Introducing the KnetMiner Knowledge Graph: things, not strings
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Rothamsted Seminar Series by Keywan Hassani-Pak, 1 April 2019 Researchers at Rothamsted and around the world are working to push the boundaries of human knowledge. One would think they have access to the best available tools to help them in their quest for knowledge. In reality the opposite is often true: the research tools at our disposal are only substandard and therefore searching and discovering new biological clues still requires a lot of hard work. We have developed an intelligent data model, known as the KnetMiner Knowledge Graph, that helps researchers to discover new information quickly and easily. Knowledge graphs are commonly used to represent biological entities and their relationships to one another: i.e. things, not strings. Our wheat Knowledge Graph, for example, currently contains more than 1.5 million objects and 6 million facts about, and relations between, these different objects. KnetMiner (www.knetminer.org) enables you to search the Knowledge Graph for genes, phenotypes, diseases, stresses, molecules and more - and instantly tell you the stories of complex traits.
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Introducing the KnetMiner Knowledge Graph: things, not strings
- 1. Introducing the KnetMiner Knowledge Graph: things, not strings. Keywan Hassani-Pak Head of Bioinformatics 1 April 2019 – Rothamsted Seminar
- 2. Outline
- 3. Biological Knowledge Discovery Unravelling the biological story behind complex traits
- 4. Genes are rarely single actors To explain the metaphor • Unravelling the roles of genes in complex trait genomics is often similar to unmasking the heroes and villains in a plant biology who-dunnit • Multiple genes are generally involved and play different roles. How they interact reveals the plotline(s) in the trait story • We build software and data resources to help automate the process of explaining the plotlines and unravelling the story behind complex trait biology • We are creating computational tools that bring different types of evidence together and then to weigh-up the competing story lines to present the user with the most compelling ones.
- 5. Assembling the evidence – data is just the beginning! Genetic diversity Sequencing data Phenotype data Literature Pathways Ontologies Better yields NUE Disease Resistance Reference genomes Gene expression QTL Data Information Knowledge Understanding We need all of these components to come together to understand our traits
- 6. KnetMiner – Accelerating biological discovery http://knetminer.rothamsted.ac.uk/ @KnetMiner • Gene Networks • Bio Databases • Data Integration • Text Mining • Visualisation • AI & Graphs • RDF & Neo4j • Java & JavaScript
- 7. How does KnetMiner work? Computer says: “Try this”
- 8. The Rise of Graph Analytics From Leonhard Euler to Google’s Knowledge Graphs
- 9. Seven Bridges of Königsberg - a historically notable problem in mathematics Its resolution by Leonhard Euler in 1736 laid the foundations of graph theory Leonhard Euler - 1736
- 10. I N T E R A C TO M E N E T WO R K G R A P H
- 11. Pathfinding Finds the shortest path or evaluates route availability and quality Centrality Determines the importance of distinct nodes in the network Community Detection Evaluates how a group is clustered or partitioned Graph Algorithms
- 12. North WykeHarpenden distance to Rothamsted Research Research Institute Site Site miles: 230 What is a Knowledge Graph? year: 1843 1843 Year founded in
- 13. Knowledge Graph: things, not strings
- 15. KnetMiner Knowledge Graph A Knowledge Graph for gene mining and biological discovery
- 17. Where do KnetMiner relations come from? Some examples:
- 18. Example - GWAS data http://plants.ensembl.org/biomart/martview Example Arabidopsis #SNP=66,816 | #Gene=27,502 | #Phenotype=107
- 19. … transform into a graph (SNP) (Trait) associated Using Ondex-Knet-Builder https://github.com/Rothamsted/ondex-knet-builder
- 20. Add Biological interaction datasets http://thebiogrid.org
- 21. (SNP) (Tait) associated … add biological interactions
- 22. Towards FAIR KnetMiner Knowledge Graphs • Green: Ondex plug-ins • rdf2neo is a generic, non Ondex-specific rdf->Neo4j conversion tool • Brandizi et al., IB-2018 (https://dx.doi.org/10.1515%2Fjib-2018-0023) • Brandizi et al., SWAT4LS-2018 (https://doi.org/10.6084/m9.figshare.7314323.v1)
- 23. Programmatic Access via Graph Query Languages MATCH // branching via ‘|’ (prot:Protein) - [:produced_by|consumed_by] -> (:Reaction) // variable-length chains - [:part_of*1..3] -> (pway:Path) RETURN prot.name, pwy LIMIT 1000 // Very compact forms available: MATCH (prot:Protein) - (pway:Path) RETURN pway • RDF + OWL used as a standardised modelling/representation language (see BioKNO ontology: github.com/Rothamsted/bioknet-onto) • SPARQL available too, both having pros/cons (see our benchmark: github.com/Rothamsted/graphdb-benchmarks) • Cypher being used for KnetMiner queries (work in progress)
- 24. Open Source Code client web-server knet-builder Deployment Model Data Integration Workflows Database Service Model Private data Public data Databases and graph queries required by KnetMiner For a species or domain of interest Graph Queries Using Knet-Builder tools
- 25. How to search and interpret so much information? • Methods needed to evaluate millions of relationships in knowledge graph, prioritize genes and extract relevant subgraphs • Interactive and exploratory tools needed to enable knowledge discovery • Interpretation should be the task of domain experts i.e. biologists!
