Skip to main content
PMC home page
Scientific Data logoLink to Scientific Data
. 2020 Oct 12;7:337. doi: 10.1038/s41597-020-00679-9

Protein ontology on the semantic web for knowledge discovery

Chuming Chen 1,✉, Hongzhan Huang 1, Karen E Ross 2, Julie E Cowart 1, Cecilia N Arighi 1, Cathy H Wu 1,2, Darren A Natale 2
PMCID: PMC7550340  PMID: 33046717

Abstract

The Protein Ontology (PRO) provides an ontological representation of protein-related entities, ranging from protein families to proteoforms to complexes. Protein Ontology Linked Open Data (LOD) exposes, shares, and connects knowledge about protein-related entities on the Semantic Web using Resource Description Framework (RDF), thus enabling integration with other Linked Open Data for biological knowledge discovery. For example, proteins (or variants thereof) can be retrieved on the basis of specific disease associations. As a community resource, we strive to follow the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles, disseminate regular updates of our data, support multiple methods for accessing, querying and downloading data in various formats, and provide documentation both for scientists and programmers. PRO Linked Open Data can be browsed via faceted browser interface and queried using SPARQL via YASGUI. RDF data dumps are also available for download. Additionally, we developed RESTful APIs to support programmatic data access. We also provide W3C HCLS specification compliant metadata description for our data. The PRO Linked Open Data is available at https://lod.proconsortium.org/.

Subject terms: Data integration, Data mining

Introduction

As an evolving extension to the current hypertext document web, Linked Open Data (LOD) is a new paradigm where data are published and interconnected on the web using open standards such as Uniform Resource Identifiers (URIs), Hypertext Transfer Protocol (HTTP), Resource Description Framework (RDF), Web Ontology Language (OWL) and SPARQL Protocol and RDF Query Language (SPARQL). This enables data from heterogeneous sources to be shared, integrated and queried in a web of data. Tim Berners-Lee in his web architecture note introduced a set of best practices for publishing and interlinking structured data on the web, also well known as the Linked Data principles1: (1) Use URIs as names of things; (2) Use HTTP URIs, so that people can look up those names; (3) When someone looks up a URI, provide useful information using the standards (RDF, SPARQL etc.); and (4) Include links to other URIs, so that they can discover more things.

Bio2RDF2 is an open source project that uses Semantic Web technologies to build a large network of Linked Open Data for Life Sciences from a diverse set of heterogeneously formatted sources obtained from multiple data providers. It uses federated SPARQL queries to facilitate continuous integration of life sciences data from resources such as Mouse Genome Informatics (MGI)3, Saccharomyces Genome Database (SGD)4, Rat Genome Database (RGD)5, NCBI’s Gene resources (NCBIGene)6, HUGO Gene Nomenclature Committee (HGNC)7, and Nematode Information Resource (WormBase)8. The European Bioinformatics Institute (EBI) provides an RDF platform9 that facilitates answering complex research questions through queries/exploration of integrated resources, including UniProtKB10, Reactome11, Ensembl12, and Gene Ontology13.

As formal and explicit specifications of domains of interest, ontologies consist of terms representing precisely defined entities and the relationships between them. Ontologies are increasingly being used to define the basic terms and relations in biological domains, often as the foundation for search, integration and exchange of biological data. The Protein Ontology (PRO)14 provides an ontological representation of protein-related entities, notably, including those of organism-neutral nature15. In addition to the ontology itself, PRO includes annotations and cross-reference information, and can be used to facilitate the discoverability and aggregation of data related to protein entities in the context of protein functions, pathways and drug targets. The Protein Ontology has been used by the research community for applications including named entity recognition/tagging16–18, entity definition (for example, of cell types19), and as an import for protein-related terms in other ontologies20,21. PRO Linked Open Data and SPARQL endpoint have been used to assist in orthology mapping22 and to study kinase post-translational modifications and cancer-associated mutations23. The PRO website has garnered more than 20 million hits in the past year. PRO Linked Open Data and SPARQL endpoint alone garnered 321,000 hits. PRO is ranked among the top 5 endpoints by YummyData SPARQL endpoint monitor24.

In this paper we introduce the PRO RDF data models and the metadata description of PRO Linked Open Data. We provide faceted browser, SPARQL endpoint with graphical user interface (GUI), and downloadable RDF files. We demonstrate federated SPARQL queries to answer questions for biological knowledge discovery using multiple connected resources.

Results

Each PRO term represents a distinct class of organism-neutral or organism-specific protein entities (e.g., modified forms, orthologous isoforms, and protein complexes). To help readers understand PRO RDF data models, we present an exemplar PRO term in each category using RDF Graph, RDF/XML, and Turtle formats. Links for these are provided in the relevant section, however an example RDF Graph of one PRO term (PR:000046294, RAC-alpha serine/threonine-protein kinase phosphorylated 3 (human)) is also shown in Fig. 1 and listed as Turtle format below.

Fig. 1.

Fig. 1

Open in a new tab

A PRO RDF data model (PR:000046294). Ellipse and circle shapes are RDF nodes. Rectangle shapes are RDF literals. Directed edges are RDF properties. Circle shapes represent anonymous classes or blank nodes. ‘AKT1’, used here for brevity, is the gene for ‘RAC-alpha serine/threonine-protein kinase’.

@prefix owl:<http://www.w3.org/2002/07/owl#>.

@prefix rdfs:<http://www.w3.org/2000/01/rdf-schema#>.

@prefix xsd:<http://www.w3.org/2001/XMLSchema#>.

@prefix obo:<http://purl.obolibrary.org/obo/>.

