Beata Ondrusova

Machteld Boonstra

Jana Svehlikova

Dana Brooks

Peter Van Dam

Ali Salman Rababah

Akil Narayan

Rob Macleod

Nejib Zemzemi

Jess Tate

The Effect of Segmentation Variability in Forward ECG Simulation

Segmentation of patient-specific anatomical models is one of the first steps in Electrocardiographic imaging (ECGI). However, the effect of segmentation variability on ECGI remains unexplored. In this study, we assess the effect of heart segmentation variability on ECG simulation. We generated a statistical shape model from segmentations of the same patient and generated 262 cardiac geometries to run in an ECG forward computation of body surface potentials (BSPs) using an equivalent dipole layer cardiac source model and 5 ventricular stimulation protocols. Variability between simulated BSPs for all models and protocols was assessed using Pearson's correlation coefficient (CC). Compared to the BSPs of the mean cardiac shape model, the lowest variability (average CC = 0.98 ± 0.03) was found for apical pacing whereas the highest variability (average CC = 0.90 ± 0.23) was found for right ventricular free wall pacing. Furthermore, low amplitude BSPs show a larger variation in QRS morphology compared to high amplitude signals. The results indicate that the uncertainty in cardiac shape has a significant impact on ECGI.

Introduction

Electrocardiographic imaging (ECGI) is of interest to guide clinical diagnosis, treatment and risk-stratification in patients. In ECGI, body surface potential (BSP) measurements are combined with patient-specific anatomical models to estimate cardiac electrical activity. Mathematical models are established to relate cardiac electrical activity to BSPs [START_REF] Gulrajani | The forward and inverse problems of electrocardiography[END_REF]. Whereas it is known that ECGI estimations are affected by the parameter settings in this forward model [START_REF] Van Dam | ECGSIM: interactive simulation of the ECG for teaching and research purposes[END_REF], many questions remain on the effect of segmentation uncertainty. It was shown previously that segmentations differ mainly for cardiac geometry [START_REF] Tate | A Cardiac Shape Model for Segmentation Uncertainty Quantification[END_REF]. Thus, in the research presented in this paper, we focus to study the variation in segmentation that may significantly affect computed BSPs from equivalent dipole layer source models. The study builds on previous research that was conducted by the Consortium for ECG Imaging (CEI) (https://www.ecg-imaging.org/) [START_REF] Tate | A Cardiac Shape Model for Segmentation Uncertainty Quantification[END_REF][START_REF] Tate | Uncertainty quantification of the effects of segmentation variability in ECGI[END_REF].

Materials and Methods

To describe the effect of variation in segmentations on computed BSPs, we implemented a statistical shape model derived from 15 ventricular segmentations of a single patient. Subsequently we simulated multiple BSPs, and computed statistics on the BSPs to relate to the shape statistics (Figure 1). The CT scans used in this study are publicly available on the EDGAR database as the Dalhousie dataset (https://www.ecg-imaging.org/edgardatabase) [START_REF] Sapp | Inverse solution mapping of epicardial potentials: quantitative comparison with epicardial contact mapping[END_REF].

Figure 1. The pipeline of this study.

Statistical Shape Model

The statistical shape model used in this study was computed using ShapeWorks [START_REF] Cates | Shapeworks: particlebased shape correspondence and visualization software[END_REF] on the 15 cardiac segmentations. ShapeWorks contains a particle system optimizer that computes correspondence points between all segmentations allowing to compute statistical shape models. The generated shape model comprises the mean shape and the major modes of variation computed using principal component analysis (PCA). The first five shape modes (Figure 2) capture >90% of the shape variability and were included in the shape model. The shape model was sampled 262 times using weighted Fekete sampling [START_REF] Burk | Efficient sampling for polynomial chaos-based uncertainty quantification and sensitivity analysis using weighted approximate fekete points[END_REF] assuming a uniform distribution along each shape mode and a range of 2 times the computed standard deviation (σ) computed for each axis. The correspondence points derived from these samples were used to generate closed triangulated surface meshes for forward simulation and for comparison of cardiac data between samples.

Forward Simulations

For all generated cardiac meshes, five types of ventricular stimulation protocols were used, consisting of four paced beats and one sinus beat (Figure 3). The paced beats were initiated in the left ventricle free wall, right ventricle free wall, at the apex or the septum whereas the sinus beat was represented with six simultaneous foci to mimic healthy ventricular activation. Ventricular activation sequences were calculated using the fastest route algorithm [START_REF] Van Dam | Application of the fastest route algorithm in the interactive simulation of the effect of local ischemia on the ECG[END_REF] assuming uniform propagation velocity (1.15 m/s) for each shape model and stimulation protocol. Subsequently, BSPs were computed using the equivalent dipole layer (EDL) cardiac source model [START_REF] Van Dam | ECGSIM: interactive simulation of the ECG for teaching and research purposes[END_REF] with the boundary element method-based volume conductor model. Local source strength was determined by the local transmembrane potential (TMP) shape. The BSPs were computed for all cardiac shapes assuming the same homogeneous torso model with 120 electrodes, from which the standard 12-lead ECG was derived.

