed response to Reviewer #3 recommendations for the authors.

5. The authors should briefly comment on the possible extension of this approach beyond pathogen-host interactions, which could increase the broader relevance of the study.

Point – Extending PHI-Canto beyond pathogen-host interactions.

We acknowledge the lack of discussion about extending the tool for broader interspecies interactions. These examples may have been omitted from a previous draft due to word count restrictions. We have included additional text in the discussion to suggest some possible extended use cases.

“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 plant, 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.”

Reviewer #1 (Recommendations for the authors):

Many readers may wrongly think they will find tones of centralized information about, e.g. their present-day favorite gene. This does not seem to be the case, leading to a related question: where do the contributed curations go and how are they made available? Is there a final control from within the resource team to filter wrong curations due to bad procedures, or even directly fraudulent data treatments?

On the model extension side, feeding on new interactions proposed by users: it is not clear what kind of follow-up would be made in order to encompass the usage with appropriate growth and amelioration. Are there plans for this? Also, given that most authors are in the industry, a short statement about conflicts of interest would be desirable. Related to this, it is not clear to the reader if new, useful added curations obtained by a user, will be added to the resource and made publicly available. In an ideal world, the ten examples forming the basis of the resource should grow to thousands. I might be missing something, though.

Points – centralized information, where contributed curations go, how they are made available and how curation is checked.

We believe that the Reviewer #1 has overlooked several key pieces of information regarding the display of curated data within a gene-centric web page in PHI-base version 5, that PHI-Canto curated display is displayed in the freely accessible PHI-base, and that PHI-Canto curated data is checked by a species expert prior to display in PHI-base. More detailed responses to reviewer #1 first point recommendations to authors have already been addressed in the author response to the Essential revisions point 1.

Point – extension of the model beyond pathogen-host interactions.

Regarding the model extensions a section of text has now been added containing some example communities as noted in point 6 of the essential revisions. Implementing these model extensions would be followed up by the individual communities where they would be able to use the freely able Canto source code and load up either newly developed or existing ontologies as required.

Point – conflict of interest.

The following text in the manuscript declares no conflicts of interest

“Ethics declarations, Competing interests.The authors declare no competing interests.”

Point – adding new curations to PHI-base.

The reviewer states that “it is not clear to the reader if new, useful added curations obtained by a user, will be added to the resource and made publicly available.” Responses to this point have already been addressed in the author response to the Essential revisions point 1.

In summary the curated data will be added to the publicly available PHI-base. If an author has more data on an interaction, this will only be added to PHI-base once this data has been through the peer review process.

Additionally the reviewer states “In an ideal world, the ten examples forming the basis of the resource should grow to thousands. I might be missing something, though.” It appears that the reviewer has overlooked the text that the ten publications were selected for ‘trial’ curation and that we expect many thousands to be curated in the future.

“Ten publications covering a wide range of typical plant, human, and animal pathogen-host interactions were selected for trial curation in PHI-Canto (Table 2).”

To clarify this points in the manuscripts for future readers we have added ‘Trial’ to the section heading of the manuscript which now reads:

“Trial curation of interspecies interaction publications”

and some additional text to describe that we expect curation to expand beyond the initial ten trial publications with publication authors curating their own data.

“Ten publications covering a wide range of typical plant, human, and animal pathogen–host interactions were selected for trial curation in PHI-Canto before the tool was made available to publication authors and communities to add further publications (Table 2).”

Reviewer #2 (Recommendations for the authors):

Given my area of expertise, I am not in a position to assess the relevance of the work from a biological point of view. However, I feel that some points relating to data management and problem modeling deserve some comment.

In particular, there is no mention of the complete data model of each annotation or the formats in which this information is stored. This omission is probably because this is part of the Canto project. Still, to make this publication self-contained, it would be desirable to include this information or at least a reference to it, especially to measure the changes required.

Something that would also improve the work is more detail on how to use or visualize the data generated. Although there are a few brief lines in the section on "Display and interoperability of data", Figure 4 raises the question of how the system will behave in cases where there are already curated publications that refer to the pathogens and hosts of the new publication to be curated. It would be desirable that they are not treated in isolation but that the system allows them to be linked and then navigated in the network resulting from curating a set of publications.