@prefix oboInOwl:<http://www.geneontology.org/formats/oboInOwl#>.

obo:PR_000046294 a                owl:Class;

rdfs:comment   “Category = organism-modification.”;

rdfs:label                       “RAC-alpha serine/threonine-protein kinase phosphorylated 3 (human)”;

rdfs:subClassOf      obo:PR_000049939, obo:PR_P31749;

rdfs:subClassOf      [a                   owl:Restriction;

owl:onProperty              obo:RO_0002160;

owl:someValuesFrom obo:NCBITaxon_9606             

];

rdfs:subClassOf   [a              owl:Restriction;

                     owl:onProperty        obo:BFO_0000051;

                     owl:someValuesFrom obo:MOD_00046

                  ];

obo:IAO_0000115          “A RAC-alpha serine/threonine-protein kinase phosphorylated 3 in human. UniProtKB:P31749, Ser-473, MOD:00046.”;

oboInOwl:hasDbXref                    “Reactome:R-HSA-377265”;

oboInOwl:hasExactSynonym “UniProtKB:P31749, Ser-473, MOD:00046”, “hAKT1/Phos:3”;

oboInOwl:hasOBONamespace “protein”;

oboInOwl:id      “PR:000046294”;

owl:equivalentClass             [a                           owl:Class;

                        owl:intersectionOf (obo:PR_000049939

                                                                     [a                                   owl:Restriction;

                             owl:onProperty        obo:RO_0002160;

                             owl:someValuesFrom obo:NCBITaxon_9606])].

                                                      ]

                                                    )

               ].

obo:RO_0002160          rdfs:label        “only_in_taxon”.

obo:NCBITaxon_9606          rdfs:label                          “Homo sapiens”.

obo:PR_000049939                 oboInOwl:hasExactSynonym “AKT1/Phos:3”.

obo:PR_P31749                           oboInOwl:hasExactSynonym   “hAKT1”.

obo:BFO_0000051           rdfs:label                             “has_part”.

obo:MOD_00046             rdfs:label                            “O-phospho-L-serine”.

PRO uniform resource identifiers (URIs)

PRO follows the rule of reusing existing identifiers whenever feasible, e.g., http://purl.obolibrary.org/obo/GO_0032991 (Gene ontology), http://purl.obolibrary.org/obo/RO_0002160” (Relations Ontology), etc. The format of PRO identifier is “PR_xxxxxxxxx”, e.g., http://purl.obolibrary.org/obo/PR_000000001. However, for UniProtKB related entries incorporated into PRO organism-gene or organism-sequence categories, we use the UniProtKB accession number and prefix it with “PR_” as its corresponding PRO identifier. e.g., http://purl.obolibrary.org/obo/PR_P00015.

Types of PRO terms

Protein Ontology terms are organized into general categories, often with organism-specific versions. Below we describe these categories. Example terms for each category are given in Table 1. Whenever possible, PRO attempts to make connections between orthologs. Extending from the concept of orthology between genes, we have introduced the concept of orthologous proteoforms. For example, ortho-isoforms are isoforms that are believed to have arisen prior to speciation and divergence of the primary sequence. That is, ortho-isoforms were true alternative isoforms (as defined above) in a common ancestor and are, quite likely, functionally equivalent. Ortho-modified forms are modified versions of ortho-isoforms where the modification(s) occur on equivalent residues.

Table 1.

Example PRO terms in each category.

Category Term Name Link to Example
Family organism-neutral PR:000000027 smad protein https://lod.proconsortium.org/rdf.html#category_family
organism-specific PR:000044507 14-3-3 protein (human) https://lod.proconsortium.org/rdf.html#category_org_family
Gene organism-neutral PR:000000364 smad2 https://lod.proconsortium.org/rdf.html#category_gene
organism-specific PR:000022736 fumarate hydratase class II https://lod.proconsortium.org/rdf.html#category_org_gene
SeqGroup organism-neutral PR:000050216 receptor-type tyrosine-protein phosphatase C isoform CD45R https://lod.proconsortium.org/rdf.html#category_seqgroup
organism-specific PR:Q9ULB1 neurexin-1-alpha (human) https://lod.proconsortium.org/rdf.html#category_org_seqgroup
Sequence organism-neutral PR:000000048 TGF-beta receptor type-2 isoform RII-1 https://lod.proconsortium.org/rdf.html#category_sequence
organism-specific PR:Q68FF6-1 ARF GTPase-activating protein GIT1 isoform 1 (mouse) https://lod.proconsortium.org/rdf.html#category_org_sequence
Modification organism-neutral PR:000049939 RAC-alpha serine/threonine-protein kinase phosphorylated 3 https://lod.proconsortium.org/rdf.html#category_modification
organism-specific PR:000046294 RAC-alpha serine/threonine-protein kinase phosphorylated 3 (human) https://lod.proconsortium.org/rdf.html#category_org_modification
Complex organism-neutral PR:000027291 phosphorylase kinase complex PHKL https://lod.proconsortium.org/rdf.html#category_complex
organism-specific PR:000036137 lipopolysaccharide receptor complex 3; endosome membrane (human) https://lod.proconsortium.org/rdf.html#category_org_complex
Open in a new tab

Category = family

Each PRO term at the family level refers to protein products of a distinct gene family arising from a common ancestor. The leaf-most nodes at this level are usually families comprising paralogous sets of gene products (of a single or multiple organisms). For example, smad2 and smad3 both encode proteins that are TGF receptor-regulated while smad1, smad5, and smad9 are all BMP receptor-regulated. Thus, “TGF-beta receptor-regulated smad protein” and “BMP receptor-regulated smad protein” are terms denoting distinct families. Note that this level collectively refers to any such grouping at any level of similarity. For example, the two families indicated above can merge into a “receptor-regulated smad protein” class and further merge (with the protein products of smad4, smad6, and smad7) into the “smad protein” class.