Data Analysis

For all BSPs simulations, all parameters were kept constant to specifically study the effect of the cardiac shape on the depolarization phase in computed BSPs. Analysis was focused on local activation timing (LAT) maps at correspondence points and the simulated QRS complexes. LAT at correspondence points was computed per shape model using linear interpolation. Simulated BSPs were compared by means of correlation coefficient (CC), QRS duration and RS amplitude. Root mean square (RMS) signals were computed for the 120-lead BSPs per model for each simulation. Per time instance, the variation in the RMS signal was determined by dividing the range in the amplitude of the RMS signal by the mean of the amplitude at the same time instance. Data are presented as mean ± standard deviation or median [range] as appropriate.

Results

Compared to the LAT sequence of the mean shape model, the average CC was 0.98 ± 0.02 and the average absolute difference in LAT was 2.8 ± 3.2 ms. For all stimulation protocols, the standard deviation in LAT ranged between 0 and 22 ms (Table 1). The variability in computed QRS complex for the five protocols is shown in Figure 4 for two representative leads of the standard 12-lead ECG. The simulated BSPs differ in both morphology and QRS duration. On model-to-model basis, the lowest average CC 0.54 ± 0.40 was observed for lead V1 when simulating septal stimulation (Figure 5).

The average CC between QRS complexes was 0.93 ± 0.17 over all stimulation protocols. Compared to the QRS complex of the mean shape model, the average CC was 0.95 ± 0.13 (Table 2). Between stimulation protocols, there were small differences. The highest average CC was observed for the stimulation protocol initiated at the apex of the ventricles, whereas the lowest average CC was observed for the RV paced beat. The average QRS duration was the highest for RV stimulation, but the highest standard deviation with respect to the average QRS duration was observed for the LV stimulation protocol (Table 2).

The variation in RMS signals (Figure 6) showed that for all beats, the variation was highest at end of the QRS complex. But whereas the variation differed for LV, RV, and septal stimulation over the course of the QRS complex, for apical stimulation it remained similar. Per stimulation protocol, the torso electrodes most affected by the variation in cardiac shape were different (Figure 7). In the stimulation protocol with the highest average CC (Table 2, apex), variation in CC over all electrodes was low whereas with lower average CC, variation over electrodes in CC was higher. The variation in QRS complexes for the five electrodes with the lowest average CC for RV stimulation shows that the RS amplitude ratio of the QRS changes and the amplitude of the QRS complexes is low for those signals (Figure 8). There was a relation between RS amplitude and CC, with lower CCs QRS complexes had lower amplitude (Figure 9) for all stimulation protocols. Figure 7. The average CC computed for all torso electrodes and all protocols for the anterior and posterior sides of the torso. The color of the electrode corresponds to the average CC computed per electrode for all shape models and the size of the dot increases with increasing standard deviation. Figure 8. The simulated QRS complexes for all 262 cardiac shape models (orange) and mean shape (black) for selected torso electrodes with the lowest mean CC for RV stimulation protocol.

Figure 9. The CCs vs RS amplitude for all cardiac shape models and all protocols.

Discussion

In this study, we analyze the effect of variability in cardiac segmentations on the solution of the forward problem. An overall good correlation (0.98 ± 0.02) was observed between LAT sequences per stimulation protocol (Table 1). We demonstrate that BSPs computed for 262 cardiac shape models vary on both the model-to-model (Figure 5) and model-to-mean basis (Figures 4, 7 and 8). Per stimulation protocol, different leads were affected most (Figure 7). The lowest CC values were observed with RV pacing and the highest for apical pacing (Table 2). When relating this to the cardiac segmentation variability [START_REF] Tate | A Cardiac Shape Model for Segmentation Uncertainty Quantification[END_REF], the lowest variability in shape was also observed around the apex of the heart. Thus, the lower variability in QRS morphology may be the result of an overall more similar average direction (apex to base) of activation waves traveling through the heart during apical pacing when compared to the other paced beats. For apical stimulation, the variation in the basal segment is likely to significantly change QRS morphology only at the beginning and end of the QRS complex, but less effect is expected in mid-QRS as the main direction of cardiac activation remains similar from apex to base (Figure 6). In the other pacing protocols, changes at mid-QRS are observed, which can be explained by the fact that if the pacing sites are closer to the base, changes within this region will thus affect QRS morphology at mid-QRS, as the basal region is activated earlier. Due to slight differences in QRS duration per simulation protocol, the highest variation in RMS curves is observed at end QRS. Furthermore, we showed that simulated QRS complexes for all torso electrodes were not equally affected by the variation in cardiac shape and that some electrodes show higher differences amongst simulated QRS complexes also depending on the stimulation protocols. Higher differences between simulated QRS complexes occurred near the base of the heart, in the valvular plane, corresponding to high shape variability [START_REF] Tate | A Cardiac Shape Model for Segmentation Uncertainty Quantification[END_REF]. The highest differences in computed BSPs in terms of the CC were observed for the paced beat initiated in the RV on the posterior region, which demonstrated only moderate shape variability [START_REF] Tate | A Cardiac Shape Model for Segmentation Uncertainty Quantification[END_REF]. Signals in this area also showed low amplitude, however not all low amplitude signals had low CC (Figure 7). The high variability of the CC in the posterior region denotes that it is an area where the direction of activation changes the most.

The results of this study suggest that the variation in cardiac shape affects the simulated BSPs. However, a more comprehensive study needs to be done to further quantify how the variation in shape affects the solution of the forward problem and consequently the ECGI estimation of cardiac activity. Furthermore, we used a simple model to simulate the activation sequences and did not yet focus on the repolarization phase. The effect of the application of repolarization gradients and more sophisticated models of cardiac activation will also be assessed with regards of the uncertainty quantification.