Finally, in the introduction to the paper, the authors make the reckless assertion that manual biocuration is the only way to reliably represent information about functions and phenotypes. I would question this assertion given the current state of the art in LNP tools. While it is likely that, in many cases, automated annotation or curation using these techniques will not yield such accurate results, I believe it would be desirable to explore these techniques. It is also possible to think of hybrid human-in-the-loop systems where automatic techniques assist experts and simplify repetitive tasks. I believe that the paper should at least include a discussion of these issues.

Point – data models.

We thank the reviewer for identifying the lack of a mention of a complete data model of each annotation and for the formats in which it is stored. The reviewer assumes correctly that this was omitted due to being part of the Canto project. However, to make this information clearer to our readers we have amended the manuscript and addressed these points in more detail in Essential revision point 3.

Point – data visualization.

The reviewer has queried how data will be visualized and what happens when older and newer publications looking at the same gene interactions are curated. The data will be visualized on gene-centric pages within the new version of PHI-base 5. These gene-centric pages will collate all the information i.e. new and existing from multiple publications into one gene page. Again more detail is provided in response to the Essential revision point 3.

Point – manual techniques compared to automated techniques for curation.

The reviewer has identified our lack of discussion regarding manual techniques compared to automated techniques for curation. We acknowledge that our initial statement in favor of manual curation may have been too bold and have amended the following text in the Introduction section:

“Due to the complexity of the biology, manual biocuration is currently the only way to reliably represent information about function and phenotype in databases and knowledge bases (Wood, Sternberg, & Lipshitz, 2022).”

To the updated text:

“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, Sternberg, & Lipshitz, 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.”

We have also updated the Discussion section with a discussion comparing manual curation to automated curation with regards to PHI-Canto.

“With regards to our focus on manual curation, we recognize that great progress has been made with machine learning (ML) approaches in recent times. However, Wood, Sternberg & Lipshitz (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.”

Reviewer #3 (Recommendations for the authors):

Here are some specific points of confusion, questions, or suggestions for improvement:

Page 5 of the manuscript (page 6 of the full pdf):

– Lines 103-105 talk about changes in pathogenicity and virulence. It would be useful to readers to have a brief explanation of how these differ from each other and why one only applies to the pathogen while the other can apply to either the host or the pathogen.

Point – Description of pathogenicity and virulence.

This is an observant point. In fact, this information was included in an earlier version of the manuscript, but later omitted to keep the word count down. New text has been added to clarify how pathogenicity and virulence differ from each other.

“Each metagenotype can be annotated with pathogen–host interaction phenotypes to capture changes in pathogenicity (caused by alterations to the pathogen) and changes in virulence (caused by alterations to the host and/or the pathogen). Pathogenicity is a property of the pathogen that describes the ability of the pathogen to cause an infectious disease in another organism. When a pathogenic organism causes disease, the severity of the disease that occurs is referred to as ‘virulence’ and this can also be dependent upon the host organism.”

– Bottom of the page talks about "annotation types". The term "annotation type" seems to be used in a way that allows confusion with the entity being annotated. I believe that the authors intend to say that gene, genotype, and metagenotype are types of biological features (to use their term) that are annotated within PHI-Canto, each with its own set of accompanying annotation types as outlined in Table 1. If that is the correct interpretation, then I suggest modifying the text to make this more explicit with a particular focus on the sentence on lines 111-113.

Point – How we describe different “annotation types”.

We thank the reviewer for this observation and understanding of our intended meaning that gene, genotype, and metagenotype are types of biological features that are annotated within PHI-Canto, each with its own set of accompanying annotation types as outlined in Table 1. We have made changes to Table 1 and the manuscript text to help clarify this. Further details below.

Changes made to Table 1

We have changed the title from

‘Annotation types and selected Annotation extensions used in PHI-Canto’

to

‘Annotation types and selected annotation extensions used in PHI-Canto, grouped by the biological feature being annotated.’

We have changed the Table 1 section headings from

‘Gene annotation type’

to

‘Annotation types for the gene biological feature’,

‘Genotype annotation type’

to

‘Annotation types for the genotype biological feature’,

and ‘Metagenotype annotation type’

to

‘Annotation types for the metagenotype biological feature’.

