STON.pdf

<b>STON, SBGN to Neo4j: using graph database technologies for storing disease-relevant biological pathways and networks</b> <p> Vasundra Touré¹, Alexander Mazein², Dagmar Waltemath¹, Irina Balaur², Ron Henkel¹, Mansoor Saqi², Johann Pellet² and Charles Auffray²</p> <p> <br> </p> <p> ¹Department of Systems Biology and Bioinformatics, University of Rostock, 18051 Rostock, Germany.</p> <p> ²European Institute for Systems Biology and Medicine (EISBM), Centre National de la Recherche Scientifique (CNRS), Campus Charles Mérieux - Université de Lyon - 50 Avenue Tony Garnier, 69007 Lyon, France; IMI-eTRIKS consortium.</p> <p> <br> </p> <p> Abstract <br></p> <p> <br> </p> <p> <b>Background: </b>Graph databases can be successfully applied in Systems Biology and in Systems Medicine for managing extensive and complex information. Ultimately, graphs are a natural way of representing biological networks. The use of graph databases enables efficient storing and processing of biological relationships, and it can lead to a better response time when querying the data.</p> <p> <br> </p> <p> <b>Objectives: </b>We would like to use graph databases structure to store and explore biological pathways and networks.</p> <p> <br> </p> <p> <b>Method:</b> Translation rules have been determined to represent biological reaction networks in a graph model, that is to say as nodes, relationships and properties. The reaction networks are provided in the graphical standard Systems Biology Graphical Notation (SBGN). The graph model is stored in a Neo4j database.</p> <p> <br> </p> <p> <b>Results: </b>We present the Java-based framework STON (SBGN TO Neo4j) to import and translate metabolic, signalling and gene regulatory pathways. On the poster, we show examples of networks representing parts of the Asthma Map, the iNOS pathway (a SBGN use case network).</p> <p> <br> </p> <p> <b>Conclusion: </b>STON exploits the power of a graph database for the search in complex biological pathways. Importing biological pathways in a graph database enables:</p> <p> 1) identification of functional sub-modules and comparing different networks in order to discover common patterns. </p> <p> 2) merging multiple diagrams for creating large comprehensive networks for empowering systems medicine approaches.</p> <p> <br> </p> <p> <b>Availability:</b> The STON framework is available here: <a href="http://sourceforge.net/projects/ston/">http</a><a href="http://sourceforge.net/projects/ston/">://</a><a href="http://sourceforge.net/projects/ston/">sourceforge</a><a href="http://sourceforge.net/projects/ston/">.</a><a href="http://sourceforge.net/projects/ston/">net</a><a href="http://sourceforge.net/projects/ston/">/</a><a href="http://sourceforge.net/projects/ston/">projects</a><a href="http://sourceforge.net/projects/ston/">/</a><a href="http://sourceforge.net/projects/ston/">ston</a><a href="http://sourceforge.net/projects/ston/">/</a>.</p> <p><br> </p

Additional file 4 of STON: exploring biological pathways using the SBGN standard and graph databases

SBGN files in a COMBINE Archive. This COMBINE Archive contains the five SBGN-ML files used to generate the benchmark table present in the Additional file 1. (OMEX 181 kb

Additional file 3: of A computational framework for complex disease stratification from multiple large-scale datasets

Table S7. Estimated accuracy and standard deviation of the RFE procedure. Table S8. Accuracy and Kappa values of the Random Forest models in the training set. Table S9. Performances values for the Random Forest model in the testing set. Figure S11. Relative importance of the top 20 predictors building the final model of the RF. The importance axis is scaled, with the mRNA expression of CD3D scaled to 100% and the methylation state of POLA2 to 0% (not shown). (DOCX 18 kb

Additional file 4: of A computational framework for complex disease stratification from multiple large-scale datasets

DIABLO sPLSDA model results. (DOCX 18966 kb

DataSheet_1_Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.xlsx

IntroductionThe COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. MethodsExtensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.ResultsResults revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. DiscussionThe key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.</p

DataSheet_2_Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.pdf

IntroductionThe COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. MethodsExtensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.ResultsResults revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. DiscussionThe key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.</p