# BioPAX in 2024: Where we are and where we are heading

Cécile Beust, Emmanuelle Becker, Nathalie Théret, Olivier Dameron

► To cite this version:

Cécile Beust, Emmanuelle Becker, Nathalie Théret, Olivier Dameron. BioPAX in 2024: Where we are and where we are heading. Computational and Structural Biotechnology Journal, 2024, 23, pp.3999-4010. 10.1016/j.csbj.2024.10.045. hal-04801710

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## Review Article

# BioPAX in 2024: Where we are and where we are heading

Cécile Beust<sup>a</sup>, Emmanuelle Becker<sup>a</sup>, Nathalie Théret<sup>a,b</sup>, Olivier Dameron<sup>a,*</sup>

<figure><img src="image_3.png" /></figure>

## ARTICLE INFO

Dataset link: https://github.com/CecileBeust/BioPAXReview2024Codes.git

**Keywords:**

Systems Biology

BioPAX

Biological pathways

Databases

## ABSTRACT

In systems biology, the study of biological pathways plays a central role in understanding the complexity of biological systems. The massification of pathway data made available by numerous online databases in recent years has given rise to an important need for standardization of this data. The BioPAX format (Biological Pathway Exchange) emerged in 2010 as a solution for standardizing and exchanging pathway data across databases. BioPAX is a Semantic Web format associated to an ontology. It is highly expressive, allowing to finely describe biological pathways at the molecular and cellular levels, but the associated intrinsic complexity may be an obstacle to its widespread adoption.

Here, we report on the use of the BioPAX format in 2024. We compare how the different pathway databases use BioPAX to standardize their data and point out possible avenues for improvement to make full use of its potential. We also report on the various tools and software that have been developed to work with BioPAX data. Finally, we present a new concept of abstraction on BioPAX graphs that would allow to specifically target areas in a BioPAX graph needed for a specific analysis, thus differentiating the format suited for representation and the abstraction suited for contextual analysis.

## 1. Introduction

Biological information encompassing data and knowledge is nowadays accumulating thanks to refinement of experimental methods in biology. This opens great possibilities for gaining new understandings of biological phenomenons. This information needs to be stored and shared online through databases, that could either represent general biological knowledge, like the Reactome pathway database (Jassal et al. [1], Miclicic et al. [2]) or specific to some biological entities, like UniProtKB (Pundir et al. [3], The UniProt Consortium [4]) for proteins or ChEBI (Hastings et al. [5]) for small molecules. The large number of biological databases resulted in fragmented biological data across sources, and their schema heterogeneity hampers their joint analyses. The need of standards to represent biological knowledge thus emerged with data massification.

The COMBINE initiative (COmputational Modeling in Biology Network) (Hucka et al. [6]) started in 2015 to harmonize efforts in systems biology. COMBINE is a common project proposing and coordinating several pre-existing standards to represent and model biological data according to specific goals. Modeling is covered by SBML (Systems Biology Markup Language) (Keating et al. [7], Lahti [8]). Graphical representation is covered by SBGN (Systems Biology Graphical Notation)

(Le Novère et al. [9]). Exchange and interoperability is covered by BioPAX (Biological Pathway Exchange) (Demir et al. [10]), a standard language designed for the representation of biological pathways at the molecular and cellular levels. Other specific standards exist in the COMBINE initiative such as CellML (Cell Markup Language), (Cuellar et al. [11], Miller et al. [12]) for mathematical models or SBOL (Synthetic Biology Open Language) (Galdzicki et al. [13], Baig et al. [14]) for synthetic biology.

Pathways are a key notion in systems biology. A pathway is defined as a series of interactions between molecules that can be physical or genetic cellular components, describing a cause-and-effect or a time-dependant process, explaining an observable biological phenomenon (Demir et al. [10]). There exist several biological pathway databases but they do not share a common data format. This results in technical challenges concerning the standardization and integration of pathway data from heterogeneous sources and the development of methods and tools to help scientist access, map and analyze pathway information.

The BioPAX format aims to address several needs related to these challenges by making pathway data easier to collect, interpret and share. It allows the representation of pathway data using a controlled vocabulary to describe the pathways, their biochemical reactions, the

<footnote><sup>a</sup> Univ Rennes, Inria, CNRS, IRISA - UMR 6074, Rennes, F-35000, France</footnote>

<footnote><sup>b</sup> Univ Rennes, Inserm, EHESP, Irset, UMR S1085, Rennes, F-35000, France</footnote>

<footnote>* Corresponding author.
E-mail address: olivier.dameron@univ-rennes.fr (O. Dameron).</footnote>
molecules involved as well as regulation processes. It is designed to
enable the integration of heterogeneous pathway data coming from mul-
tiple sources and their efficient sharing and interoperability. Moreover,
BioPAX facilitates the storage and automatic querying of pathway data
within databases, acting as a common interface for software tools to ac-
cess pathway data from multiple sources (Demir et al. [15]). BioPAX
emerged in 2002 with a first level supporting only metabolic pathway
interactions. In 2005, the level 2 added support for molecular interac-
tions and post-translational protein modifications. Since 2010, the level
3 also supports signaling pathways, molecular state, gene regulation and
genetic interactions.