Conclusion

In this study, we showed that a high variation in shape is observed at the base of the ventricles around the heart valves. It was shown that this variation in cardiac shape affects the simulated BSPs. The extent to which the simulated signals are affected depends on the ventricular stimulation protocol used. The results suggest that variation in cardiac shape may have a significant impact on the ECGI. Thus, more emphasis must be placed on the study of the effects of variability in segmentations on the accuracy of the ECGI which plays an important role in clinical practice.

Figure 2 :

 2 Figure 2: Variation in first five modes of variation. Standard deviation (σ) is presented in mm.

Figure 3 .

 3 Figure 3. Ventricular stimulation protocols depicted for the mean cardiac shape model.

Figure 4 .

 4 Figure 4. The simulated QRS complexes for all 262 cardiac shape models (orange) and mean shape (black) for a lead II and V1 of a standard 12-lead ECG for all protocols.

Figure 5 .

 5 Figure 5. Pearson's correlation matrix for a lead II and V1 of a standard 12-lead ECG for all protocols.

Figure 6 :

 6 Figure 6: The variation in RMS signal for all protocols.

Table 1 :

 1

	Protocol	Avg. CC	Avg. abs diff.	Range stdev.
		[-]	[ms]	[ms]
	LV	0.99 ± 0.01	4 ± 9	0 -11
	RV	0.98 ± 0.02	5 ± 9	0 -22
	Apex	0.99 ± 0.01	4 ± 12	0 -8
	Septum	0.97 ± 0.04	3 ± 7	0 -20
	Sinus	0.98 ± 0.01	3 ± 6	0 -7

Average correlation coefficient (Avg. CC), average absolute difference (Avg. abs. diff), and standard deviation (stdev) range in local activation timing (LAT) for all protocols.

Table 2 :

 2 Average correlation coefficient (Avg. CC), the range of CCs and QRS duration for all protocols.

	Protocol	Avg. CC	Range CC	QRS duration
		[-]	[-]	[ms]
	LV	0.97 ± 0.10	-0.86 -1.00	124.14 ± 5.35
	RV	0.90 ± 0.23	-0.86 -1.00	134.69 ± 3.87
	Apex	0.98 ± 0.03	0.50 -1.00	114.75 ± 2.40
	Septum	0.95 ± 0.11	-0.63 -1.00	98.04 ± 3.44
	Sinus	0.93 ± 0.10	-0.62 -1.00	70.26 ± 2.68

Acknowledgments

This work was supported by the VEGA Grant Agency in Slovakia under grant number 2/0109/22, by the Slovak Research and Development Agency under grant number APVV-19-0531, by the National Institutes of Health under grant number NIBIB-U24EB029011. The authors would like to thank all who generously provided the segmentations used in this study.


Annabelle Monnier
email: annabelle.monnier@univ-rennes1.fr

Je

Cécile Beust……………………………………………

ENGAGEMENT DE NON PLAGIAT

e) en Master 1 Bioinformatique………………………………… Déclare être pleinement informé (e) que le plagiat de documents ou d'une partie de documents publiés sous toute forme de support (y compris l'internet), constitue une violation des droits d'auteur ainsi qu'une fraude caractérisée.

En conséquence, je m'engage à citer toutes les sources que j'ai utilisées pour la rédaction de ce document.

Table of Contents

Chronic liver diseases and the exposome concept

Chronic liver diseases (CLD) are long-term diseases that lead to a progressive deterioration of the liver functions. They are characterized by chronic inflammation, destruction of the liver parenchyma and the formation of regeneration nodules, associated with the development of fibrosis and cirrhosis [START_REF] Sharma | Chronic Liver Disease[END_REF]. The causes of these diseases are multiple : viral infection (hepatitis B, C), alcohol overconsumption, metabolic disorders (Non-Alcoholic Fatty Liver Disease), or genetic factors. The diversity of these causal factors makes chronic liver diseases an important public health issue. They have become an epidemiological problem, given the wide range of people affected throughout the world, and the considerable impact of our lifestyle and environment on the development of these diseases [START_REF] Beier | Environmental exposure as a risk-modifying factor in liver diseases: Knowns and unknowns[END_REF]. Indeed environmental contaminants contribute to the development of chronic liver diseases [START_REF] Armstrong | Understanding Environmental Contaminants' Direct Effects on Non-alcoholic Fatty Liver Disease Progression[END_REF]. This leads us to the exposome concept, which is defined as the set of environmental exposures to which an individual is exposed through his life from conception to death [START_REF] Wu | Capturing a Comprehensive Picture of Biological Events From Adverse Outcome Pathways in the Drug Exposome[END_REF] (Figure 1) . Identifying the links between exposure factors and chronic liver diseases is therefore a key factor in the understanding of these pathologies. In order to study the exposome and how it affects human health, it is important to store and be able to access information about chemicals and any type of environmental exposure, as well as their possible involvements in the onset of diseases or physiological disorders. There are many specialized databases that store information about toxicology like CTD1 , T3DB2 or Exposome Explorer 3 . Within the framework of this internship, we used the Comparative Toxicogenomics Database (CTD) because it describes environmental exposures at several levels of association (chemical-gene, chemical-disease and gene-disease associations), giving a complete and global vision of the impact of exposures on human health. CTD is the database that provides access to a large amount of cross-referenced information about the impact of chemical exposures on the development of liver diseases.

