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6 results for “Reactome”
Reactome
<p>The Reactome dataset originates from the website https://reactome.org/ where it can be downloaded as Neo4j graph. This graph has been subsequently imported to Neo4j and in turn exported as RDF dump file in Turtle format which is now hosted on this site. The dataset is not yet RDFS-entailed and it utilizes the "http://dev.neo4j.owl.de/" base URI instead of "http://reacome.org/".</p>
VIGET: A web portal for study of vaccine-induced host responses based on Reactome pathways and ImmPort data
<p>Host responses to vaccines are complex but important to investigate. To facilitate the study, we have developed a tool called Vaccine Induced Gene Expression Analysis Tool (VIGET), with the aim to provide an interactive online tool for users to efficiently and robustly analyze the host immune response gene expression data collected in the ImmPort database. VIGET allows users to select vaccines, choose ImmPort studies, set up analysis models by choosing confounding variables and two groups of samples having different vaccination times, and then perform differential expression analysis to select genes for pathway enrichment analysis and functional interaction network construction using the Reactome’s web services. VIGET provides features for users to compare results from two analyses, facilitating comparative response analysis across different demographic groups. VIGET uses the Vaccine Ontology (VO) to classify various types of vaccines such as live or inactivated flu vaccines, yellow fever vaccines, etc. Different variables are classified using our Vaccine Investigation Ontology (VIO). To showcase the utilities of VIGET, we conducted a longitudinal analysis of immune responses to yellow fever vaccines and found an intriguing complex activity response pattern of pathways in the immune system annotated in Reactome, demonstrating that VIGET is a valuable web portal that supports effective vaccine response studies using Reactome pathways and ImmPort data. The portal has been deployed at <a href="https://viget.violinet.org/">https://viget.violinet.org/</a>.</p>
Reactome HSA v71 pathways and hierarchy
<p>Originally from:<strong> https://reactome.org/download-data</strong><br> Version 71: <strong>https://reactome.org/about/news/145-version-71-releases</strong></p> <p>What was done here:<br> Top pathways found in <strong>https://reactome.org/PathwayBrowser/</strong> were manually added as children to the parent R-HSA-0000000 in the file "NewestReactomeNodeRelations.txt". These top pathway descriptions were also added to the file "Top_Human_REACTOME_Nodes_ManualCuration.txt"</p> <p>The rest of the pathway parent child relationships were taken from:<br> <strong>https://reactome.org/download/current/ReactomePathwaysRelation.txt</strong><br> but only the R-HSA pathways were used and added to the file "NewestReactomeNodeRelations.txt"</p> <p>The pathway definitions are the ones found in<br> "Ensembl2Reactome_All_Levels_v71.txt"<br> </p>
Reactome COVID-19: the literature curation strategy
<p>ABSTRACT</p> <p>In response to the deluge of COVID-19-related publications Reactome developed a computational triaging strategy to review and identify publications appropriate for manual curation (66,100 SARS-Cov-2 articles on PUBMED, tallied on 30/October/2020 https://www.ncbi.nlm.nih.gov/research/coronavirus/; Chen et al., 2020). </p> <p>The literature triaging approach consisted of 4 main steps: 1) Literature screening; 2) Literature selection; 3) Reference tagging; and 4) Reference database construction. Two primary reference databases were downloaded and automatically text-mined: CDC COVID-19 downloadable database and bioRxiv database. Other collections of SARS-Cov-2 literature were manually screened with a focus on molecular interactions. These included: a Zotero Library built and updated by members of COVID-19 Disease Map (Ostaszewski et al., 2020); CORD-19 (Wang et al., 2020); LitCOVID (Chen et al., 2020); Johns Hopkins literature summary; Cell Press Coronavirus Resource Hub; Nature Coronavirus and COVID-19 updates; and Science’s Latest Coronavirus research. </p> <p>About 5% of the