Here we report on the use of BioPAX across pathway databases and
list some potential technical, organizational and conceptual issues that
may limit its adoption by the bioinformatician or biologist communities,
and suggest some areas of improvement to facilitate and extend the use
of the BioPAX format. We first give an illustrated overview of the BioPAX
format and examples of pathway represented in BioPAX. Then we com-
pare how BioPAX is used across the main pathway databases and assess
how these databases use BioPAX to cross-link and make their data in-
teroperable. We also present the various tools that have been developed
to work with BioPAX data. Finally, we propose an abstraction strategy
to differentiate the data representation formalism and the data analysis
formalism using BioPAX, paving the way to future possible applications
of BioPAX data through the use of graph abstractions.

## 2. An illustrated overview of the BioPAX pathway data language

BioPAX is a standard language designed for the representation of
metabolic, signaling pathways, molecular and genetic interaction as well
as gene regulation networks (Demir et al. [10]). It focuses on exchanging
and integrating large biological process maps (Hucka et al. [6]). BioPAX
was developed and is maintained by a multi-disciplinary scientific com-
munity including pathway database groups (data providers and users)
and tool developers. It evolves in the form of levels, each level refining
the format or enriching it with new functionalities. The current level of
BioPAX is the level 3 created in 2010. The BioPAX material and docu-
mentation is open source and accessible at http://www.biopax.org/.

Technically, BioPAX is a data format based on RDF (Resource De-
scription Framework) and OWL (Ontology Web Language), Semantic
Web formats (Bizer et al. [16]) supporting data interoperability and al-
lowing to seamlessly take knowledge into account during processing.
Entities and relationships from pathway data described in BioPAX are
represented in the form of RDF triples with a subject (from which the
relation starts), a relation (a predicate) and an object (to which the rela-
tion points to). BioPAX data can thus be represented as directed graphs,
that can be automatically queried using the SPARQL language (Cheung
et al. [17]).

BioPAX comes with an ontology providing a controlled vocabulary
and a hierarchical system of classes and properties. Entities are instances
of some BioPAX ontology classes, and the relations between entities are
represented by BioPAX ontology properties.

Fig. 1 represents a simplified diagram of the BioPAX ontology. It
presents the main classes of the BioPAX ontology as well as the relation-
ships (properties) that the instances of the classes can share to express
their interactions. The *Pathway* class is used to describe among others
the components of the pathways and their sequence of steps. The *In-
teraction* class allows to specify the nature of interactions that the
entities share and allows to describe them according to their types (for
example biochemical reactions, catalysis, molecular interactions...). The
*PhysicalEntity* class is used to describe entities, including the spec-
ification of their nature (proteins, small molecules, complexes, RNA or
DNA). Other classes exist in the BioPAX ontology but are not represented
on Fig. 1, like the *Xref* class which allows to link external references
to the entities (Pubmed identifiers of publications, cross-links to other
databases, identifiers...). The complete diagram of the BioPAX ontology
is provided as Supplementary Figure S-1.

For a better understanding, an illustration of use of the classes and
properties of the BioPAX ontology with a pathway example is given in
Fig. 2 (panel A), where we present the instantiation of a pathway from
Reactome (Jassal et al. [1]) ('Formation of RNA pol II elongation com-
plex', R-HSA-112382). The pathway is represented as a directed graph
describing the interactions of the pathway components, their types and
their associated biochemical reactions. The nodes representing enti-
ties are drawn as ellipses and nodes representing BioPAX classes are
drawn as boxes. Node colors match those of the ontology presented in
Fig. 1. The ellipse corresponding to the pathway of interest is repre-
sented in red, it is an instance of the *Pathway* class. The *bp3:path-
wayOrder* property is used to specify the composition of the pathway
into pathway steps (in purple ellipses, instances of the *PathwayStep*
class). The *bp3:nextStep* property specifies the sequence organiza-
tion of the pathway steps. Each pathway step is associated to one or
more interactions (green ellipses, instances of sub-classes of *Inter-
action*) by the *bp3:stepProcess* property. Typically, it allows to
group a biochemical reaction and its associated catalysis as a pathway
step. Here for the sake of representation, we only detailed the interac-
tions linked to the *PathwayStep10617* and *PathwayStep10618*. The
other instances of *PathwayStep* are represented to show the intercon-
nections of pathways in Reactome. Here in the *PathwayStep10617*,
the *Catalysis3256* interaction activates the *BiochemicalReac-
tion8726* via the *bp3:controlled* property. Each interaction in the
graph (green ellipses) is linked to entities that can be the reactants (prop-
erty *bp3:left*), products (property *bp3:right*) or the controllers
(property *bp3:controller*) of a biochemical reaction. These enti-
ties can be any type of instance of the *PhysicalEntity* class of the
BioPAX ontology. Here some complexes being part of the reactions are
detailed (with blue ellipses). The proteins composing these complexes
are specified with the *bp3:component* property. All the entities and re-
lationships visualized on the BioPAX graph are encoded as RDF triples
such as illustrated on the panel B of Fig. 2. The complete list of RDF
triples corresponding to the pathway is given in Supplementary Figure
S-2. The pathway was extracted from the BioPAX export of Reactome
(version 90, September 2024), using a SPARQL query provided as Sup-
plementary Figure S-3.

The BioPAX ontology thus provides a controlled vocabulary, and
allows to finely describe, in a standard way, the mechanisms con-
necting the multiple elements involved in biological processes. BioPAX
data can be queried using the SPARQL query language, in order to
retrieve specific triples, specific entities (nodes) or specific relation-
ships (edges) from the graph. The explicit and fine-grain representa-
tion of information supports highly-expressive queries and powerful
analyses, but can quickly become complex as the pathways grow in
size.