Integration of databases thanks to Semantic Web technologies

To analyse the biological interactions described in the CTD, we have to integrate its content in order to structure it and give it meaning. Integrating CTD data would allow to cross-reference its information with other databases. For example, to characterize an entity like a gene, we need to call on the Gene Ontology4 terms. This need for integration is increasingly felt in the field of life sciences, because of the large expansion of biological data over the last decades [11] [10].

Integrating biological data can be facilitated thanks to the Semantic Web technologies. The Semantic Web is an extension of the World Wide Web in which information is given well-defined and explicitly meaning, better enabling computers and people to work together in cooperation [START_REF] Berners-Lee | The Semantic Web: A New Form of Web Content That is Meaningful to Computers Will Unleash a Revolution of New Possibilities[END_REF]. Thus, the aim of the Semantic Web is to give sense to the Web data, making them interpretable by a machine. This is enabled thanks to standards like RDF (Resource Description Framework) 5 , allowing automatic processing and interoperability of data. In RDF, resources are described with identifiers called URIs (Uniform Resource Identifiers) and are represented as triples (subject, predicate, object) as shown in Figure 2. The subject is the resource we are describing, the predicate is the relationship describing the subject, and the object is one of the values of the predicate for the subject. A dataset described as RDF triples can be represented as an oriented graph, a web of information about related things ( [START_REF] Berners-Lee | The Semantic Web: A New Form of Web Content That is Meaningful to Computers Will Unleash a Revolution of New Possibilities[END_REF]). RDF data can be queried thanks to the SPARQL query language 6 .

Semantic web standards also include ontologies, which are computational models explicitly representing the meaning of terms and the relationships between those terms [START_REF] Chen | Semantic Web meets Integrative Biology: a survey[END_REF]. Ontologies provide controlled vocabularies shared by the community, and allow to describe any type of resource and relationship with the use of hierarchical classes and subclasses and their corresponding properties.

Integration of databases content into a Semantic Web's format is important because most databases like CTD often use their own file formats and are not interoperable with each other.

Many life science reference resources are already available in Semantic Web format as shown in Figure 3. Unfortunately, this is not the case for the up-to-date CTD.

Goals of the internship

The aim of this internship is to use the Comparative Toxicogenomics Database as a source of information to identify environmental factors that contribute to the onset of chronic liver diseases. The first objective was to integrate the CTD data into a Semantic Web format. We chose the BioPAX format, which is adapted to the description of biological pathway data in accordance with Semantic Web standards. Ultimately, the integrated CTD data would allow to build large-scale networks of chemical-gene-disease associations with the CadBiom application.

The interrogation of these networks would allow to identify factors triggering the expression genes associated with chronic liver diseases.

Materials and methods

The Comparative Toxicogenomics Database (CTD) as an information source

The Comparative Toxicogenomics Database (http://ctdbase.org/) is a publicly available database describing chemical-gene, chemical-disease and gene-disease associations to better understand the impact of environmental exposures on human health. It uses controlled vocabularies about chemicals, genes, diseases, anatomy and pathways as well as ontologies to favor interoperability with other databases and the interconnection of knowledge. The CTD is updated every month and is enriched by inferring relationships through text mining in articles. The information is manually curated by professionals, thus ensuring the accuracy of the stored data [START_REF] Davis | Comparative Toxicogenomics Database: a knowledgebase and discovery tool for chemical-gene-disease networks[END_REF] (Figure 4). Today the CTD describes more than 45 million toxicogenomic relationships involving 16300 chemicals, 51300 genes, 5500 phenotypes, 7200 diseases and 16300 exposure events for more than 600 species [START_REF] Peter | Comparative Toxicogenomics Database (CTD): update 2021[END_REF]. The CTD is made up of 18 data files freely downloadable in csv, tsv and xml formats via the CTD download tab.

Translation of the CTD information into BioPAX format

Information on chemical-gene-disease associations from the CTD is defined as biological pathway data since it represents biological regulatory processes. More precisely, a pathway is a set of interactions between physical or genetic components, often describing a causal-andeffect or time-dependant process, that explains an observable biological phenomenon. To answer our biological question we need to integrate CTD information into a Semantic Web's format in order to browse, query, visualize and analyze it. We therefore chose to translate the CTD data in the BioPAX format (Biological Pathway Exchange, http://www.biopax.org/)

which is an open standard knowledge format for the representation of pathway interactions [START_REF] Demir | The BioPAX community standard for pathway data sharing[END_REF]. It is also a community-developed language that aims to improve the sharing, integration and unification of pathway data knowledge between databases. BioPAX format uses the RDF formalism, and is implemented as an OWL Ontology, which means that the data are represented as hierarchichal structured classes and subclasses having their own properties while inheriting the properties of superclasses. The BioPAX ontology helps to describe pathway data by providing a controlled vocabulary for biological interactions as shown in Figure 5. The level 3 of the BioPAX ontology used for this project was released in 2010 and is available at http://www.biopax.org/release/biopax-level3.owl BioPAX allows the description of several types of pathway data such as metabolic, molecular and signaling pathways and is also suitable for the representation of genetic interactions and gene regulation networks. BioPAX is thus well adapted to represent the pathway data of the CTD.