Category = gene

Each PRO term at the gene level refers to the protein products of a distinct gene in a reference organism and the orthologs thereof. For example, “smad2” and “smad3” are two different genes, and therefore have two different PRO entries at the gene level of distinction. The protein products of what is recognized as smad2 in humans and what is recognized as smad2 in mouse fall under the single gene-level term “smad2”. Thus, a single term at the gene-level distinction collects the protein products of (usually 1-to-1) orthologs for that gene. Organism-specific versions are (typically) defined logically as the intersection of the parent (gene-level) and the organism (taxonomic) terms. If a resource provides gene information, PRO will indicate this using the has_gene_template relation.

Category = seqgroup

Each PRO term at the seqgroup level refers to proteins encoded by the same gene that are distinguished from siblings based on differences in shared portions of encoding mRNAs. That is, all members of a given seqgroup are encoded by mRNAs that have a common subset of sequence features, either as a common subset of exons or as a common subset of sequence variations. Examples include proteins encoded by the PTPRC (CD45) gene, where each member of the CD45R subtype (CD45RA, CD45RAB, CD45RAC) minimally contains exon 4 (aka ‘A’) even while each member of a given subtype has other exons that make them distinct, and histocompatibility genes such as HLA-A, where each member of a given subtype (HLA-A*24, HLA-A*68, etc) shares a common set of variations even while each member of a given subtype has other variations that make them distinct.

Category = sequence

Each PRO term at the sequence level refers to the protein products that arise from different alleles of a given gene (sequence variants), from splice variants of a given RNA, or from alternative initiation and ribosomal frameshifting during translation. One can think of this as a mature mRNA-level distinction. For example, smad2 encodes both a long splice form and a short splice form. The protein products of each isoform are separate PRO terms. If there is clear knowledge on the equivalency of isoforms (that is, they are “ortho-isoforms”)14, then the equivalent terms from different organisms are defined as children of the sequence level terms. For example, ARF GTPase-activating protein GIT1 isoform 1 from mouse (PR:Q68FF6-1), ARF GTPase-activating protein GIT1 isoform 1 from rat (PR:Q9Z272-1), and ARF GTPase-activating protein GIT1 isoform 3 from human (PR:Q9Y2X7-3) are ortho-isoforms that are children of the sequence level term PR:000044155. If the equivalency of isoforms is not well-established, the organism-sequence term is defined as a child of the organism-gene level term.

Category = modification

Each PRO term at the modification level refers to the protein products derived from a single mRNA species that differ because of some change (or lack thereof) that occurs after the initiation of translation (co- and post-translational). This includes sequence differences due to cleavage and chemical changes to one or more amino acid residues. In general, PRO does not provide time or space information, i.e., the order in which modifications occur or the location in which they are found unless such are required to properly define the term. PRO only represents the final modified objects. If there is clear knowledge on the equivalency of the modified forms from different organisms, then the equivalent organism-modification terms are defined as children of the modification level terms. For example, RAC-alpha serine/threonine-protein kinase phosphorylated 3 from human (PR:000046294) and mouse (PR:000049940) are ortho-modified forms that are phosphorylated on the position equivalent to Ser-473 of the human protein. In the PRO hierarchy, they are children of the modification level term PR:000049939. If the equivalency of the modified forms is not well-established, the organism-modification term is defined as a child of the appropriate organism-gene term.

Category = complex

Each PRO term at the complex level25 ultimately traces to the general term “protein-containing complex” found in GO (GO:0032991), defined as “A stable assembly of two or more macromolecules, i.e. proteins, nucleic acids, carbohydrates or lipids, in which at least one component is a protein and the constituent parts function together”. Indeed, complex-level terms in PRO are imported from GO whenever possible. However, because GO complexes are (predominantly) defined with respect to function, PRO will create complex terms when a component-specific definition is desired. When doing so, PRO represents complexes explicitly–that is, by subunit composition–defining each member of the complex at the level of its isoform, variant, or modified form, whenever possible. Most complex-related terms defined by PRO are organism-specific. Note that a protein in its monomeric state, linked non-covalently to a small chemical, is not considered a protein complex by the above definition, but it can be defined as a subclass of the protein term using the CHEBI ID for the small chemical and the relation “non-covalently_bound_to”.

Dataset description

A high-quality consistent metadata description is essential to the successful discovery, exchange, and query of a Linked Dataset. The Protein Ontology Linked Open Data is accompanied with metadata description using Vocabulary of Interlinked Datasets (VoID), the Provenance vocabulary (PROV) and Dublin Core vocabulary, which are compliant with the W3C HCLS specification26. The metadata for Protein Ontology Linked Open Data is described at three levels: (1) summary level, which provides a description of a dataset that is independent of a specific version or format; (2) version level, which captures version-specific characteristics of a dataset; and (3) distribution level, which captures metadata about a specific form and version of a dataset.

Linkset is a way of identifying the content that links instances in one dataset with instances in another dataset. A separate linkset is created for each link predicate relating a particular pair of datasets. A linkset is a subset of the dataset which publishes it. The linkset itself is of type void:Linkset and provides the same metadata as a RDF distribution. The statistics relevant for a linkset are the number of triples it contains and are reported using the void:triples property. The full VoID description of PRO Linked Open Data is at https://lod.proconsortium.org/releases/latest/void.ttl.

Federated SPARQL query

Federated SPARQL query is a powerful method that allows the simultaneous search of multiple data resources and aggregates the results from multiple SPARQL endpoints. PRO RDF data can be combined with other RDF data, such as UniProt10 RDF data (at https://sparql.uniprot.org/) and DisGeNET27 RDF data (at http://rdf.disgenet.org/sparql/) as demonstrated below to generate new biological insights that are not possible using one individual resource.