## 3. BioPAX as a common but flexible pathway language

The goal of the BioPAX format is to describe biological pathway data
in a uniform way. To do so, BioPAX comes with a controlled vocabu-
lary and semantics, establishing a standard way to represent pathway
information. However, this standardization effort can only be fully ef-
fective if its flexibility is limited enough so that the vocabulary is used
uniformly by the different providers.

To evaluate the uniformity in the production of BioPAX, we com-
pared the BioPAX exports of five main pathway databases: Reactome
(Jassal et al. [1]), PANTHER Pathway (Mi and Thomas [18]), Thomas
et al. [19]), PathBank (Wishart et al. [20], Wishart et al. [21]),
HumanCyc (Romero et al. [22]) and KEGG Pathway (Kanehisa and
Goto [23], Kanehisa et al. [24]). Except for the standalone BioPAX ex-
port of Reactome (version 90, September 2024) and the standalone
export of PANTHER Pathway, all the BioPAX files of pathway databases
were downloaded from the PathwayCommons aggregation service
(PathwayCommons version 14, 2024), (Rodchenkov et al. [25]). Path-
wayCommons is part of the standardization effort of pathway data
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Simplified BioPAX data schema adapted from Demir et al. [10]. The hierarchy of the main classes of the BioPAX ontology is represented as color boxes. The main properties associated to each class are detailed at the bottom of the boxes. The types of resources properties point to are indicated next to each of them. The properties originating from a class are either class-specific or inherited from the ancestor classes. The *Entity* class (in gray) is the root of the BioPAX ontology. *PhysicalEntity* (in blue) describes the biological entities involved in pathways such as proteins, complexes, small chemical molecules or even RNA or DNA. *Interaction* (in green) encompasses several types of biological interactions including reactions as well as regulation processes that typically involve one or several physical entities. *Pathway* (in red) is used to characterize biological pathways, including the reactions and pathway steps (the sequence of reactions) composing them.</figcaption></figure>

and aims to provide a platform for sharing pathway data files in a standardized format including BioPAX, PSI-MI (Kerrien and Hermjakob [26]) and SBML (Hucka et al. [27]). Because human is the only organism supported by all resources, our study compared human BioPAX exports. For the standalone export of PANTHER Pathway, which integrates information about protein orthologs, we filtered it in order to only retrieve information about human proteins in pathways.

Counts were done using SPARQL queries, that are available in a git repository (https://github.com/CecileBeust/BioPAXReview2024Codes). A data acquisition procedure description is available as Supplementary Text S-1.

For each pathway database, we represented the proportion of instances of eight classes from the BioPAX ontology on radar plots. We normalized the counts of instances by the maximal values of each class. This comparison highlights a high heterogeneity in the BioPAX exports of pathway information across databases, both in terms of content or representation. The scope of PANTHER Pathway (standalone BioPAX export and PathwayCommons export), HumanCyc, and KEGG Pathway seem to focus more on small molecules than on other instances types. PathBank has a more balanced focus between small molecules, interactions, pathways, pathway steps and biochemical reactions. Overall, Reactome presents more instances of biochemical reactions and physical entities

(Rna, Dna and proteins) than every other databases. These differences in usage of BioPAX classes between pathway databases can be explained by the differences of scope between the databases. Indeed, the HumanCyc and KEGG Pathway are more focused on metabolism, that could explain why we observe more instances of *SmallMolecule*. Strikingly Reactome and PathBank are the only resources using the *PathwayStep* class to describe a unit composing a pathway that can belong to several pathways, interconnecting pathway graphs. Other pathway databases make little or no use of the *PathwayStep* class, but represent a pathway as an independent unit of information, in which the components are not described as sequential reactions. This observation suggests that pathway databases are more or less detailed, leading to a different low-level representation. Reactome is thus the most comprehensive and connected map of biological processes.

However, these results should not be generalized to the original resources before their export to BioPAX. First of all, the BioPAX files from the PathwayCommons database used in this study are not all up-to-date with the current versions of the databases. Only Reactome and PANTHER Pathway provide regular BioPAX exports independently from the PathwayCommons database (Reactome provides updated BioPAX exports every three months). The conversion tools used to convert databases to BioPAX are not mentioned on the PathwayCommons website. Another important point is that we can not distinguish between
<figure><img src="image_5.png" /><figcaption>B) RDF triples corresponding to the BioPAX representation of the pathway</figcaption></figure>

```
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdf: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX bp3: <http://www.biopax.org/release/biopax-level3.owl#>
PREFIX reactome: <http://www reactome.org/biopax/77/48887#>

reactome:Pathway1891 rdf:type bp3:Pathway.
reactome:Pathway1891 bp3: displayName "Formation of RNA Pol II elongation complex".
reactome:Pathway1891 bp3: pathwayOrder reactome:PathwayStep10617.
reactome:Pathway1891 bp3: pathwayOrder reactome:PathwayStep10618.
reactome:Pathway1891 bp3: pathwayComponent reactome:BiochemicalReaction8726.
reactome:Pathway1891 bp3: pathwayComponent reactome:BiochemicalReaction8727.

reactome:PathwayStep10615 bp3:nextStep reactome:PathwayStep10617.
reactome:PathwayStep10617 bp3:nextStep reactome:PathwayStep10618.
reactome:PathwayStep10618 bp3:nextStep reactome:PathwayStep10620.
reactome:PathwayStep10615 rdf:type bp3:PathwayStep.
...
```