articles made it through reference screening focused on Reactome SARS-CoV-2 map construction to the literature selection step. If relevance was confirmed, the reference was then tagged in step 3. SARS-CoV-2 selected references were tagged regarding multiple features: (i) type of publication (e.g., article, review, pre-print, comment); (ii) Virus and host species (e.g., SARS-CoV-2, SARS-CoV-1; MERS, ACE2); (iii) Entity (specific molecules studied); (iv) Methods (e.g., Cryo-EM, ELISA, IC50); (v) Cell line and/or Tissue (e.g., vero-E6, lung tissue); (vi) subcellular localization (e.g., plasma membrane, ER); (vii) Molecular event (e.g., virus cycle step, pathway, host response); and (viii) Phenotype (e.g., immune, coagulation). Features i, ii, iii, vii and viii were mandatory. This Reference Database is stored in a shared spreadsheet, in which Reactome team members can edit and refine the Library (e.g., inclusion of tags). </p> <p> As Reactome is an evidence-based database built on reliable experimental published data, the process of SARS-CoV-2 reference selection is stringent and prioritizes peer-reviewed references. Nevertheless, the final decision on the reliability of the scientific evidence to support a molecular interaction was made by Reactome curators. In this case the focused literature triaging provides curators with a deeply researched trove of articles.</p> <p> The Reactome strategy of first creating an individual map for SARS-CoV-1 supported and guided the construction of a refined SARS-CoV-2 map. The SARS-CoV-2 Reactome map is an ongoing task, built on the foundation of a strong literature curation strategy. Together the literature triage and curatorial groups have built open-source COVID-19 viral infection pathways incorporating rapid scientific development and literature availability.</p> <p> </p> <p>References.</p> <p> </p> <p>Chen, Q., Allot A., Lu Z. Keep up with the latest coronavirus research. Nature 579, 193 (2020). doi: 10.1038/d41586-020-00694-1 </p> <p> </p> <p>Ostaszewski, M., Mazein, A., Gillespie, M.E. et al. COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms. Sci Data 7, 136 (2020).<a href="https://doi.org/10.1038/s41597-020-0477-8"> https://doi.org/10.1038/s41597-020-0477-8</a></p> <p> </p> <p>Wang, L., Lo K, Chandrasekhar, Y., et al. CORD-19: The Covid-19 Open Research Dataset. Preprint. ArXiv. 2020;arXiv:2004.10706v2. Published 2020 Apr 22.CORD-19.<a href="https://covidsearch.sinequa.com/app/covid-search/#/home"> https://covidsearch.sinequa.com/app/covid-search/#/home</a></p> <p> </p> <p>Reference Databases: CDC Covid-19 Database (<a href="https://www.cdc.gov/library/researchguides/2019novelcoronavirus/researcharticles.html">https://www.cdc.gov/library/researchguides/2019novelcoronavirus/researcharticles.html</a>); bioRxiv database (<a href="https://www.biorxiv.org/about-biorxiv">https://www.biorxiv.org/about-biorxiv</a>); Johns Hopkins literature summary (<a href="https://ncrc.jhsph.edu/topics/">https://ncrc.jhsph.edu/topics/</a>); Cell Press Coronavirus Resource Hub (<a href="https://www.cell.com/COVID-19">https://www.cell.com/COVID-19</a>); Nature Coronavirus and COVID-19 updates (<a href="https://www.nature.com/collections/aijdgieecb">https://www.nature.com/collections/aijdgieecb</a>); Science’s Latest Coronavirus research (<a href="https://www.sciencemag.org/collections/coronavirus?IntCmp=coronavirussiderail-128">https://www.sciencemag.org/collections/coronavirus?IntCmp=coronavirussiderail-128</a>).</p> <p> </p>
Reactome Data 86 - Neo4j & MySQL
Open the record for dataset details and reuse information.
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 Attribution 4.0 International (CC BY 4.0)</a> license. 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>). </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 graph database consisting of 1,703,054 nodes and 3,368,926 edges. </p><h3>References</h3><p>1. 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. 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. Ma, H. & 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. 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> </p>
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