Using Grakn to Analyse Protein Sequence Alignment
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Cognitive and AI applications consume data that is far too complex for current databases. These systems require an expressive data model and an intelligent query language to perform knowledge engineering over complex datasets. GRAKN.AI is a database to organise such complex networks of data. Systems biology is one of the domains that produces huge amounts of data which presents integration challenges due to their complex nature. As understanding the complex relationships among these biological data is one of the key goals in biology, solutions are necessary that speed up the integration and querying of such data. However, analysing large volumes of this biological data through traditional database systems is troublesome and challenging. In this talk, we will demonstrate how integrating a sequencing algorithm with a Grakn knowledge graph leads to valuable new insights of our data at scale.
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Using Grakn to Analyse Protein Sequence Alignment
- 1. T H E K N O W L E D G E G R A P H Join our community at grakn.ai/community Using Grakn in Protein Sequence Alignment By Tomas Sabat COO of GRAKN.AI @graknlabs @tasabat
- 2. Follow us @GraknLabs We push the boundary of intelligent systems forward, starting at the database.
- 3. Follow us @GraknLabs Integrating datasets Contextualisation of insights Discovery of new relationships Difficult to integrate heterogeneous and flat datasets Difficult to ingest insights and connect with rest of public/private data Difficult to reason and discover new explainable relationships What problems have we found in bioinformatics?
- 4. Follow us @GraknLabs Grakn accelerates biomedical knowledge discovery Faster ingestion and integration of data Contextualise newly generated insights Discover and explain new relationships Grakn’s hyper-relational data model enables fast and flexible integration of heterogeneous biomedical data sets Bring context to newly generated insights by understanding how it interacts with the rest of the graph Through automated deductive reasoning of data points derive new relationships between biological components
- 5. Follow us @GraknLabs GRAKN.AI the knowledge base foundation for intelligent systems a.k.a., GRAKN.AI is a knowledge graph Knowledge Storage System Novel Knowledge Representation System based on Hypergraph Theory Knowledge Inference OLTP Reasoning Engine Knowledge Analytics OLAP Distributed Analytics
- 6. Follow us @GraknLabs 1. How do we integrate biomedical data?
- 7. Follow us @GraknLabs Integrating data Protein
- 8. Follow us @GraknLabs Integrating data Protein Kinase sub Ion -channel Nuclear- receptor GPCR sub sub sub
- 9. Follow us @GraknLabs Integrating data Drug Disease Protein Gene Kinase sub Ion -channel Nuclear- receptor GPCR sub sub sub
- 10. Follow us @GraknLabs Integrating data Drug Disease Protein Gene gene- protein- encoding encoding-gene encoded-protein Kinase sub Ion -channel Nuclear- receptor GPCR sub sub sub
- 11. Follow us @GraknLabs Integrating data Protein Drug DiseaseGene encoding-gene encoded-protein protein- disease- associati on associated-protein associated-disease associated-diseaseassociated-gene drug- protein- interaction target-protein interacted-drug drug-gene- interaction inhibitor target-gene drug-- disease- association affected-disease therapeutic gene- protein- encoding gene- disease- association Kinase sub Ion -channel Nuclear- receptor GPCR sub sub sub
- 12. Follow us @GraknLabs Hyper-Relationship Example: Nested-Relationship Protein Protein Tissue PPI loc interacting interacting data source has
- 13. Follow us @GraknLabs Hyper-Relationship Example: Ternary-Relationship Drug Disease Side effect therapeutic effect ass
- 14. Follow us @GraknLabs 2. How do we ingest and contextualise sequencing data?