**Fig. 2.** Example of a pathway represented in BioPAX. (Panel A) The pathway represented is the 'Formation of the RNA Pol II elongation complex' pathway (R-HSA-112382) from the Reactome database (version 90, September 2024). It is described using the classes and properties of the BioPAX ontology. Each arrow represents a relation (also called a predicate) from a subject to an object, the whole making up a RDF triple (Panel B, see Supplementary File S-2 for the complete list of RDF triples). Resources are represented with their identifiers in the BioPAX export of Reactome (version 87). Instances are represented by ellipses and classes by boxes (node color legend from Fig. 1). The instantiation relationships describing the membership of resources to a class are represented by red arrows. Instantiation relationships were omitted for the *PhysicalEntity* instances. The pathway of interest (in red) is composed of two direct pathway steps (in purple), linked by a *bp3:nextStep* relationship indicating the sequence of the steps. The first pathway step (*PathwayStep10617*) is composed of the *BiochemicalReaction8726* ('Hyperphosphorylation (Ser2) of RNA Pol II CTD by P-TEFb complex') which consumes the *Complex5463* (via the *bp3:left* relation) and produces the *Complex10863* (via the *bp3:right* relation). *BiochemicalReaction8726* is controlled (activated) by the *Catalysis3256* reaction. The second pathway step (*PathwayStep10618*) is composed of the *BiochemicalReaction8727* ('Recruitment of elongation factors to form elongation complex') which consumes the product of the previous pathway step and produces the *Complex10864*.

information that are not present in the original resources before conversion to BioPAX and information that is lost during conversion. We can distinguish three different types of information that can be lost during the conversion to BioPAX: the information that could have been translated to BioPAX but was not present in the original database, the information that could have been translated to BioPAX but was not because of the converter, and the information that could not have been translated to BioPAX because of a specific type of information not supported by BioPAX. Keeping in mind that the differences in BioPAX classes' usage observed in Fig. 3 can be due to the differences in scope from the databases, these BioPAX exports are the only ones available for these different databases and their comparison remains relevant.

## 4. BioPAX as a language for pathway data exchange and interoperability

One of the goals of BioPAX is to make pathway data both reusable and extendable by external resources. Several features of BioPAX based on its RDF syntax address these requirements.

BioPAX graphs can be cross-linked with other databases thanks to the universal nature of RDF identifiers. Any dataset that refers to a BioPAX pathway or one of its components simply has to reuse its identifier, for example to represent the fact that it is involved in a particular disease. Conversely, a BioPAX dataset can refer to third-party resources to provide additional information. Typically, a BioPAX dataset would describe the participation of certain proteins and molecules to a partic-
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Comparison of the contents of BioPAX exports of the main biological pathway databases. The BioPAX exports of five pathway databases were extracted from the PathwayCommons database (Reactome, PANTHER Pathway, PathBank, HumanCyc, and KEGG Pathway) and the standalone BioPAX export of Reactome (version 90, September 2024) and PANTHER Pathway have been added. For each database, the number of instances of each of the following BioPAX class is represented on the radar plots: *BiochemicalReaction*, *PathwayStep*, *Pathway*, *Interaction*, *SmallMolecule*, *Protein*, *Dna*, *Rna*. The values are calculated as percentages of the maximum value of each class. The same comparison was extended to the nine pathway databases available on PathwayCommons is available in Supplementary Figure S-4. For more specific information on each pathway database, a detailed table is available as Supplementary Table S-1.</figcaption></figure>

ular pathway, and reference their identifiers in external authoritative resources such as UniProtKB (Pundir et al. [3], The UniProt Consortium [4]) or ChEBI (Chemical Entities of Biological Interest, Hastings et al. [5]), that respectively provide additional descriptions of proteins and small molecules. Figs. 4 and 5 present the comparison of the entities mappings to UniProtKB and ChEBI respectively. To our knowledge, such comparison of pathway databases mappings has not already been performed.

Examples of BioPAX mappings of a protein to UniProtKB and a small molecule to ChEBI are respectively given in the top panels of Figs. 4 and 5. One key element of these mappings is the use of the *EntityReference* class, which is used to group several entities across different contexts and molecular states. For example, a protein can exist in multiple molecular states (localization, post-translational modification, oligomerization...). Each of them has its own identifier in the BioPAX dataset and they appear as many instances of the *Protein* class. However, they are all associated to the same entity reference with the *bp3:entityReference* property to represent the fact that they are different manifestations of the same protein. The same mechanism exists for small molecules.

BioPAX then allows to provide cross links from entity references to external databases such as UniProtKB, ChEBI or PubMed via the *bp3:xref* property. A mapping of an entity from the BioPAX graph and an external resource is thus specified by a path in the graph that starts from the entity (protein or small molecule), goes through its corresponding entity reference and points to an instance of *UnificationXref* related to a UniProtKB or a ChEBI identifier. The mapping process to UniProtKB proteins for the PANTHER Pathway database is slightly different. Because the PANTHER Pathway database integrates information about protein families and orthologs, the database sets the UniProtKB mappings by passing through the *bp3:memberEntityReference*

properties. A schema explaining the mapping process to UniProtKB in PANTHER Pathway is available as Supplementary Figure S-5.