The following changes in the main text have also been made

“In PHI-Canto, ‘annotation’ is the task of relating a specific piece of knowledge to a biological feature. To curate a wide variety of experiment types, three groupings of annotation types are available in PHI-Canto, covering ‘metagenotype’, ‘genotype’ (of a single species) and ‘gene’ annotation types (Table 1).”

Has been changed to

“In PHI-Canto, ‘annotation’ is the task of relating a specific piece of knowledge to a biological feature. Three types of biological features can be annotated in PHI-Canto: genes, genotypes and metagenotypes. Genotypes can be further specified as pathogen genotypes or host genotypes. Each of these biological features has a corresponding set of annotation types. The relation between biological features, annotation types and the values that can be used for annotation are shown in Table 1.”

Page 6 of the manuscript

Line 114 – "curators use annotation extensions" is referenced to a GOA paper. Perhaps then the sentence should specify that GO annotation curators use annotation extensions or indicate the reference is an example of this practice, using "e.g." perhaps.

Point – Use of GO annotation curators as an example.

We thank the reviewer for this observation and have added further detail to the text as suggested to clarify that GO curators are an example of curators using an annotation extension.

“To capture additional biologically relevant information associated with an annotation, curators use the concept of annotation extensions (which include Gene Ontology annotations described by Huntley et al., 2014) to extend the primary annotation.”

Page 11 of the manuscript

Last paragraph – the text mentions that ECO terms are used to capture evidence. However, the annotation examples in Appendix 1 appear to use a combination of GO evidence codes and terms/phrases that are related to ECO term names. If ECO is being used, why not use the ECO term ids and/or term names across the board?

Point – Different evidence codes are used for GO annotations and phenotype annotations.

GO annotations in Canto are configured to use GO evidence codes. We use ECO (Experiment and Conclusion Ontology) terms to curate the experimental evidence used in making a phenotype annotation and PHI-ECO (PHI-Experimental Conditions Ontology) terms to curate experimental conditions. As we understand it, GO does not permit any evidence codes to be used on its annotations except the GO evidence codes, so terms from ECO would not be applicable for GO annotations. To make this clearer, the following text has been added to Section 1D under the first example GO annotation.

“Note: GO annotations use GO evidence codes (http://geneontology.org/docs/guide-go-evidence-codes/).”

For the phenotype annotation examples the following text has been added to Section 1A

“Note: Phenotype annotations use evidence codes modeled on the Evidence & Conclusion Ontology (ECO). Evidence code ‘Cell growth assay’ corresponds to ‘cell growth assay evidence’ (ECO:0001563).”

Section 1B

“Note: Phenotype annotations use evidence codes modeled on ECO. Evidence code ‘Macroscopic observation (qualitative observation)’ corresponds to the new ECO term ‘qualitative macroscopy evidence’ (ECO:0006342).”

Section 1C

“Note: Phenotype annotations use evidence codes modeled on ECO. Evidence code ‘Microscopy’ corresponds to ‘microscopy evidence’ (ECO:0001098).”

Point – How we have developed and used ECO terms.

With regards to the evidence codes that appear to be related to ECO term names, the discrepancy between the evidence codes used in PHI-Canto and the terms in ECO is due to the fact that, for historical reasons, Canto does not load terms directly from ECO, but rather uses text that is closely aligned to the term names in ECO. In light of this, we have been working to ensure that any new evidence codes added to PHI-Canto also have a corresponding ontology term added to ECO. This is described in the following original text of the manuscript.

“Annotations in PHI-Canto include experimental evidence, which is specified by a term from a subset of the Evidence & Conclusion Ontology (ECO) (Giglio et al., 2019). Experimental evidence codes specific to pathogen-host interaction experiments have been developed and submitted to ECO.”

We have been active in this area and the following ECO terms have now been newly created (or edited) and were recently released by ECO (05_08_2022).

sporulation assay evidence (ECO:0006344)

asexual sporulation assay evidence (ECO:0006345)

sexual sporulation assay evidence (ECO:0006346)

quantitative macroscopy evidence (ECO:0006343)

qualitative macroscopy evidence (ECO:0006342)

host penetration assay evidence (ECO:0006348)

host-surrogate penetration assay evidence (ECO:0006347)

host colonization assay evidence (ECO:0001830) (the comment was edited)

The current images and training materials under development for this manuscript have not yet been updated with these small text change discrepancies between the final term names used by ECO and the initial evidence names used in Canto. A note has been added to the examples of phenotype annotation evidence used Appendix 1 to illustrate more recent updates and slight changes in phrase. Details have been recorded in our response to the ‘Point – Different evidence codes are used for GO annotations and phenotype annotations.’