Moreover, the CadBiom application that we want to use to represent the pathways can take as input BioPAX files to convert it in the BCX format thanks to the program biopax2cadbiom (https://gitlab.inria.fr/DYLISS/biopax2cadbiom) developed by Pierre Vignet [START_REF] Vignet | Discrete modeling for integration and analysis of large-scale signaling networks[END_REF].

Some of the data contained in the CTD are available in the BioPAX language on Pathway Commons (https://www.pathwaycommons.org/) in the form of an OWL file. Pathway commons is an online database storing integrated pathway data from multiple databases in a Semantic

Web's format. This BioPAX file of the CTD was submitted in 2019, and the CTD-to-BioPAX converter used was coded by Igor Rodchenkov and is available on his public GitHub repository (https://github.com/PathwayCommons/ctd-to-biopax). We explored the content of this file with SPARQL queries and it revealed that the file was not usable because of its incompletness. We tried to reuse the available converter, but due to system compatibility issues, we were never able to get it to work. So, we chose to recode a CTD-to-BioPAX converter in Python7 in order to have an up-to-date and well-described BioPAX file of the CTD.

The Python converter takes as input a tsv file downloaded from the CTD and gives as output a BioPAX file describing the content of the tabulated file in a RDF formalism. One of the main advantages of representing data in RDF is that it allows automatic querying of datasets with the SPARQL query language. Thanks to SPARQL queries we can analyze the relationships between entities in RDF datasets: class and subclass identification, determination and count of direct or indirect instances of a class, characterization of the relationships linking instances. To perform SPARQL queries we used the Apache Jena Fuseki server software (https: //jena.apache.org/documentation/fuseki2/).

After getting our BioPAX file of the CTD, we converted some interactions into an oriented graph. To do this we used a bash script rdf2image, available on the GitLab repository of Olivier DAMERON (https://gitlab.com/odameron/rdf2image).

CadBiom : an application to build dynamic networks

CadBiom (Computer Aided Design of BIOlogical Model, http://cadbiom.genouest.org/)

is an application developed by the DYLISS and the Dymec teams, allowing to model the dynamics of biological networks as discrete events [START_REF] Andrieux | An integrative modeling framework reveals plasticity of TGF-β signaling[END_REF]. CadBiom models biological interactions on the basis of guarded transition formalism. This discrete modeling approach models system dynamics by taking into account the competition and cooperation events in chains of reactions [START_REF] Vignet | Discrete modeling for integration and analysis of large-scale signaling networks[END_REF]. A guarded transition is defined with a quadruplet as shown in Figure 6. A set of transitions forms a trajectory and all trajectories constitute the final biological network. CadBiom also allows to query the networks by "Model Checking" approaches. This is a property of the software allowing to answer a biological question such as "What leads to the activation of a given gene ?", by checking all the state variables and chronology of events leading to the activation of this gene. Model checking enables to select trajectories that can answer the biological question.

Results

Obtaining a BioPAX file describing the content of the CTD

Evaluation of the reusability of the 2019 BioPAX file of the CTD

First, we tried to re-use the available BioPAX file of the CTD on PathwayCommons. This file is from 2019, and was built using 3 files (chemical-gene interactions, chemical vocabulary and gene vocabulary) of the 18 files of the CTD. We explored its content using SPARQL queries in order to qualify and to quantify the contained information. SPARQL queries revealed that there was a considerable loss of information compared to the original data of the CTD. The information was partially described. For example, the chemical "10074-G5" (an inhibitor of c-Myc-Max dimerisation) is involved in 15 reactions according to the current CTD taublated files, but only appears in 3 of them according the the 2019 BioPAX version (Figure 7). It could be explained by the fact that the file is not up-to-date, considering that the CTD is updated every month. But apart from that, the information contained in the file was not clearly described and the way the file was build was unclear.

Re-use the Java CTD-to-BioPAX converter

Since the 2019 BioPAX file was not usable, we tried to regenerate a 2022 version of the CTD using the CTD-to-BioPAX converter developed by Igor Rodchenkov (https://github.com/ PathwayCommons/ctd-to-biopax) that was used to build the 2019 file. But I encountered some system incompatibility issues with this converter that I never managed to get it to work, even with the help of Sebastien AUBER who worked with it during his 2021 internship, nor with the help of Matéo BOUDET, an engineer from the Genouest platform. The converter was coded in Java and the code was not easy to understand for people who don't know this language.

Moreover, the Java code was structured inside a Maven project 8 and the main problem I encountered was to deal with several versions of Java. Some packages of the Maven project were suitable for an older version of Java and it didn't worked on more recent systems, even with the use of virtualization processes like Docker9 or using virtual environments on Genouest virtual machines. Altogether, this made future use of the converter not sustainable. Thus, we decided to code a new CTD-to-BioPAX converter using the Python language.

Writing a standalone CTD-to-BioPAX converter

The code of the CTD-to-BioPAX converter is available on my GitLab repository (https: //gitlab.com/cecilebeust/ctd_to_biopax.git).

Convert the tabulated files of the CTD

To convert the content of the CTD into BioPAX, we must first understand how it is represented in the database itself. This is not documented on the CTD website nor in the CTD articles. We chose to focus first on the chemical-gene interactions file, and took an example of such interaction involving the chemical "10074-G5" and searched for the corresponding reactions. In the chemical-gene interactions file, one line describes one interaction between one chemical and one gene, and values of each column are separated by tabulations. Table 1 is a table containing the values from the file for a line that describes a relationship involving the chemical "10074-G5".