As shown in the bottom panels of Figs. 4 and 5, we counted the number of proteins that map to a UniProtKB identifier and the number of small molecules that map to a ChEBI identifier for the six different BioPAX exports of pathway databases. When these mappings were correctly set up, we counted the number of unique UniProtKB and ChEBI identifiers. Conversely, when the mappings are not set up, we enumerated the number of entities that do not map to UniProtKB or ChEBI, either because they are not associated to an instance of *entityReference* or because their reference do not map at all to these external resources.

For the mappings to UniProtKB, the number of proteins that are cross-linked to UniProtKB is the highest (28027) for the standalone BioPAX export of Reactome (version 90, September 2024) and is of 27775 for the PathwayCommons export of Reactome. The number of mappings is lower for the other databases (lower than 11000). This can partly be explained by the huge difference between the number of protein entities in Reactome and the other databases. Taken as percentages, the proportion of proteins that map to UniProtKB is higher than 85% for the two versions of Reactome, for HumanCyc and for PathBank (highest score with 96.6% of mappings). This demonstrates an overall good mapping of BioPAX entities to UniProtKB.

For the mappings to ChEBI, we observe that the number of small molecules cross-linked to ChEBI is the highest for PathBank (4586), followed by Reactome (with 3680 mappings in the standalone BioPAX export and 3667 mappings in the PathwayCommons export). The number of mappings is lower for the other databases (lower than 4000). This may be explained by the higher number of small molecules present in the PathBank database and also in Reactome. Taken as percentages, the proportion of small molecules that map to ChEBI is higher than 75% for
<figure><img src="image_7.png" /><figcaption>**Fig. 4. Mappings of BioPAX instances to UniProtKB in the BioPAX exports of the main pathway databases** (Reactome standalone BioPAX export and from PathwayCommons, PANTHER Pathway standalone BioPAX export and from PathwayCommons (filtered for human-only uniprot protein mappings), HumanCyc from PathwayCommons, PathBank from PathwayCommons and KEGG Pathway from PathwayCommons). The top panel represents the mapping of BioPAX proteins to UniProtKB. Each instance of Protein (P) points to a ProteinReference (PR) that can be linked to a UniProtKB identifier. On the bottom panel, the number of mappings from the BioPAX instances of P to UniProtKB is detailed as well as the number of the unique UniProtKB identifiers that the database points to. The number of instances of P that lack association with PR is also reported, as well as the number of P whose PR do not point to a UniProtKB identifier. Comparative analysis for the nine pathway databases available on PathwayCommons is provided as Supplementary Figure S-6.</figcaption></figure>

the two PANTHER Pathway exports, for PathBank and HumanCyc, and is close to 70% for the other databases.

Overall, the proportion of entities that map UniProtKB or ChEBI is greater than the proportion of entities that do not map, highlighting an interconnection of BioPAX pathway data with existing other resources. We noticed that for every database, the number of unique identifiers is lower than the number of mappings. It highlights the granularity of the representation of entities in BioPAX: entities are described individually in different molecular states, but are also gathered with references, which are used to set the mappings. Interestingly, we remark that only the latest BioPAX export of Reactome presents entities that are not associated to an instance of *entityReference*. These proteins that are not associated to a reference in Reactome are converted from *EntitySets*. Reactome uses *EntitySets* to gather physical entities that

function interchangeably in a given situation. For example, the *EntitySet* "Insulin-like growth factors" is not found in UniProtKB, but gathers two proteins (*IGF-1* and *IGF2 (25-91)*), which map to UniProtKB in the BioPAX export.

Overall, for Reactome, PANTHER Pathway, HumanCyc and PathBank, entities described in BioPAX are well mapped to UniProtKB and ChEBI (mappings greater than 70%). This mapping is supported by the use of the *entityReference* and *Xref* classes of the BioPAX ontology. Cross-links between BioPAX entities and UniProtKB and ChEBI strengthen interoperability of resources as these bases act as references for proteins and small molecules.

However, some potential improvements in the mapping process could increase interoperability. The presence of entities that do not map to UniProtKB and ChEBI can be explained by a lack of harmonization
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Mappings of BioPAX instances to ChEBI in the BioPAX exports of the main pathway databases (Reactome standalone BioPAX export and from PathwayCommons, PANTHER Pathway standalone BioPAX export and from PathwayCommons, HumanCyc from PathwayCommons, PathBank from PathwayCommons and KEGG Pathway from PathwayCommons). The top panel represents the mapping of BioPAX small molecules to ChEBI. Each instance of *SmallMolecule* (SM) points to *SmallMoleculeReference* (SMR) that can be linked to a ChEBI identifier. On the bottom panel, the number of mappings from the BioPAX instances of SM to ChEBI is detailed as well as the number of the unique ChEBI identifiers that the database points to. The number of instances of SM that lack association with SMR is also reported, as well as the number of SM whose SMR do not point to a ChEBI identifier. Comparative analysis for the nine pathway databases available on PathwayCommons is provided as Supplementary Figure S-7.</figcaption></figure>

in the use of identifiers across databases, which can lead to incomplete mappings and reduced interoperability. Another lack of uniformity in these mappings is that the character strings referring to UniProtKB and ChEBI in the BioPAX files are not the same from one database to the other. The standalone export of Reactome (version 90, September 2024) uses the strings “UniProt” and “ChEBI” where other databases use the strings “uniprot” and “chebi”. This is an obstacle to the standardization of query protocols when we want to extract knowledge from BioPAX files. Using the same identifiers in all databases would both improve integration of BioPAX data coming from different sources and reduce redundancy.