Page 12 of the manuscript

Top of page – what is the relationship between PHIDO and other existing disease ontologies such as Mondo or DO?

We have described this relationship in detail in Essential revision 4. covered in the ‘Point – why we have developed PHIDO.’ and we have also added further clarification to the manuscript text by altering the original text from

“Diseases are specified by a controlled vocabulary of disease names (called PHIDO), which was derived from disease names curated in previous versions of PHI-base.”

to

“Diseases are specified by a controlled vocabulary of disease names (called PHIDO), which was derived from disease names curated in previous versions of PHI-base (Urban et al., 2022). PHIDO was developed as a placeholder to allow disease names to be annotated on a wide variety of pathogen interactions, including those on plant, human, animal and invertebrate hosts, especially where such diseases were not described in any existing ontology.”

Figure 5

The NCBI taxonomy is listed in the databases section, however, it is more of a cv – it's certainly not a database like UniProtKB or PHI-base are. The Evidence and Conclusion Ontology is mentioned in the text but is not in the list of OBO Ontologies. The curated list of strains (line 247, page 11) is not shown as a PHI-base CV, although perhaps the list of strains is stored in the form of the mapping file that is shown in the figure. Is that the case? If so, then perhaps rename that box to make that more clear.

Point – Why we have listed NCBI taxonomy as a database.

We believe that the reviewer is mistaken in this comment and will continue to list NCBI taxonomy in the databases section of Figure 5. We have taken this information from https://www.ncbi.nlm.nih.gov/taxonomy where the landing page states that it is a database. “The Taxonomy Database is a curated classification and nomenclature for all of the organisms in the public sequence databases. This currently represents about 10% of the described species of life on the planet.”

Point – Adding The Evidence and Conclusion Ontology to Figure 5.

We thank the reviewer for spotting this omission and The Evidence and Conclusion Ontology has now been added to the list of OBO ontologies within Figure 5.

Point – Adding the list of strains as a controlled vocabulary to Figure 5.

This is a helpful suggestion and we have added the strain lists to the Controlled Vocabulary box in Figure 5.

Table 1

– In the gene section, GO annotation type – are the host species and symbiont species extensions meant to indicate the interacting species? Or the species from which the gene comes? I'm assuming it means the interacting species, but this could be made more explicit.

Point – How the annotation extensions refer to the interacting species.

The reviewer is correct, the extensions indicate the interacting species. A primary GO annotation is made and then an annotation extension can be used to indicate the interacting species, hence ‘with host species’ or ‘with symbiont species’.

– In the genotype section under "single species phenotype" should it not say "(Pathogen phenotype or Host phenotype)" rather than "and"?

Point – How the “single species phenotype” can refer to a “Pathogen phenotype or a Host phenotype”.

We agree with the reviewer that in Table 1, in the genotype section under "single species phenotype" it would be clearer to say "(Pathogen phenotype or Host phenotype)". The text has been changed.

Appendix 1

– In general, I find the header/spacing organization made it difficult to follow where one part ended and the next began. Perhaps giving letters to the sections starting "If you have…" such that there would be Section 1A, 1B, etc. might help. Also perhaps the use of some indentation so that separate sections referring to each publication will be easier to see.

Point – Layout of Appendix 1

We thank the reviewer for this helpful suggestion and apologize that the organization of Appendix 1 was difficult to follow. Following the reviewer’s guidance we have added the suggested subsections names to each section within Appendix 1 and also a new contents section just after the opening paragraph in Appendix 1. Here is a list of the Appendix 1 contents

“Contents:

Section 1: Annotation Extensions for curating pathogen-host interaction phenotypes on metagenotypes

Section 1A: If you have a metagenotype phenotype recording ‘unaffected pathogenicity’ (corresponds to footnote ^‡ in Table 2)

Section 1B: If you have a metagenotype phenotype recording ‘altered pathogenicity or virulence’ (corresponds to footnote ^§ in Table 2)

Section 1C: If you have a metagenotype phenotype recording ‘mutualism’ (corresponds to footnote ^** in Table 2)

