aPhyloGeo-Covid: A Web Interface for Reproducible Phylogeographic Analysis SARS-CoV-2 Variation using Neo4j and Snakemake Wanlin Li and Nadia Tahiri. Department of Computer Science, Université de Sherbrooke, Canada ABSTRACT DATA INTEGRATION GitHub Link https://github.com/tahiri- lab/iPhyloGeo/tree/iPhylooGeo-neo4j Tahiri Lab website https://tahirinadia.github.io/ WORKFLOW Acknowledgements RESULTS CONCLUSIONS REFERENCES Brister, J. Rodney, et al. (2015). NCBI viral genomes resource. Nucleic acids research, 43.D1, D571- D577. Mathieu, E. et al. (2021). A global database of COVID-19 vaccinations. Nature human behaviour, 5.7: 947-953. O‘Toole, Á. et al. (2021). Tracking the international spread of SARS-CoV-2 lineages B. 1.1. 7 and B. 1.351/501Y-V2 with grinch. Wellcome Open Research, 6 (2021). Tahiri (2012). Un nouvel algorithme pour retrouver les relations phylogénétiques entre la distribution géographique des espèces et leurs compositions génétiques. Ø This research developed an interactive analysis platform that facilitate efficient filtering and organization of input data for phylogeographical studies of SARS-CoV-2. Ø The Neo4j database was integrated as a comprehensive repository, consolidating COVID-19 pandemic-related sequence information, climate data, and demographic data obtained from public databases. Ø Additionally, the platform provides a scalable and reproducible phylogeographic workflow for investigating the intricate relationship between geographic features and the patterns of variation in diverse SARS-CoV-2 variants. Ø Database currently contains 113,774 nodes and 194,381 relationships. Ø The code is freely available to researchers and collaborators on GitHub. Neo4j Data Modeling facilitate • storage, • management • querying of extensive SARS-CoV-2 variants-related data. Neo4j Data Analytics ensure the repeatability and comparability of phylogeographic analysis results. Ø The sliding window region with the lower RF distance was exclusively identified in the integrated analysis. Ø Within the regions identified with lower RF distance, a special attention should be given to regions with positions between 792 to 940 residue. • The platform streamlines the extraction, validation, and integration of genetic and environmental data, overcoming the limitations of manual tools. • aPhyloGeo-Covid workflow utilizes a sliding window approach to identify region-specific subparts of viral genetic sequences affected by environmental conditions. • The application of Snakemake workflow management ensures that the phylogeografic analysis can be replicated, validated, and used as a reliable basis for further research or analysis. • The platform facilitates the sharing of research results, encourages collaboration and promotes the exploitation of previous work. Ø Putative horizontal gene transfer (HGT) events were detected in the window regions spanning residues 792-940 of the amino acid sequences of 14 SARS-CoV-2 variants. Source Figure 1: Schema of Neo4j Database for Phylogeographic Analysis of SARS-CoV-2 Variation Figure 2: The networks of a single analysis experiment Figure 3: Integrated analysis of genetic data and environmental data Figure 4: Snakemake workflow of the algorithm based on the new pipeline aPhyloGeo Figure 5: Robinson and Foulds topological distance normalized changes over the alignment windows Figure 6: Putative horizontal gene transfer events found for the window regions of 792-940 residue (amino acid sequences) of 14 SARS-Cov-2 variants • Multiple sequences are aligned and segmented into numerous alignment windows • Robinson and Foulds (RF) metric was employed to quantify the dissimilarity between o the phylogenetic tree of each window and o the topological tree of geographic features. Ø Tuning of window size and step size are required to optimize the analysis. Ø Reproducibility played a critical role in this process. • The network highlights all entities serving as input data sources and their relationships. •