Sample federated SPARQL query 1

To answer the question, “Which human proteins are potentially involved in disease via loss of function”, the query is designed to get all human proteins in PRO whose UniProtKB counterpart has variants with loss of function implicated in disease. The partial query result is shown in Fig. 2 as a knowledge graph.

Fig. 2.

Fig. 2

Open in a new tab

Knowledge graph of exemplary query result of federated SPARQL query 1 (Get all human genes in PRO whose UniProtKB counterpart has variants with loss of function implicated in disease). Ellipse shapes are RDF nodes. Rectangle shapes are RDF literals. Directed edges are RDF properties.

Query 1:

PREFIX obo:<http://www.geneontology.org/formats/oboInOwl#>

PREFIX up:<http://purl.uniprot.org/core/>

PREFIX ud:<http://purl.uniprot.org/database/>

PREFIX up:<http://purl.uniprot.org/core/>

PREFIX taxon:<http://purl.uniprot.org/taxonomy/>

PREFIX rdf:<http://www.w3.org/1999/02/22-rdf-syntax-ns#>

PREFIX rdfs:<http://www.w3.org/2000/01/rdf-schema#>

PREFIX faldo:<http://biohackathon.org/resource/faldo#>

SELECT DISTINCT?

PRO_term

(STR(?_PRO_label) AS?PRO_label)

(STRAFTER(STRBEFORE(STR(?_PRO_category), “.”), “ = “) AS?PRO_category)

?Protein

?Variant

(STR(?_Text) AS?Description)

WHERE

{

SERVICE<http://sparql.uniprot.org/sparql>

{

?Protein rdfs:seeAlso?PRO_term.?

PRO_term

up:database ud:PRO;

a up:Resource.?Protein a up:Protein.?Protein up:organism taxon:9606.?Protein up:annotation?Variant.?Variant a up:Natural_Variant_Annotation.?Variant rdfs:comment?_Text.

FILTER (CONTAINS(?_Text, ‘loss of function’))

}

?PRO_term rdfs:label?_PRO_label.

?PRO_term rdfs:comment?_PRO_category.

}

Sample federated SPARQL query 2

To answer the question, “What disease(s) are associated with AlzForum-derived sequence variants described in PRO”, we construct a query to find variants in UniProt or DisGeNET for AlzForum (https://www.alzforum.org/) PRO terms. The partial query result is shown in Fig. 3 as a knowledge graph.

Fig. 3.

Fig. 3

Open in a new tab

Knowledge graph of exemplary query result of federated SPARQL query 2 (Find variants in UniProt or DisGeNET for AlzForum PRO terms). Ellipse and circle shapes are RDF nodes. Rectangle shapes are RDF literals. Directed edges are RDF properties. Circle shapes represent anonymous classes or blank nodes.

Query 2:

PREFIX obo:<http://purl.obolibrary.org/obo/>

PREFIX up:<http://purl.uniprot.org/core/>

PREFIX ud:<http://purl.uniprot.org/database/>

PREFIX taxon:<http://purl.uniprot.org/taxonomy/>

PREFIX rdf:<http://www.w3.org/1999/02/22-rdf-syntax-ns#>

PREFIX rdfs:<http://www.w3.org/2000/01/rdf-schema#>

PREFIX sio:<http://semanticscience.org/resource/>

PREFIX ncit:<http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#>

PREFIX dcterms:<http://purl.org/dc/terms/>

PREFIX so:<http://purl.obolibrary.org/obo/SO_>

PREFIX oboInOwl:<http://www.geneontology.org/formats/oboInOwl#>

PREFIX skos:<http://www.w3.org/2004/02/skos/core#>

SELECT DISTINCT

?PRO

(?PRO_Label AS?Name)

(STRAFTER(STRBEFORE(STR(?PRO_Category), “.”), “ = “) AS?Category)

(STRBEFORE(STRAFTER(STR(?PRO_Category), “.”), “.”) AS?Comment)

(?protein AS?UniProt)

(?uniprotvar AS?UniProt_Var)

(?variant AS?DisGeNET_Var)

(STR(?variantTitle) AS?RS_ID)

(STR(?vscore) AS?VDAScore)

(?disease AS?Disease)

(STR(?diseaselabel) AS?DiseaseName)

(?gene AS?Gene)

(STR(?gSymbol) AS?GeneSymbol)

(STR(?geneTitle) AS?GeneName)

WHERE {

{

SELECT?PRO?Parent?PRO_Label?PRO_Category?uniprotvar

WHERE {

VALUES?PRO {obo:PR_000037702 obo:PR_000037753}

?PRO rdfs:subClassOf?Parent.

?Parent rdfs:seeAlso?uniprotvar.

?PRO rdfs:label?PRO_Label.

?PRO rdfs:comment?PRO_Category.

FILTER(CONTAINS(LCASE(?PRO_Category), “by = alzforum”))

FILTER(CONTAINS(LCASE(STR(?uniprotvar)), “annotation”))

}

}

SERVICE<http://sparql.uniprot.org/sparql>

{

?uniprotvar

a up:Natural_Variant_Annotation;

rdfs:seeAlso?_rs;

skos:related?up_disease.

?up_disease

a up:Disease;

rdfs:comment?up_disease_comment.

FILTER(CONTAINS(LCASE(?up_disease_comment), “alzheimer”))

[]

rdf:object?uniprotvar;

rdf:predicate up:annotation;

rdf:subject?protein;

a rdf:Statement.