## 5. Using BioPAX: a landscape of available tools

Since the creation of BioPAX, a wide range of tools and packages have been developed to work with BioPAX files. Due to the intrinsic complexity of the BioPAX format, specialized tools are needed to browse, retrieve, visualize and analyze pathway data in BioPAX. An overview of the landscape of available tools allowing to work with BioPAX files is available as a mindmap in Supplementary figure S-8. Also, a table of these tools is provided as Supplementary Table S-2.

One of the most polyvalent tool to work with BioPAX files is Paxtools (Demir et al. [15]). Paxtools is an open-source Java library that
constitutes the only complete BioPAX API available. It allows to perform common but complicated tasks on BioPAX files such as reading, writing, searching, merging, comparing and transforming pathway data contained in BioPAX files.

Although Paxtools is a quite complete and efficient tool for manipulating BioPAX files, it remains difficult to use for non computer scientists or users unfamiliar with Java programming or command-line based tools. Some tools have been developed in order to simplify the access to specific pathway information, or allow the manipulation of BioPAX files with other common languages in bioinformatics.

A Python tool allowing to work with BioPAX files is PAX2GRAPHML (Moreews et al. [28]). It allows to interpret BioPAX files as regulated reaction graphs describing biochemical reactions with their substrates, products, and regulators. PAX2GRAPHML allows to analyze pathway data and extract sub-graphs. The PyBioPAX Python package (Gyori and Hoyt [29]) was recently created in order to meet the need of a Python software support for BioPAX. PyBioPAX allows to manipulate and analyze BioPAX models by implementing the BioPAX model as a set of Python classes. Finally, easy manipulation of BioPAX files will be improved with the coming release of the Python BIOPAX-Explorer package (https://forgemia.inra.fr/pegase/biopax-explorer), which will provide a BioPAX Python object-model with advanced query features.

Working with BioPAX files in R is possible with the R package rBiopaxParser (Kramer et al. [30]), which allows to build regulatory networks from pathway information. Also, a part of the Paxtools library has been exported to an R package named PaxtoolsR (Luna et al. [31]).

Some tool aim to facilitate the access of pathway information in BioPAX, like the BioPAX-Parser BiP (Agapito et al. [32]) which is available through a graphic command and aims to allow users easily explore and analyze pathway information. It allows to perform pathway enrichment analysis (PEA).

Some other tools aim to meet the need of providing a visual representation of BioPAX models for easier pathway visualization. ChiBE (Chisio BioPAX Editor) (Babur et al. [33]) is a software that allows easy manipulation and visualization of BioPAX models. The Cytoscape application (Shannon et al. [34]) is able to read and visualize BioPAX files. Although Cytoscape plugins were developed to reason on BioPAX data using all the properties of the language (like the BiNoM plugin (Bonnet et al. [35]), they are unfortunately not maintained. NAViGaTOR (Network Analysis, Visualization, & Graphing TORonto) (Brown et al. [36]) is also a software for visualizing and analyzing networks that supports BioPAX files. CellDesigner (Funahashi et al. [37]) is a software that allows to create pathway diagrams. It comes with a package that is able to export pathway data in BioPAX Level 3 (Mi et al. [38]). The PathVismo software (Kutmon et al. [39]) also has a BioPAX plugin, allowing to import and export pathway data in BioPAX.

BioPAX files can be converted to other data formats using specific tools, thus improving its interoperability with the other formats and softwares that use these formats. As there are no general tools allowing to directly reason on BioPAX data, a conversion step to another data format is often needed for algorithms or tools to be used. Paxtools (Demir et al. [15]) and ChiBE (Babur et al. [33]) allow to convert BioPAX files to other data formats, ensuring wider potential downstream analysis. BioPAX data can be converted to SBML (Systems Biology Markup Language) (Keating et al. [7]) using for example the BIOPAX2SBML converter (Büchel et al. [40]), allowing integration with SBML models and dynamic simulation of the system. BioPAX data can also be converted into SBGN (Systems Biology Graphical Notation) (Le Novère et al. [9]), a graphical language allowing to build alternative projections of complex biological data.

BioPAX files can also be converted in other data formats that are not standards. For example it is possible to convert BioPAX data to GRAPHML files using PAX2GRAPHML (Moreews et al. [28]). It is also possible to convert BioPAX files in formats suitable for dynamical modeling. For example, Vignet et al. [41] developed a package, biopax2cadbiom, that automatically generates Cadbiom models from BioPAX files,

allowing to perform dynamical modeling on large-scale networks. Considering the conversion of original pathway data to BioPAX, this process is often realized independently by resource providers. Most of the time this conversion has to be set up manually and adapted to the type of data the database describes, as done by Lee et al. [42].