- 15. Follow us @GraknLabs How do we ingest and contextualise sequencing data? Protein Drug Disease Gene encoding-gene encoded-protein protein- disease- associati on associated- protein associated-disease associated-disease associated-gene drug- protein- interaction target-protein interacted-drug drug-gene- interaction inhibitor target-gene drug- disease- association affected-disease therapeutic gene- protein- encoding gene- disease- association Kinase sub Ion -channel Nuclear- receptor GPCR sub sub sub
- 16. Follow us @GraknLabs How do we ingest and contextualise sequencing data? Protein Drug Disease Gene protein- protein- alignment target-protein matched-protein encoding-gene encoded-protein protein- disease- associati on associated- protein associated-disease associated-disease associated-gene drug- protein- interaction target-protein interacted-drug drug-gene- interaction inhibitor target-gene drug- disease- association affected-disease therapeutic gene- protein- encoding gene- disease- association sequence Kinase sub Ion -channel Nuclear- receptor GPCR sub sub sub
- 17. Follow us @GraknLabs How do we ingest and contextualise sequencing data? Protein Drug Disease Gene protein- protein- alignment target-protein matched-protein encoding-gene encoded-protein protein- disease- associati on associated- protein associated-disease associated-disease associated-gene drug- protein- interaction target-protein interacted-drug drug-gene- interaction inhibitor target-gene drug- disease- association affected-disease therapeutic gene- protein- encoding gene- disease- association sequenceSequence- sequence- alignment target-seq matched-seq Kinase sub Ion -channel Nuclear- receptor GPCR sub sub sub
- 18. Follow us @GraknLabs How do we ingest and contextualise sequencing data? Protein Drug Disease Gene protein- protein- alignment target-protein matched-protein encoding-gene encoded-protein protein- disease- associati on associated- protein associated-disease associated-disease associated-gene drug- protein- interaction target-protein interacted-drug drug-gene- interaction inhibitor target-gene drug- disease- association affected-disease therapeutic gene- protein- encoding gene- disease- association sequenceSequence- sequence- alignment target-seq matched-seq sequence- positivity sequence- identicality Kinase sub Ion -channel Nuclear- receptor GPCR sub sub sub
- 19. Follow us @GraknLabs 3. How do we discover and explain new relationships?
- 20. Follow us @GraknLabs How do we discover new relationships? Protein Drug Disease Gene protein- protein- alignment target-protein matched-protein encoding-gene encoded-protein protein- disease- associati on associated- protein associated-disease associated-disease associated-gene drug- protein- interaction target-protein interacted-drug drug-gene- interaction inhibitor target-gene drug- disease- association affected-disease therapeutic gene- protein- encoding gene- disease- association sequenceSequence- sequence- alignment target-seq matched-seq sequence- positivity sequence- identicality Kinase sub Ion -channel Nuclear- receptor GPCR sub sub sub
- 21. Follow us @GraknLabs Complex Query Example Asthma Disease Gene Nuclear Receptor Kinase Ion channel GPCR Nuclear Receptor Kinase Ion channel GPCR Gene Drug seq seq seq seq seq seq seq seq ass ass ass ass ass ass ass ass enc enc enc enc What drugs are associated to “Asthma”? align id pos
- 22. Follow us @GraknLabs align Complex Query Example Asthma Disease Gene Nuclear Receptor Kinase Ion channel GPCR Nuclear Receptor Kinase Ion channel GPCR Gene Drug seq seq align seq seq seq seq seq seq ass ass ass ass ass ass ass ass enc enc enc enc What drugs are associated to “Asthma”? id pos
- 23. Follow us @GraknLabs Rule Example: Transitive Relationship Protein Protein align Drug ass ass Disease ass
- 24. Follow us @GraknLabs Rule Example: Transitive Relationship Protein Protein align Drug ass ass Disease ass
- 25. Follow us @GraknLabs Rule Example: Chained Rules Protein sequence- alignment Protein sequence sequence protein- alignment seq-pos seq-id >0.9 >0.9
- 26. Follow us @GraknLabs Rule Example: Chained Rules Protein sequence- alignment Protein sequence sequence protein- alignment seq-pos seq-id >0.9 >0.9
- 27. Follow us @GraknLabs 1. How do we integrate so much biomedical data? 2. How do we ingest and contextualise sequencing data? 3. How do we discover new relationships?
- 28. Follow us @GraknLabs Grakn accelerates biomedical knowledge discovery Faster ingestion and integration of data Contextualise newly generated insights Discover and explain new relationships Grakn’s hyper-relational data model enables fast and flexible integration of heterogeneous biomedical data sets Bring context to newly generated insights by understanding how it interacts with the rest of the graph Through automated deductive reasoning of data points derive new relationships between biological components
- 29. T H E K N O W L E D G E G R A P H Join our community at grakn.ai/community Using Grakn in Protein Sequence Alignment By Tomas Sabat COO of GRAKN.AI @graknlabs @tasabat