BIND(IRI(REPLACE(STR(?_rs), “purl.uniprot.org”, “identifiers.org”)) AS?variant)

}

SERVICE<http://rdf.disgenet.org/sparql/>

{

OPTIONAL {

?vda sio:SIO_000628?variant,?disease;

sio:SIO_000216?vscoreIRI.?vscoreIRI sio:SIO_000300?vscore.?variant dcterms:title?variantTitle.?disease rdfs:label?diseaselabel.?disease dcterms:title?diseaseTitle.

FILTER(CONTAINS(LCASE(?diseaseTitle), “alzheimer”))

OPTIONAL {?variant so:associated_with?gene.?gene a ncit:C16612.?gene dcterms:title?geneTitle.?gene sio:SIO_000205?symbolUri.?symbolUri dcterms:title?gSymbol.

}

}

}

} ORDER BY DESC(?vscore)

Discussion

The guiding principles of FAIR data aim to facilitate the discovery, integration, and analysis of relevant datasets by humans and machines by making them Findable (F), Accessible (A), Interoperable (I), and Reusable (R)28. As a community resource, we strive to follow FAIR principles, disseminate regular updates of our data, support multiple methods for accessing, querying and downloading data in various formats, and provide documentation both for scientists and programmers. The formal FAIRness assessment29,30 results for Protein Ontology Linked Open Data are presented in Table 2. According to the results, PRO follows the FAIR principles quite well. Moreover, according to the YummyData SPARQL endpoint monitor, PRO (https://yummydata.org/endpoint/129) ranks among the top 5 endpoints by the Umaka Score24 calculated based on six criteria: Availability, Freshness, Operation, Usefulness, Validity and Performance.

Table 2.

The formal FAIRness assessment results for Protein Ontology Linked Open Dataset.

Metric Requirement Resource Resource data/content
F1A IRI for a registered identifier scheme for your resource’s IRI PURL schema PURL schema
http://purlz.org http://purlz.org
F1B IRI to a document describing the persistency policy for the identifier of this data http://purlz.org http://purlz.org
https://lod.proconsortium.org/rdf.html#uri
F2 IRI for machine-readable metadata for the resource https://lod.proconsortium.org/releases/latest/void.ttl https://lod.proconsortium.org/PR_000025934
IRI to file format for this metadata https://www.w3.org/TR/void/ https://www.w3.org/TR/void/
F3 Is the resource identifier specified in the metadata? Yes Yes
F4 URL to a search engine indexing your resource https://www.google.com https://www.google.com
Search query/terms “Protein Ontology” - > First hit “PR_000025934” - > First hit
A1.1 URL to the description of the protocol HTTP HTTP
https://en.wikipedia.org/wiki/Hypertext_Transfer_Protocol https://ecciki/Hypertext_Transfer_Protocol
Is the protocol open? Yes Yes
Is the protocol (royalty) free? Yes Yes
A1.2 Is authorization required to access the content of your resource? No No
A2 URL to metadata longevity plan https://lod.proconsortium.org/release.html Provided at the dataset level
I1 URL to a specification language RDFS and OWL ontology RDFS and OWL ontology
https://www.w3.org/TR/rdf-schema/ https://www.w3.org/TR/rdf-schema/
https://www.w3.org/TR/owl2-overview/ https://www.w3.org/TR/owl2-overview/
I2 Maximum 3 IRIs for vocabularies used within the (meta)data http://purl.org/dc/terms/ dcterms:title “PRO Linked Open Data”@en
http://www.w3.org/ns/dcat# dcat:keyword “Protein Ontology”^^xsd:string, “Linked Open Data”^^xsd:string
http://www.w3.org/2004/02/skos/core# void:linkPredicate skos:closeMatch
I3 URL to a LinkSet (https://www.w3.org/TR/void/) for the resource https://lod.proconsortium.org/releases/latest/void.ttl Provided at the dataset level
R1.1 URL to license/terms of use for the resource https://creativecommons.org/licenses/by/4.0/
R1.2 Maximum 3 IRIs used to describe the provenance of the resource http://purl.org/dc/terms/ dcterms:accrualPeriodicity freq:quarterly
http://xmlns.com/foaf/0.1/ dcterms:publisher [foaf:page]
http://purl.org/pav/ pav:hasCurrentVersion:prolod59_0
Maximum 3 IRIs used to describe domain information http://purl.obolibrary.org/obo/pr# oboInOwl:hasSynonymType pr:PRO-short-label
http://purl.obolibrary.org/obo/ obo:PR_Q7TMZ5
http://www.geneontology.org/formats/oboInOwl# oboInOwl:hasSynonymType pr:PRO-short-label
R1.3 IRI that represents certification from a recognized authority http://yummydata.org Provided at the dataset level
Open in a new tab

Nonetheless, there are still some improvements to be made, and these will be addressed in the future work. For example, we plan to improve PRO entry pages to support content negotiation with structured data, such as JSON-LD to provide explicit clues about the meaning of the PRO term to search engines.

PRO has a user-friendly web interface that supports search, browse and retrieval of PRO terms and related information at https://proconsortium.org/. The PRO website also provides a query interface to the SPARQL endpoint, and new functionality using Linked Open Data is being developed. Web statistics show an increasing proportion of accesses to the SPARQL query interface over text searches, indicating the trends towards using the SPARQL server and programmatic access.

Methods

Data imported into PRO comes largely from well-known curated sources (for example, UniProtKB and Reactome) that are updated with each release through a combination of manual and automated means. Special attention is given to terms for which positional information is cited. Upon import, and regularly thereafter, such positions are verified against the current protein sequence (for example, to ensure that a named amino acid is at the indicated position on the sequence). Data integrity and consistency of ontology is checked prior to each release using the ELK reasoner31, a very fast reasoner that supports the EL subset of OWL 2 as part of the ROBOT tool32.