Finally, one singular tool available in the landscape of BioPAX tools is the BioPAX Validator (Rodchenkov et al. [43]). It allows to detect non-conformities in BioPAX files, by identifying issues in a BioPAX document. However, the BioPAX validator only validates the syntactic aspect of BioPAX. Currently there exists no tool capable of assessing the semantics of pathway data within BioPAX files. Inconsistencies are frequent in BioPAX files. For example, widespread invalid description of molecular complexes has been shown in the BioPAX export of Reactome (Juigné et al. [44]). Improving the semantic validation of BioPAX data is a key point to reduce the non-conformities in the usage of BioPAX across pathway databases.

Overall, despite the variety of tools developed for handling BioPAX data, the format remains poorly used by the scientific community. The lack of BioPAX user-friendly interfaces for pathway analysis is one of the reasons. Also, some of the tools presented above have a small user community, making them difficult to use. Another important point is that there are not enough tools allowing to directly reason on BioPAX data and extract knowledge from it. The majority of tools are more adapted for the querying of the files, like for example getting the RDF triples associated to a pathway of interest. These tools are not suited for direct reasoning on BioPAX data, such as traversing the graph of pathways or identifying potential regulators of biochemical reactions.

## 6. Interfacing BioPAX: from low-level representations to high-level abstractions

As illustrated in Fig. 3, the information in BioPAX exports can vary depending both on the scope of databases, and on the way the BioPAX semantics is used. There exist several representations of the same data in BioPAX, which can be seen as low-level representations of raw BioPAX data.

Based on the low-level representation of the pathway ‘Signaling by EGFR’ (R-HSA-177929) from Reactome, we show an abstraction method to shield users from the complexity of the BioPAX graph. Note that we envision this abstraction as a post-processing of BioPAX data in order to meet a specific goal, not as a replacement for BioPAX.

As shown in Fig. 6, the pathway is finely described using the classes and properties of the BioPAX ontology, resulting in a dense directed graph (323 nodes and 576 edges). The root pathway ‘Signaling by EGFR’ is composed of five subpathways linked to the root pathway with the *bp3: pathwayComponent* property. The same property is also used to link pathways to the biochemical reactions they are composed of. Each biochemical reaction is then associated to a reactant (*bp3:left property*) and a product (*bp3:right property*). The reactants and products are also described (small molecules, proteins or even complexes). Moreover, the regulation processes like catalysis associated to biochemical reactions are described (*bp3:controller property*), adding more relationships to the graph.

For the sake of representation, the full pathway is not represented on Fig. 6. Some properties are omitted, such as the *bp3:conversionDirection* property of biochemical reactions which adds a hub node on the graph or the *bp3:comment* property which allows to link entities to curator’s string comments. Also the cross-links to literature references and mappings external resources are not represented (*bp3:xref property*) as well as the provenance of entities (*bp3:datasource property*) and their links to entity references (*bp3:entityReference property*). Thus, the representation of the ‘Signaling by EGFR’ pathway is already a simplification of the complex BioPAX representation of the pathway described in Reactome.

However, this low-level representation is still hard to visualize and analyze because of the density and quantity of information. For example,
<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Simplified BioPAX representation of the pathway ‘Signaling by EGFR’ (R-HSA-177929) from Reactome (BioPAX export (version 90, September 2024)). The instances of some BioPAX classes are represented as color nodes (Pathway, PathwayStep, BiochemicalReaction, Complex, Protein, Small-Molecule, Stoichiometry, Catalysis, Control). The BioPAX properties between two resources are detailed on the edges. The root pathway ‘Signaling by EGFR’ and its five associated subpathways are framed with dashed boxes. Each of these pathways may be composed of different pathway steps (visualized as purple nodes), that are linked to processes that can be biochemical reactions (green nodes) or catalysis events that control the reactions (light green nodes). Red edges represent the *bp3:nextStep* edges between pathway steps. Each biochemical reaction involves entities as reactants or products, that can be complexes (dark blue nodes), proteins (light blue nodes) or small molecules (orange nodes). Biochemical reactions are also characterized by a stoichiometry, represented by pink nodes. The following BioPAX properties are not represented: *bp3:datasource*, *bp3:comment*, *bp3:xref*, *controlType*, *bp3:conversionDirection*, *bp3:evi-dence*, *bp3:organism*, *bp3:availability*, *bp3:entityReference*, *bp3:cellularLocation*. Visualization using Cytoscape (Shannon et al. [34]).</figcaption></figure>

ple, to retrieve the sequence of events that compose a pathway, it is necessary to pass through the *PathwayStep* instances and follow the *bp3:nextStep* properties. The use of the *PathwayStep* BioPAX class increases the number of paths to follow to access the sequence of biochemical reactions within pathways. Also, BioPAX introduces some bias in the topological analysis of the pathway graph. For example, every resource in a BioPAX graph is linked to an instance of the *Provenance* class via a *bp3:datasource* property, indicating the source of the data (here the Reactome database). Each entity in the graph shares a minimal distance of two with other entities in the low-level BioPAX graph. This is a critical problem when computing shortest paths between entities, or applying random walk algorithms. There is a need to separate the BioPAX representation formalism (which we consider better than the alternatives) and the analysis formalism.

A solution can be to abstract the original low-level representation of the BioPAX data to generate high-level views of the original graph. For example, we can abstract the BioPAX graph of the ‘Signaling by EGFR’ pathway presented in Fig. 6 by abstracting the instances of the *PathwayStep* class. A first simplification of the BioPAX graph, only representing the instances of the *Pathway*, *PathwayStep* and *BiochemicalReaction* classes, is given in Supplementary Figure S-9. Still, this simplification of the graph does not allow to directly access to the sequence of biochemical reactions in the root pathway and its

sub-pathways. A simplification is thus not enough to show this information, and an abstraction strategy based on reasoning is needed.