This is the information that will be used to build the RDF triples describing the interaction. Table 1. CTD chemical-gene interactions file content example for one line describing one interaction involving the chemical "10074-G5". The CasRN cell is empty because there was no value for the Cas Registry Number for the chemical "10074-G5".

The CTD cross-references information from different databases to build these relationships. It uses the MeSH database 10 to identify the chemical with a ChemicalID. When it is available, the chemical also has a Cas Registry Number 11 (CasRN, which is not the case here). The CTD also uses NCBI Gene Identifiers 12 to identify the gene involved (GeneID), and a NCBI Taxonomy identifier 13 for the organism (OrganismID). Each interaction has also an assigned PubMed identifier (PubMedIDs) that links it to a literature reference in PubMed 14 . The need to use multiple databases to characterize an entity is justified by the fact that resource descriptions are fragmented on the Web. This is where BioPAX comes in, as it aims to improve the integration and efficient reuse of data between databases [START_REF] Demir | The BioPAX community standard for pathway data sharing[END_REF].

Taking into account these information, we can build a graph representing the entities involved in this interaction, the relationships between them and the BioPAX class to which they can be connected (Figure 8). For the description of entities involved in the interactions, we added some external identifiers to those already used in the CTD in order to cross-reference the information about the entities available in other database. We used SO15 and SKOS16 identifiers which refer to other gene ontologies. The mesh:D005796 instance refers to the Gene section of the MeSH database and the mesh:D011506 instance refers to the Protein section. After characterizing chemicals and genes, we assigned chemicals to the bp3:SmallMolecule BioPAX class and the genes to the bp3:Gene class (bp3 is the prefx meaning BioPAX level 3).

Analysis of the Chemical-Gene interactions file of the CTD

To describe the interactions between chemicals and genes, we used the InteractionActions column of the CTD file, which is the one precising the type of the interaction and its effects. class. I chose to first describe these 5 interactions because these are the ones involving the chemical "10074-G5" that we took as example. I used this example to build a structure of the converter and make sure that it worked before converting the entire file.

Conversion of some simple leaves

The description of elementary interactions gives a set of RDF triples which can be considered as simple leaves in the final oriented graph of the final BioPAX file. Figure 10 shows one of these leaves.

Extraction of the graph of dependencies between the CTD interactions

In addition to describe elementary interactions, the

Comparaison with the 2019 BioPAX file

We compared the new CTD BioPAX file to the one available on PathwayCommons in order to quantify the information gain concerning chemical-gene interactions. As shown in Figure 11, the new file is enriched in all classes except the bp3:Catalysis one when compared with the 2019 BioPAX file of the CTD. Importantly we noted a very significative increase in instances of the bp3:TemplateReactionRegulation class.

Discussion

Analysis of the new CTD BioPAX file

The final BioPAX file of chemical-gene interactions of the CTD that we obtained thanks to the python converter is huge. It contains approximately 130 million of lines for a size of 9,5 GB. It was thus quite difficult to query with SPARQL and I had to subdivize it in two files to make the queries.

The SPARQL queries revealed that the new file contains significantly more information than the file from 2019. Indeed, it contains three times more RDF triples than the 2019 BioPAX file, and thus describes much more chemical-gene interactions. In particular, there is a sharp increase in the number of TemplateReactionRegulation interactions described. Since we associated this class to interactions having an impact on gene expression, the new file seems to support the description of three times more of these interactions. This global observation can be explained on one hand by the fact that the CTD had been enriched since 2019 by new chemical-gene interactions. We wanted to compare the two files using the same CTD input data, but for this we needed to get the CTD chemical-gene interaction file from 2019. But this was not possible because the CTD does not store and provide archive files. Also the comparaison of the two files can not be based on the same input data. On the other hand the python converter seems to describe much more interactions. The converter used to build the 2019 BioPAX file seems to not decompose chain reactions into elementary ones and then miss a lot of information about interactions linked by causal relationships.

Possible improvements for the converter

The python converter works well however it assigns a specific BioPAX class to only 5 types of chemical-gene interactions for the moment. All interactions are described in the file as instances of the Interaction class, but only 5 types are associated to a more precise BioPAX class. By doing this for all the 134 elementary interactions, and by describing these interactions using correct BioPAX properties, it would be possible to precisely describe it all in the final file.

By identifying BioPAX subclasses that could describe more precisely the interaction we can further improve the coverage of the converter. Indeed, the more we go down in the BioPAX hierarchy, the more we can describe our interactions with precise terms. In the current file, many interactions are associated to the Control class which is the most global to describe reactions that influence others. We could search for subclasses of Control that could describe more precisely the nature of these interactions. Netherless, this would require an important work to study in detail each interaction type in order to determine the most appropriate BioPAX class for each interaction. There are also still some improvements to bring to the description of complex interactions since the most complex ones (high number of chained reactions) are not perfectly described and structured. Finally, the global code of the converter could be optimized in order to be more efficient and less-resource intensive.

Next steps of the analysis

To analyze CTD data further and answer our biological question, the next step is to build large scale networks of chemical-gene interactions described in BioPAX using the CadBiom application. We plan to do this over the next weeks before the end of the internship. Then it will be possible to query these networks using reachability queries. It will allow to identify the trajectories to activate genes involved in liver fibrosis and next to extract the chemicals involved in these trajectories. Based on this analysis we will identify chemical signatures that have an impact on the expression of genes associated to chronic liver diseases.