Users can query PRO Linked Open Data using SPARQL at https://sparql.proconsortium.org/virtuoso/sparql, which is powered by OpenLink Virtuoso server community edition (http://vos.openlinksw.com/owiki/wiki/VOS) (version 07.20.3217) with the faceted browser, SPARQL 1.1 query federation and Cross-Origin Resource Sharing (CORS) enabled to support a range of complex and federated queries that merge data from other SPARQL endpoints.

Faceted browser

Protein Ontology Linked Open Dataset can be accessed via Virtuoso Faceted Browser (Fig. 4), a general-purpose RDF data query facility for data exploration by faceted browsing over entity relationship types (i.e. relations).

Fig. 4.

Fig. 4

Open in a new tab

Virtuoso faceted browser query interface and result table view.

SPARQL GUI

Protein Ontology Linked Open Data can be accessed via YASGUI (Yet Another Sparql GUI), a web application to query any SPARQL endpoint. YASGUI provides various advanced features for creating, sharing, and visualizing SPARQL queries and their results. We also provided a comprehensive set of example SPARQL queries (Fig. 5).

Fig. 5.

Fig. 5

Open in a new tab

PRO LOD SPARQL GUI. It provides users with a portal to query Protein Ontology Linked Open Data using the SPARQL 1.1 standards as well as a comprehensive set of example queries.

RESTful APIs

As a full query language, SPARQL can be difficult for some people to learn. We therefore developed RESTful APIs (Fig. 6) for programmatic access to Protein Ontology Linked Open Data for data integration or analysis. The API specification was designed using the Swagger™ Editor based on OpenAPI Specification 3. Swagger UI was used to visualize and interact with the API’s resources automatically generated from API specifications. The PRO APIs include 8 API operation groups and 34 access paths as shown in Table 3 and are implemented using the Django-REST framework.

Fig. 6.

Fig. 6

Open in a new tab

API documentation for Protein Ontology Linked Open Data. The Swagger™ API generates an interactive webpage where users can ‘try out’ the service with real queries. Results are returned in the ‘Response Body’ in the user selected response format (JSON illustrated) or XML.

Table 3.

Currently supported Protein Ontology RESTful API endpoints.

API Operation Group API Access Path* Description
PRO Terms /pros Search PRO terms.
/pros/{proIds} Return PRO terms by IDs.
Proteoform Terms /proforms/modification Returns a list of modified protein forms.
/proforms/modification/phosphorylated Returns a list of phosphorylated protein forms.
/proforms/modification/methylated Returns a list of methylated protein forms.
/proforms/modification/acetylated Returns a list of acetylated protein forms.
/proforms/modification/ubiquitinated Returns a list of ubiquitinated protein forms.
/proforms/modification/glycosylated Returns a list of glycosylated protein forms.
/proforms/orthoisoform Returns a list of ortho-isoform protein forms.
/proforms/orthomodform Returns a list of ortho-modform protein forms.
/proforms/sequence Returns a list of sequence level protein forms.
/proforms/organism-sequence Returns a list of organism-sequence level protein forms.
Protein Evolutionary Terms /proevos/family Returns a list of family level protein terms.
/proevos/gene Returns a list of gene level protein terms.
/proevos/organism-gene Returns a list of organism-gene level protein terms.
Protein Complex Terms /procomps/organism Returns a list of organism specific protein complex terms.
/procomps Returns a list of organism non-specific protein complex terms.
Database Cross-references /dbxrefs/EcoCyc_ID Returns a list of PRO terms with EcoCyC ID as cross-reference.
/dbxrefs/HGNC_ID Returns a list of PRO terms with HGNC ID as cross-reference.
/dbxrefs/MGI_ID Returns a list of PRO terms with MGI ID as cross-reference.
/dbxrefs/Ontology_ID Returns a list of PRO terms with Ontology ID as cross-reference.
/dbxrefs/PANTHER_ID Returns a list of PRO terms with PANTHER ID as cross-reference.
/dbxrefs/PIRSF_ID Returns a list of PRO terms with PIRSF ID as cross-reference.
/dbxrefs/PMID Returns a list of PRO terms with PMID as cross-reference.
/dbxrefs/Reactome_ID Returns a list of PRO terms with Reactome ID as cross-reference.
/dbxrefs/NCBITaxon_ID Returns a list of PRO terms with NCBI Taxon ID as cross-reference.
/dbxrefs/UniProtKB_ID Returns a list of PRO terms with UniProtKB ID as cross-reference.
PRO Annotation File /paf/{proIds} Returns annotations for the given PRO ID(s).
OBO File /obo/{proIds} Returns PRO term in OBO format for the given PRO ID(s).
DAG /dag/parent/{proIds} Returns direct parent PRO terms by the given PRO ID(s).
/dag/ancestor/{proIds} Returns direct and indirect parent PRO terms by the given PRO ID(s).
/dag/children/{proIds} Returns direct children PRO terms by the given PRO ID(s).
/dag/descendant/{proIds} Returns direct and indirect children PRO terms by the given PRO ID(s).
/dag/hierarchy/{proId} Returns hierarchy of PRO terms by the given PRO ID.
Open in a new tab

*After “https://research.bioinformatics.udel.edu/PRO_API/V1”.

Users can use the API web interface to interactively customize the API requests. The API web pages also provide users with dynamically generated source code (client libraries) for common scripting and programming languages that provide examples with their query options and filters fully integrated that can be used as standalone scripts or programs or integrated into users’ large projects.