As shown in Fig. 7, the pathway ‘Signaling by EGFR’ from Fig. 6 has been abstracted to represent the sequence of biochemical reactions of the pathway (16 nodes and 21 edges). The direct biochemical reactions of the root pathway ‘Signaling by EGFR’ are represented (green nodes). Two biochemical reactions are linked by a plain green edge if their associated pathway steps on Fig. 6 (purple nodes) are linked by a *bp3:nextStep* property. This new relation on the abstracted graph has the URI *abs:NextStepBiochemicalReaction*. Biochemical reactions are linked to sub-pathways (red nodes) by a dashed gray edge if the following biochemical reaction of the sequence is a part of the sub-pathway. If any biochemical reaction of a sub-pathway is followed by a biochemical reaction of another sub-pathway, a dashed red edge is added between the sub-pathways. These two new relations have the URI *abs:NextStepPathway*. The *abs* prefixes stand for http://abstraction/.

In this way, the general succession of biochemical reactions composing the pathway ‘Signaling by EGFR’ is directly visible on the abstracted graph, represented by plain green arrows.

Similarly to this abstraction presented in Fig. 7, different types of abstractions on BioPAX graphs can be developed depending on the analysis. For example, when doing shortest paths analysis on BioPAX data,
<figure><img src="image_10.png" /><figcaption>Fig. 7. Example of abstraction of the ‘Signaling by EGFR’ pathway (R-HSA-177929) from Reactome (version 90, September 2024) of Fig. 6. The pathway was abstracted in order to show the sequence of biochemical reactions (green nodes) of the pathway and their interconnections to other sub-pathways (red nodes). On the original graph of Fig. 6, if two instances of PathwayStep are linked by a bp3:nextStep property, their associated biochemical reactions (green nodes) are linked by a new abs:NextStepBiochemicalReaction property (plain green edge) on the abstracted graph. If any biochemical reaction of the pathway is followed by a biochemical reaction belonging to another sub-pathway, an abs:NextStepPathway property (dashed gray edge) is added between the biochemical reaction node (green nodes) and the sub-pathway node (red nodes). If any biochemical reaction of a sub-pathway is followed by a biochemical reaction of another sub-pathway, an abs:NextStepPathway property (dashed red edge) is added between the sub-pathways. Ultimately, we removed the pathway steps (purple nodes). This abstraction reveals the high-level sequences between the direct components of a pathway of interest.</figcaption></figure>

one would like to abstract the relations of entities to their references (bp3:entityReference properties), which add nodes in the graph.

Overall, the abstraction strategy allows to keep working on BioPAX data when performing specific analysis, while getting rid of a part of its complexity. We thus retain all the advantages of working with a semantic web format (interoperability and standardization), while being able to reason on it and apply classical graph exploration algorithms. One possible future work would be to develop contextual abstraction of BioPAX graphs that would be adapted to specific types of analysis.

## 7. Conclusion

Overall, the BioPAX format answers the initial needs. It provides a controlled vocabulary and a data structure allowing to standardize pathway information. Also, BioPAX improves interoperability between pathway databases, thanks to the ability to map entities to external resources. This capability differentiates BioPAX as an RDF-based format from other systems biology standards such as SBML.

Although BioPAX has been adopted by individual resource providers and aggregation services such as PathwayCommons (Rodchenkov et al. [25]), its use remains very limited. The intrinsic complexity of

the format is a hindrance to its wider adoption. BioPAX is designed to be machine-readable, and is thus not directly accessible for researchers without appropriate tools. Moreover, querying BioPAX files needs programming skills (SPARQL query language). Individual data providers could also set up standalone conversion of their data to BioPAX. At present this conversion is only carried out on a regular basis by Reactome. Another area for improvement could be to have more transparent conversion processes from the original data to BioPAX. Currently, there is no way of checking how the original pathway data have been converted in BioPAX in the exports we retrieved from PathwayCommons. In addition, the variability with which BioPAX is used by databases is an obstacle to aggregation procedures of pathway data. There is therefore a need to improve the validation and quality control of BioPAX files. Also, there is an important need of tools allowing to reason on BioPAX graphs, which would be able handle the intrinsic complexity of the format. This is not the case today, as BioPAX files must be often converted into another data format for downstream analyses. To resolve the complexity of BioPAX data, the development of abstraction methods would facilitate the use of BioPAX knowledge graphs by biologists.
## CRediT authorship contribution statement

**Cécile Beust:** Writing – review & editing, Writing – original draft, Formal analysis. **Emmanuelle Becker:** Writing – review & editing, Writing – original draft, Supervision, Conceptualization. **Nathalie Théret:** Writing – review & editing, Writing – original draft, Supervision, Conceptualization. **Olivier Dameron:** Writing – review & editing, Writing – original draft, Supervision, Project administration, Conceptualization.

## Declaration of competing interest

The authors have no conflict of interest to declare.

## Acknowledgements

We thank the GenOuest bioinformatics platform for technical support.

## Appendix A. Supplementary material

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.csbj.2024.10.045.

## Data availability

The codes used in this review, including SPARQL queries, are available as Jupyter notebooks at: https://github.com/CecileBeust/BioPAXReview2024Codes.git.

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