Exploring neXtProt data and beyond: A SPARQLing solution
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neXtProt (www.nextprot.org) is a knowledge base for human proteins. At neXtProt, we use SPARQL, a semantic query language for databases to check, explore, and visualize our data. SPARQL queries are also used to check the quality and consistency of the data loaded at each release.
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Database Firewall from Scratch
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Molecular interactions. PSICQUIC and IntAct.
This document summarizes an bioinformatics workshop on enabling systems biology. It discusses various databases and standards for sharing molecular interaction data, including IntAct, UniProt, Reactome, PRIDE, and PSI formats. It also describes services for programmatically accessing this data, such as PSICQUIC and PSISCORE, which allow querying multiple source repositories using a common standard. Clients have been created to access these services and visualize interaction networks.
Как разработать DBFW с нуля
Докладчик расскажет о технических аспектах разработки с нуля прототипа межсетевого экрана уровня систем управления базами данных Database Firewall: о том, что нужно чтобы разработать DBFW, о возможности применения методов машинного обучения для эффективного обнаружения SQL-инъекций по SQL-запросам, обнаружении SQL-инъекций на основе методов синтаксического анализа, реализации ролевого и атрибутного управление доступом. Также речь пойдет о перспективных механизмах защиты приложений на основе технологий межсетевого экранирования и статического анализа кода.
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Instrumentation and measurement
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QuTrack: Model Life Cycle Management for AI and ML models using a Blockchain ...
As the complexity in AI and Machine Learning processes increases, robust data pipelines need to be
developed for industrial scale model development and deployment. . In regulated industries such as
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In this talk, we discuss QuTrack, a Blockchain-based approach to track experiment and model
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SMART Protocols
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Towards better software quality assurance by providing intelligent support
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Exploring neXtProt data and beyond: A SPARQLing solution
- 1. www.sib.swiss Exploring neXtProt data and beyond: A SPARQLing solution Biocuration 2019, Cambridge, UK Monique Zahn
- 2. Overview Exploring neXtProt data using SPARQL Biocuration and quality control Summary and future perspectives Introduction01 02 03 04
- 3. neXtProt – the SIB knowledgebase on human proteins https://www.nextprot.org/
- 5. A resource • ENTRIES have a globally unique and persistent ID • Use controlled vocabularies and ontologies • SPARQL end-point • Detailed provenance • Data license CC BY 4.0 • All data since 2011 on FTP • Code on GitHub • Web: human readable • API: machine parsable • FTP: XML, PEFF, RDF/ttl FINDABLE ACCESSIBLE INTEROPERABLE REUSABLE
- 6. Overview Exploring neXtProt data using SPARQL Biocuration and quality control Summary and future perspectives Introduction01 02 03 04
- 7. neXtProt Advanced Search Complex queries to retrieve entries matching criteria i.e. queries that cannot be performed using the simple search
- 8. Advanced Search results NXQ_00022 Proteins with no function annotated
- 9. neXtProt SNORQL Search Queries to retrieve entries and other information matching criteria
- 10. SNORQL Search results NXQ_00125 Domains that are entirely covered by 3D structures
- 11. Data visualization Use javascript to see SPARQL query results using Highcharts.com SPARQL query Retrieve results Plot results
- 12. Visualizing SPARQL query results Automatically updated at each data release
- 13. Overview Exploring neXtProt data using SPARQL Biocuration and quality control Summary and future perspectives Introduction01 02 03 04
- 14. SPARQL for biocuration Routine maintenance Updating controlled vocabularies Finding potential annotation targets NXQ_00022 Proteins with no function annotated NXQ_00125 Domains that are NOT entirely covered by 3D structures Tracking progress Number of entries with a GO molecular function Number of GO molecular function terms annotated
- 15. Updating controlled vocabularies SPARQL query to find protein domains relevant to no entry returns domains to be deleted
- 16. SPARQL for quality control Examples for spot checks Entry with the most isoforms (CACNA1C NX_Q13936 37 isoforms) Entry with the most variants (TTN NX_Q8WZ42 24,240 variants) Global checks Find data which is: • missing • incorrect • incomplete
- 17. Automated quality control (1)
- 18. Automated quality control (2) SPARQL queries must return 0 (rows) if there is no error Execute a batch of queries either on the test or production server There are 4 protein domains with no associated entry to be deleted.
- 19. Automated quality control (3) There are 10 sequence conflict annotations for which the quality has been incorrectly assigned.
- 20. Overview Exploring neXtProt data using SPARQL Biocuration and quality control Summary and future perspectives Introduction01 02 03 04
- 21. Summary and future perspectives Exploration Execute complex queries Query several database endpoints (federated queries) Biocuration Quality control User guide to using SPARQL in neXtProt Dashboard Visual detection of anomalies
- 22. Acknowledgements neXtProt Directors Amos Bairoch, Lydie Lane Biocurators Pascale Gaudet, Paula Duek Developers Pierre-André Michel, Alain Gateau, Frédéric Nikitin Researcher Mathieu Schaeffer (poster 80), Kasun Samarasinghe Quality assurance Monique Zahn (poster 92) Web: https://www.nextprot.org/ Twitter: @neXtProt_news ResearchGate: neXtProt project Photograph taken by Chris James