Conclusion

The CTD-to-BioPAX python converter that I developed allows to build a BioPAX file containing the CTD chemical-gene interactions that is up-to-date and well-described in accordance to Semantic Web standards. Its information is standardized and is computer-readable, allowing it to be queried. In a second part of this project, it will allow to determine exposure factors associated to chronic liver diseases. Considering the wide range of exposures that can be involved, this integrative biology approach makes sense and seems to be the most appropriate to answer this biological question. Ultimately, it could be possible to adapt the converter in order to translate the whole content of the CTD into BioPAX and to be able to query other parameters.

Personal assessment of the internship

This internship allowed me to be part of the DYLISS research team for three and a half months, while having weekly interactions with the Dymec research team at the Irset. I realized the importance of cooperation and communication between researchers from different backgrounds when working at the interface between two disciplines that are computer science and biology. This internship has also made me more autonomous in my work. It taught me to be well organized in the management of a project over several months. I also had the opportunity to present my work to the members of the DYLISS and Dymec teams. It made me work on my communication skills and taught me to adapt my speech depending on whether I was addressing to biologists or computer scientists. This project also gave me a good overview of scientific research and its hazards. I learned how to overpass problems and to adapt to the difficulties encountered during this internship, especially computer problems.

Finally, I would also say that this internship made me aware of the importance of producing a quality code that is reusable. This is a good practice that will be useful in all types of projects that I will have to manage in the future.

Introduction 1 . 1 4 Discussion 4 . 1 6 . 1

 1144161 Chronic liver diseases and the exposome concept . . . . . . . . . . . . . . . . 1.2 Integration of databases thanks to Semantic Web technologies . . . . . . . . . 1.3 Goals of the internship . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Materials and methods 2.1 The Comparative Toxicogenomics Database (CTD) as an information source . 2.2 Translation of the CTD information into BioPAX format . . . . . . . . . . . . 2.3 CadBiom : an application to build dynamic networks . . . . . . . . . . . . . . 3 Results 3.1 Obtaining a BioPAX file describing the content of the CTD . . . . . . . . . . . 3.1.1 Evaluation of the reusability of the 2019 BioPAX file of the CTD . . . 3.1.2 Re-use the Java CTD-to-BioPAX converter . . . . . . . . . . . . . . . 3.2 Writing a standalone CTD-to-BioPAX converter . . . . . . . . . . . . . . . . . 3.2.1 Convert the tabulated files of the CTD . . . . . . . . . . . . . . . . . . 3.2.2 Analysis of the Chemical-Gene interactions file of the CTD . . . . . . 3.2.3 Conversion of some simple leaves . . . . . . . . . . . . . . . . . . . . 3.2.4 Extraction of the graph of dependencies between the CTD interactions . 3.2.5 Comparaison with the 2019 BioPAX file . . . . . . . . . . . . . . . . Analysis of the new CTD BioPAX file . . . . . . . . . . . . . . . . . . . . . . 4.2 Possible improvements for the converter . . . . . . . . . . . . . . . . . . . . . 4.3 Next steps of the analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Presentation of the host structure . . . . . . . . . . . . . . . . . . . . . . . . . iii 6.2 Personal assessment of the internship . . . . . . . . . . . . . . . . . . . . . . . iv

Figure 1 .

 1 Figure 1. The Exposome is the sum of all the factors our body is exposed to (Escher et al. [9])

Figure 2 .

 2 Figure 2. Example of a RDF triple represented as an oriented graph. This triple describes the instantiation relationship between the object gene01 and its belonging class Gene. It describes that the object gene01 is an instance of the class Gene through the predicate rdf:type.

Figure 3 .

 3 Figure 3. LOD (Linked Open Data) Cloud, representing all RDF datasets linked together in the Web. Life sciences datasets are represented in pink and the density of black lines in this section shows strong interconnection of resources.

Figure 4 .

 4 Figure 4. Chemical-Gene-Disease associations described in the CTD (a) and inference process of new associations from literature (b) (Davies et al. [6])

Figure 5 .

 5 Figure 5. Simplified representation of the hierarchy of the main BioPAX classes. The Entity class is the parent class of all the other BioPAX classes. In the blue box are listed the properties we can apply to the instances of the Protein class (Demir et al., [8])

Figure 6 .

 6 Figure 6. Guarded transition modeled with CadBiom. A guarded transition is defined with a quadruplet (A,B,C,h). A is the input, B is the output, h is the time event and C is the logical condition for the transition to occur. The transition can only occur if A is activated and if C is verified. If the transition occurs, A is inactivated and B is activated. All entities sharing the same time event will occur at the same moment.

Figure 7 .

 7 Figure 7. 15 chemical-gene interactions involving the chemical 10074-G5 (inhibitor of c-Myc-Max dimerisation) stored in the CTD. The 3 interactions described in the BioPAX file of the CTD on PathwayCommons are highlighted in yellow.

Figure 8 .

 8 Figure 8. Graph representing an interaction involving the chemical "10074-G5". BioPAX classes are represented by blue boxes and their instances by purple ovals. Each arrow represents a relation linking an instance to its reference class, or linking two instances together. For each relation, the predicate (the nature of the relation) is indicated above the concerned arrow. Strings referring to compound, gene, organism or interaction name are represented by light blue lozenges.