RDF dumps

RDF dumps of Protein Ontology Linked Open Data are available for download in application/rdf + xml or text/turtle formats with corresponding RDF centric statistics, as shown in Table 4. Each dataset is loaded into a named graph in the Virtuoso triple store. A set of six RDF centric metrics are computed for each dataset that summarize their contents:

  • total number of triples

  • total number of distinct classes (the number of distinct resources occurring as objects of rdf:type)

  • total number of entities

  • total number of distinct subject nodes

  • total number of distinct predicates

  • total number of distinct object nodes

Table 4.

RDF centric statistics for Protein Ontology Linked Open Dataset (release 61.0).

Dataset Named Graph Triples Classes Entities Subjects Predicates Objects
pro http://purl.obolibrary.org/obo/pr 11,858,7202 8 1,996,180 2,285,489 48 3,1298,222
paf http://pir.georgetown.edu/pro/paf 91,407 4 8,313 19,733 22 31,301
pro-ensembl-closeMatch-linkset https://lod.proconsortium.org/ensembl-closeMatch 48 0 0 24 2 24
pro-ensemblbacteria-closeMatch-linkset https://lod.proconsortium.org/ensemblbacteria-closeMatch 1,758 0 0 493 2 879
pro-hgnc-closeMatch-linkset https://lod.proconsortium.org/hgnc-closeMatch 36,542 0 0 18,268 2 18,271
pro-mgi-closeMatch-linkset https://lod.proconsortium.org/mgi-closeMatch 31,024 0 0 15,509 2 15,512
pro-ncbigene-closeMatch-linkset https://lod.proconsortium.org/ncbigene-closeMatch 5,310 0 0 1,693 2 2,655
pro-reactome-closeMatch-linkset https://lod.proconsortium.org/reactome-closeMatch 29,042 0 0 10,169 2 14,521
pro-reactome-exactMatch-linkset https://lod.proconsortium.org/reactome-exactMatch 43,998 0 0 11,220 3 14,666
pro-rgd-closeMatch-linkset https://lod.proconsortium.org/rgd-closeMatch 14,690 0 0 7,343 2 7,345
pro-sgd-closeMatch-linkset https://lod.proconsortium.org/sgd-closeMatch 2,522 0 0 1,261 2 1,261
pro-uniprotkb-closeMatch-linkset https://lod.proconsortium.org/uniprotkb-closeMatch 274,308 0 0 54,960 2 133,016
pro-uniprotkb-exactMatch-linkset https://lod.proconsortium.org/uniprotkb-exactMatch 527,418 0 0 175,793 3 169,975
pro-uniprotkbvar-closeMatch-linkset https://lod.proconsortium.org/uniprotkbvar-closeMatch 938 0 0 41 2 469
pro-uniprotkbvar-exactMatch-linkset https://lod.proconsortium.org/uniprotkbvar-exactMatch 1,407 0 0 469 3 469
pro-wormbase-closeMatch-linkset https://lod.proconsortium.org/wormbase-closeMatch 3,570 0 0 1,785 2 1,785
void https://sparql.proconsortium.org/.well-known/void 2,326 7 74 292 63 606
Open in a new tab

SPARQL queries used to calculate statistics can be found at https://code.google.com/archive/p/void-impl/wikis/SPARQLQueriesForStatistics.wiki.

The triples in “exactMatch-linkset” use “skos:exactMatch” as linkPredicate. The triples in “closeMatch-linkset” use “skos:closeMatch” as linkPredicate.

Acknowledgements

The authors thank our collaborators from Protein Ontology Consortium. The funding is provided by the National Institute of General Medical Sciences [R01GM080646].

Author contributions

Author contributions are as follows: Writing [C.C., H.H., K.R., J.C., C.A., C.W. and D.N.]; curation [K.R., D.N. and C.A.]; programming [C.C., H.H. and J.C.]; discussion [C.C., H.H., K.R., J.C., C.A., C.W. and D.N.].

Data availability

RDF dumps can be downloaded at https://lod.proconsortium.org/release.html. Virtuoso faceted browser can be accessed at https://sparql.proconsortium.org/virtuoso/fct/. RESTful APIs can be accessed at https://lod.proconsortium.org/api.html. SPARQL GUI can be accessed at https://lod.proconsortium.org/yasgui.html. The formal FAIRness assessment results can be accessed at https://lod.proconsortium.org/fair.html.

Code availability

The source code that implements https://lod.proconsortium.org/ website is available from GitHub at https://github.com/PROconsortium/PRoteinOntology/tree/master/pro_lod

The source code that implements PRO RESTful APIs is available from GitHub at https://github.com/PROconsortium/PRoteinOntology/tree/master/proapi

