Reactome modified for tracing ArangoDB version

<h3>Reactome database download and customization</h3><p>The Reactome database [1,2] was downloaded as a neo4j graph database (<a href="https://reactome.org/download-data">https://reactome.org/download-data</a> version 75), which is covered by the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons&nbsp;Attribution 4.0 International (CC BY 4.0)</a> license.&nbsp;A series of database queries was used to generate a database version suitable for graph data science which can be followed in detail in the attached Jupyter notebook (Or at <a href="https://github.com/SBRG/GDS-Public/blob/main/notebooks/reactome/Reactome%20GDS.ipynb">GDS-Public/notebooks/reactome/Reactome GDS.ipynb at main · SBRG/GDS-Public (github.com)</a>).&nbsp;</p><p>Nodes, labels and relationships not required for graph algorithmic analyses were removed. For instance, this included nodes like person, affiliation, and taxa as well as all nodes representing entities of organisms other than <i>Homo sapiens</i>. Subcellular locations (compartments) of biological entities were set as node properties. To allow for improved graph traversal, selected relationships were reversed or added. Because currency metabolites, e.g. ATP, NAD(P)H and H+, can artificially connect metabolic reactions and pathways in network analyses [3,4], we labelled such compounds plus the regulatory protein ubiquitin accordingly and thereby excluded them from all analyses. Finally, the database was transformed into an ArangoDB&nbsp;graph database consisting of 1,703,054 nodes and 3,368,926 edges.&nbsp;</p><h3>References</h3><p>1. &nbsp;Gillespie, M. <i>et al.</i> The reactome pathway knowledgebase 2022. <i>Nucleic Acids Research</i> <strong>50</strong>, D687–D692 (2022).</p><p>2. &nbsp;Fabregat, A. <i>et al.</i> Reactome graph database: Efficient access to complex pathway data. <i>PLoS Computational Biology</i> <strong>14</strong>, (2018).</p><p>3.&nbsp;&nbsp;Ma, H. &amp; Zeng, A.-P. <i>Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms</i>. <i>BIOINFORMATICS</i> vol. 19 https://academic.oup.com/bioinformatics/article/19/2/270/372721 (2003).</p><p>4.&nbsp;&nbsp;Martínez, V. S. <i>et al.</i> The topology of genome-scale metabolic reconstructions unravels independent modules and high network flexibility. <i>PLoS Computational Biology</i> <strong>18</strong>, (2022).</p><p>&nbsp;</p>