Section 1D: If you have a metagenotype phenotype recording ‘a pathogen effector’ (corresponds to footnote ^†† in Table 2)

Section 2: Annotation Extensions for curating gene-for-gene phenotypes on metagenotypes

Section 2A: If you have a metagenotype phenotype recording ‘a gene-for-gene interaction’ (corresponds to footnote ^{‡ ‡} in Table 2)

Section 2B: If you have a metagenotype phenotype recording ‘an inverse gene-for-gene interaction’ (corresponds to footnote ^{¶ ¶} in Table 2)

Section 3: Annotation Extensions for curating single species phenotypes (pathogen phenotypes or host phenotypes)

Section 3A: Example of an in vitro pathogen phenotype (corresponds to footnote ^¶ in Table 2)

Section 3B: Example of an in vitro pathogen chemistry phenotype (corresponds to footnote ^*** in Table 2)

Section 3C: Example of an in vivo host phenotype (corresponds to footnote ^{§ §} in Table 2)”

– Section 1, the section on "If you have a metagenotype phenotype recording "a pathogen effector' (corresponds to footnote 5 in table 2)" – annotations for PMID:31804478: I'm confused why in the gene level GO process annotations that the gene is annotated to the specific child GO:0052034 'effector-mediated suppression of host pattern-triggered immunity', however, when the 'protein binding' and 'enzyme inhibitor activity' GO function annotations are made, the part_of annotation extension is to a grandparent of GO:0052034 that is GO:0140590 'effector-mediated suppression of host defenses. I would have thought that the part_of annotations would have been to GO:0052034. Why is this not the case?

Point – Aligning the part_of GO annotation extension to the primary GO BP annotation

We thank the reviewer for noting this annotation oversight, which may have occurred as new GO terms were being newly developed at the time of curation and PHI-Canto development. The 'protein binding' and 'enzyme inhibitor activity' GO function annotations should also use the part_of annotation extension GO:0052034 'effector-mediated suppression of host pattern-triggered immunity'. These annotations have now been altered in Appendix 1.

Table 1 and Appendix 1 – both refer to the example paper involving mutualism. By definition, in a mutualist relationship, neither partner is a pathogen as disease is not caused. I realize this issue is likely beyond the scope of this paper to discuss, but I wonder about the inclusion of this annotation in the PHI resource since it does not involve a pathogen. Is it because the species has been seen to be a pathogen in other cases? Or is it that PHI includes some non-pathogenic interactions as well? If the first case, it brings to bear how to define something as a pathogen when (as is true for almost all organisms that cause disease in another organism) it only causes disease in some situations and not others (which is of course relevant to the host-pathogen-environment disease triangle mentioned in the text and Figure 1). If it is the second case, might it make sense to think about the scope of the resource as related to its name? Since mechanisms of colonization are often shared between pathogens and beneficial commensals alike, including annotations for symbionts beyond known pathogens would be useful. However, if these are regularly included, more prominent statements to that effect made on the PHI website and in publications would inform users.

Point – Inclusion of a mutualistic interaction to illustrate the extension potential of PHI-Canto to curate alternative interactions.

We thank the reviewer for this observation. We decided to include this publication within the ten trial papers to illustrate that PHI-Canto could be used to curate mutualism relationships. The PHI-base release 4.14 contains curated data for 4847 publications and only two of these publications are for a mutualism interaction. In these two examples there is an interaction shift from mutualism to enhanced antagonism/loss of mutualism (PHI:576 and PHI:578). We were able to successfully curate this shift within PHI-base 4 as a proof of concept idea. Whilst developing PHI-Canto one of these mutualist interaction publications was also included to confirm that curation of this type of interaction was also possible with the new tool. One of the reasons we wish to publish in eLife is so that knowledge of the curation tool can be shared and extended to additional communities (see author response to Essential revisions point 6). We have added the following additional text to the footnote ^** of Table 2 which links out to the example of curating 'mutualism' that is available in Appendix 1.

^“**Example of curating 'mutualism' available in Appendix 1. Although ‘mutualism interactions’ are generally out of scope for PHI-base, PHI-Canto can be used to curate these publications if required. In this study, the fungal gene mutation altered the interaction from mutualistic to antagonistic.”

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

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