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Berners-Lee, T. Linked Data, https://www.w3.org/DesignIssues/LinkedData.html (2006).
  • 2.Callahan, A. et al. Bio2RDF release 2: improved coverage, interoperability and provenance of life science linked data. In: Cimiano P., Corcho O., Presutti V., Hollink L., Rudolph S. (eds) The Semantic Web: Semantics and Big Data. ESWC 2013. Lecture Notes in Computer Science. 7882, 200-212 (Springer, Berlin, Heidelberg, 2013).
  • 3.Bult J, et al. Mouse genome database (MGD) Nucleic Acids Res. 2019;47:D801–D806. doi: 10.1093/nar/gky1056. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Cherry M, et al. Saccharomyces genome database: the genomics resource of budding yeast. Nucleic Acids Res. 2012;40:D700–705. doi: 10.1093/nar/gkr1029. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Smith R, et al. The year of the rat: the rat genome database at 20: a multi-species knowledgebase and analysis platform. Nucleic Acids Res. 2020;48:D731–D742. doi: 10.1093/nar/gkaa239. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.National Center for Biotechnology Information. Gene https://www.ncbi.nlm.nih.gov/gene/ (2020).
  • 7.Yates B, et al. Genenames.org: the hgnc and vgnc resources in 2017. Nucleic Acids Res. 2017;45:D619–D625. doi: 10.1093/nar/gkw1033. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Todd W, et al. WormBase: a modern model organism information resource. Nucleic Acids Res. 2020;48:D762–D767. doi: 10.1093/nar/gkz920. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Jupp S, et al. The ebi rdf platform: linked open data for the life sciences. Bioinformatics. 2014;30:1338–1339. doi: 10.1093/bioinformatics/btt765. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.The UniProt Consortium UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res. 2019;47:D506–D515. doi: 10.1093/nar/gky1049. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Jassal B, et al. The reactome pathway knowledgebase. Nucleic Acids Res. 2020;48:D498–D503. doi: 10.1093/nar/gkz1031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Yates, A. et al. Ensembl 2020. Nucleic Acids Res. 48, D682–D688 (2020). [DOI] [PMC free article] [PubMed]
  • 13.The Gene Ontology Consortium The gene ontology resource: 20 years and still going strong. Nucleic Acids Res. 2019;47:D330–D338. doi: 10.1093/nar/gky1055. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Natale D, et al. Protein ontology (PRO): enhancing and scaling up the representation of protein entities. Nucleic Acids Res. 2017;45:D339–D346. doi: 10.1093/nar/gkw1075. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Neal L, et al. Harmonizing semantic annotations for computational models in biology. Brief Bioinform. 2019;20:540–550. doi: 10.1093/bib/bby087. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Wang D, et al. AAgAtlas 1.0: a human autoantigen database. Nucleic Acids Res. 2017;45:D769–D776. doi: 10.1093/nar/gkw946. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Bada M, et al. Concept annotation in the craft corpus. BMC Bioinformatics. 2012;13:161. doi: 10.1186/1471-2105-13-161. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Pyysalo S, et al. LION lbd: a literature-based discovery system for cancer biology. Bioinformatics. 2019;35:1553–1561. doi: 10.1093/bioinformatics/bty845. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Overton A, et al. Reporting and connecting cell type names and gating definitions through ontologies. BMC Bioinformatics. 2019;20:182. doi: 10.1186/s12859-019-2725-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.He Y, et al. OSCI: standardized stem cell ontology representation and use cases for stem cell investigation. BMC Bioinformatics. 2019;20:180. doi: 10.1186/s12859-019-2723-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Lin Y, Xiang Z, He Y. Ontology-based representation and analysis of host-brucella interactions. J. Biomed. Semantics. 2015;6:37. doi: 10.1186/s13326-015-0036-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Zeinab F, et al. Murine oviductosomes (ovs) microrna profiling during the estrous cycle: delivery of ovs-borne micrornas to sperm where mir-34c-5p localizes at the centrosome. Sci. Rep. 2018;8:16094. doi: 10.1038/s41598-018-34409-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Huang L, et al. Integrative annotation and knowledge discovery of kinase post-translational modifications and cancer-associated mutations through federated protein ontologies and resources. Sci. Rep. 2018;8:6518. doi: 10.1038/s41598-018-24457-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Yamamoto Y, Yamaguchi A, Splendiani A. YummyData: providing high-quality open life science data. Database. 2018;2018:bay022. doi: 10.1093/database/bay022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Bult C, et al. The representation of protein complexes in the protein ontology (pro) BMC Bioinformatics. 2011;12:371. doi: 10.1186/1471-2105-12-371. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.W3C Interest Group. Dataset descriptions: hcls community profile. http://www.w3.org/TR/2015/NOTE-hcls-dataset-20150514/ (2015).
  • 27.Piñero J, et al. The disgenet knowledge platform for disease genomics: 2019 update. Nucleic Acids Res. 2020;48:D845–D855. doi: 10.1093/nar/gkz1021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Wilkinson M, et al. The fair guiding principles for scientific data management and stewardship. Sci. Data. 2016;3:160018. doi: 10.1038/sdata.2016.18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Wilkinson M, et al. A design framework and exemplar metrics for fairness. Sci. Data. 2018;5:180118. doi: 10.1038/sdata.2018.118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Garcia L, et al. FAIR adoption, assessment and challenges at UniProt. Sci. Data. 2019;6:175. doi: 10.1038/s41597-019-0180-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Kazakov Y, Krötzsch M, Simančík F. The Incredible elk - from polynomial procedures to efficient reasoning with el ontologies. Journal of Automated Reasoning. 2014;1:1–61. doi: 10.1007/s10817-013-9296-3. [DOI] [Google Scholar]
  • 32.Jackson R, et al. ROBOT: a tool for automating ontology workflows. BMC Bioinformatics. 2019;1:407. doi: 10.1186/s12859-019-3002-3. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

RDF dumps can be downloaded at https://lod.proconsortium.org/release.html. Virtuoso faceted browser can be accessed at https://sparql.proconsortium.org/virtuoso/fct/. RESTful APIs can be accessed at https://lod.proconsortium.org/api.html. SPARQL GUI can be accessed at https://lod.proconsortium.org/yasgui.html. The formal FAIRness assessment results can be accessed at https://lod.proconsortium.org/fair.html.

The source code that implements https://lod.proconsortium.org/ website is available from GitHub at https://github.com/PROconsortium/PRoteinOntology/tree/master/pro_lod

The source code that implements PRO RESTful APIs is available from GitHub at https://github.com/PROconsortium/PRoteinOntology/tree/master/proapi

Articles from Scientific Data are provided here courtesy of Nature Publishing Group

RESOURCES