Integration and analysis of heterogeneous biological data through multilayer graph exploitation to gain deeper insights into feed efficiency variations in growing pig

Summary

Recent technological advancements in biological data acquisition have resulted in an explosion of multimodal and multicentric data. This phenomenon raises numerous questions regarding the storage, standardization, and analysis of these massive datasets. This thesis focuses on the development of an integrative method that can be used to analyze and extract knowledge from biological data. To account for their strong interdependencies, this approach involves the integration of different types of biological entities (mRNA, proteins, metabolites, observable traits) that are typically studied independently. The computational solution it devises enables the integration of these heterogeneous data into a multilayer graph, with each layer representing a specific type of entity. The solution’s novelty lies in linking elements within a layer or across different layers by utilizing properties extracted from public knowledge databases through Semantic Web technologies. Based on this graph, the objective is to characterize the relationships among a group of molecules of interest using graph theory metrics. The method is applied to experimental datasets (transcriptomics, metabolomics and animal phenotypes) to describe and understand the relationships between specific molecules and determine their importance in feed efficiency variations in growing pigs. Feed efficiency is a key phenotype for sustainable farming, but is recognized as complex. This work provides innovative analysis methods to analyze and integrate various levels of biological organization, facilitating a better understanding of biological processes. 

Keywords :  Data integration, Feed efficiency, Multilayer graph, Multi-omics, Web SemanticKeywords :  Data integration, Feed efficiency, Multilayer graph, Multi-omics, Web Semantic

Contact

• Camille Juigné (EGAAL , UMR Pegase )
• Linkedin

Publications

Conference papers 

• Detection and correction of non-conformities and redundancies in complexes of molecules in BioPAX. Camille Juigné, Olivier Dameron, François Moreews, Florence Gondret, Emmanuelle Becker. Journées Ouvertes en Biologie, Informatique et Mathématiques (JOBIM), Jul 2022, Rennes, France. pp.1-25. ⟨hal-03752473⟩. 
• A method to identify target molecules and extract the corresponding graph of interactions in BioPAX. Camille Juigné, Olivier Dameron, Florence Gondret, Emmanuelle Becker. BBCC2022 - Bioinformatics and Computational Biology Conference, Dec 2022, Virtual, Italy. ⟨hal-03876091⟩. Oral
• Combined transcriptomics and metabolomics in the whole blood to depict feed efficiency in pigs. Camille Juigné, Emmanuelle Becker, Florence Gondret. EAAP Annual Meeting 2023, Lyon, France - August 26th / September 1st, 2023. Oral.
• A graph-based approach to identify complex connections in heterogeneous biological networks. Camille Juigné, Emmanuelle Becker, Océane Carpentier, Florence Gondret, Olivier Dameron. BDA - French National Database Conference 2023. 23-26 oct. 2023 Montpellier (France)

Journal article

• Fixing molecular complexes in BioPAX standards to enrich interactions and detect redundancies using Semantic Web Technologies. Camille Juigné, Olivier Dameron, François Moreews, Florence Gondret, Emmanuelle Becker. Bioinformatics, btad257, 2023. https://doi.org/10.1093/bioinformatics/btad257
• Juigné, C., Becker, E. & Gondret, F. Small networks of expressed genes in the whole blood and relationships to profiles in circulating metabolites provide insights in inter-individual variability of feed efficiency in growing pigs. BMC Genomics 24, 647 (2023). https://doi.org/10.1186/s12864-023-09751-1