  In this column, elementary interactions are presented chained to each other by a pipe character, as presented on Figure7. The column contains 3264 combinations of 134 elementary interactions, that can chain to each other up to 7 times, giving an important depth of description of the interactions. It is therefore difficult to describe one line of the file in BioPAX because it refers to several interactions influencing each other. The python converter has a DescribeInteractions function which parses the content of the InteractionActions column and decomposes the chain into elementary interactions. These elementary interactions are successively described by the function by attributing one unique identifier to each interaction, and a specific BioPAX class to each interaction type.

Figure 9 .

 9 Figure 9. Extract of the DescribeInteractions function which describes chain interactions. This portion of code describes interactions affecting the activity of a protein.

Figure 10 .

 10 Figure 10. Chemical-gene elementary interaction "10074-G5 results in decreased activity of MYC protein" described in BioPAX as RDF triples in an oriented graph. Instances are represented by blue ellipses; classes by boxes containing URIs and strings by boxes containing the value.

  DescribeInteractions function successively links chained reactions in the final BioPAX file. Since the interactions are linked by causal relationships and are chained to each other as shown in the InteractionActions column of the CTD file in Figure 7, the DescribeInteractions function is recursive. It calls itself multiple times while parsing the content of the InteractionActions column. The final BioPAX file of chemical-gene interactions of the CTD results in an oriented graph containing simple leaves linked to each other. The final graph of chemical-gene interactions of the CTD is a dependency graph between interactions representing a total of 23, 616, 142 RDF triples.

Figure 11 .

 11 Figure 11. Comparaison of the information in the CTD BioPAX files from 2019 and the newly-built chemical-gene interactions BioPAX file of the CTD (2022). The number of instances of each BioPAX class used to build the file are counted. Each of these instances represents one elementary chemical-gene interaction described using this class.

1

 1 Presentation of the host structure This first year bioinformatics Master's degree internship is held in collaboration between the DYLISS team (Dynamics, Inference and Logics for BIological Systems and Sequences) of the IRISA (Institute for Research in Computer Science and Randomized Systems) and the Dymec team (Microenvironment Dynamics and Cancer) of the IRSET (Institute for Environmental and Occupational Health Research). The DYLISS team is a bioinformatics team working on data management and representation in life science using Semantic Web technologies. It is part of the IRISA laboratory, which is a mixed research unit in computer science and new information technologies. The IRISA is attached to the Inria Rennes-Atlantic Brittany center (French national research institute for digital science and technology). The Dymec team works on the impact of the exposome on the occurence of chronic liver diseases. The team possesses a research axis dedicated to integrative biology and modeling. It is part of the Irset laboratory, a mixed reasearch unit of the Inserm, which focuses on environmental and work-related risks.

http://ctdbase.org/

http://www.t3db.ca/

http://exposome-explorer.iarc.fr/

http://geneontology.org/

https://www.w3.org/TR/rdf11-concepts/

https://www.w3.org/TR/sparql11-overview/

https://www.python.org/

https://maven.apache.org/

https://www.docker.com/

http://purl.obolibrary.org/obo/SO_

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

Acknowledgements

I would like to warmly thank my supervisors, Olivier DAMERON and Nathalie THERET, for this internship opportunity, for their kindness and their precious advice as well as their great support throughout this internship.

I would like to thank the DYLISS and Dymec teams members for their warm welcome, their good mood, and their feedbacks on my work. Thanks also to the other IRISA's interns for the good atmosphere in the intern room, as well as to Guillaume COLLET for the batucada lessons on lunck break! I would also like to thank Sébastien AUBER, who did his internship in 2021 on a similar subject, for his help with the use of some tools. Thanks also to Matéo BOUDET for helping me in the installation of CadBiom and make it work on the Genouest cluster.

I also thank my referent teacher Annabelle MONNIER for her availability and her external support during this internship.

Abstract

Chronic liver diseases are long-term pathologies affecting a wide range of people nowadays. They can be caused by multiple factors including viral infection, alcohol overconsumption, metabolic disorders or genetic factors to which are added the impact of our lifestyles and environmental exposures. These diseases have become an important public health problem, but the impact of our environment is difficult to determine give the diversity of factors involved and the dynamics of these exposures. This internship project aims to identify environmental exposure factors associated to the occurrence of chronic liver diseases. The Comparative Toxicogenomics Database (CTD) is used as an information source. This database describes exposure events at several levels of associations (chemical-gene, chemical-disease and gene-disease associations). The conversion of the CTD tabulated chemical-gene interactions file into the BioPAX format (Biological Pathway Exchange) thanks to the development of a Python CTD-to-BioPAX converter allows to standardize it in accordance to Semantic Web standards. The obtained BioPAX file can be used as an input of the CadBiom application developed by the DYLISS and Dymec teams in order to build large-scale biological dynamic networks based on guarded transitions. The generated models could be analyzed thanks to reachability queries in order to identify environmental exposures causal signatures associated to the occurrence of chronic liver diseases.

Keywords : chronic liver diseases, exposome, Semantic Web, large-scale networks, BioPAX

Résumé

Les maladies chroniques du foie sont des pathologies de longue durée qui touchent aujourd'hui un grand nombre de personnes. Elles peuvent être causées par de multiples facteurs comme une infection virale, une surconsommation d'alcool, des troubles métaboliques ou des facteurs génétiques, auxquels s'ajoute l'impact de nos modes de vie et des expositions environnementales. Ces maladies sont devenues un problème de santé publique important,