- 26. KnetMiner Use Cases Visualising data connections promises faster discoveries
- 27. What does KnetMiner know about your gene? Reverse genetics applications: gene to phenotype
- 29. Wheat Arabidopsis What happens if we knockout TT2 (R Myb) in wheat? With Andy Phillips
- 30. What KnetMiner knows about TT2 (R Myb) TT2 (R Myb) on chromosome 3D in wheat is predicted (p-value=0.01) to regulate the transcriptional activation of MFT according to data from the analysis of 850 RNA-seq samples in wheat (Ramírez-González et al. 2018) using GENIE3 (Huynh-Thu et al. 2010). The TT2 3B homoeolog is not predicted to regulate MFT, and the TT2 3A homoeolog is not annotated in the latest version of the wheat genome. MFT has been recently linked to grain germination [(Zong Y ; Li Q ): “Recent studies in both Arabidopsis and wheat have uncovered a new role of MOTHER OF FT AND TFL1 (MFT) in seed germination”] and seed dormancy [(Nakamura S ): “Mapping analysis showed that MFT on chromosome 3A (MFT-3A) colocalized with the seed dormancy quantitative trait locus (QTL) QPhs.ocs-3A.”]. The MFT ortholog in Arabidopsis has a 3’ UTR variant that has been associated with (p- value = 5.5x10-5) increased germination rate after 56 days of dry storage (Atwell et al. 2010).
- 31. DFW Nov 2018 Visualising connections can lead to new lines of inquiries • Can grain colour and PHS be linked because R Myb targets the grain germination gene MFT? • Do white grain varieties (R Myb mutants) have increased root hair density? • Is there a link between root hair density and PHS?
- 32. What does KnetMiner know about your trait? Forward genetics applications: phenotype to gene
- 34. https://aragwas.1001genomes.org P-value: 7.7E-7 Distance: 285bp Identity: 81% PPI: Two-Hybrid ortholog Arabidopsis Wheat http://knetminer.rothamsted.ac.uk/Triticum_aestivum/
- 35. IRRI Germplasm Acquisition of early seedling traits and image processing GWAS Gene discovery related to seed vigour Guillaume Menard Smita Kurup Peter Eastmond Kirsty Hassall David Hughes Colin Li Direct Seeded Rice Improve seedling establishment, emergence and vigour http://knetminer.rothamsted.ac.uk/Oryza_sativa/
- 36. PMID: 16377752
- 37. Future Work Growing and Learning the Knowledge Graph
- 38. Growing the KnetMiner Knowledge Graph
- 39. Work in progress
- 40. Future development • Personalised search experience • Save and share your networks • Like and Dislike buttons on relations • Personalised networks based on usage data • Better knowledge visualisation • Reduce information overload using network clustering • Annotate nodes with quantitative user data • Predictive network analysis • Find research trends using publication and graph data • Automatic story and hypotheses generation
- 41. Future vision – Automatic story generation TT2 (R Myb) on chromosome 3D in wheat is predicted (p-value=0.01) to regulate the transcriptional activation of MFT according to data from the analysis of 850 RNA-seq samples in wheat (Ramírez-González et al. 2018) using GENIE3 (Huynh-Thu et al. 2010). The TT2 3B homeologue is not predicted to regulate MFT, and the TT2 3A homeologue is not annotated in the latest version of the wheat genome. MFT has been recently linked to grain germination [(Zong Y ; Li Q ): “Recent studies in both Arabidopsis and wheat have uncovered a new role of MOTHER OF FT AND TFL1 (MFT) in seed germination”] and seed dormancy [(Nakamura S ): “Mapping analysis showed that MFT on chromosome 3A (MFT-3A) colocalized with the seed dormancy quantitative trait locus (QTL) QPhs.ocs-3A.”]. The MFT ortholog in Arabidopsis has a 3’ UTR variant that has been associated with (p-value = 5.5x10-5) increased germination rate after 56 days of dry storage (Atwell et al. 2010). • Well structured semantics of entities and relationships in the network • Maintaining provenance of derived relations • Confidence values on relations • Supporting literature
- 42. KnetMiner Impact • Over 1800 unique users last year • 68% of users from non-UK countries • KnetMiner part of 3 grants recently submitted to BBSRC (BBR, sLoLa) • KnetMiner code is open-source; v3.0 released in Feb 2019 • Developers are starting to contribute to our open-source tools • Resources are starting to link into KnetMiner (eg. WheatIS, T3, Ensembl) • Requests to build KnetMiner for new species (eg. Sugarcane, honey bee) • Invited to run training courses and workshops (eg. EBI, PAG, IB2018) • KnetMiner used in two agrifood companies, several companies have expressed interest
- 43. The right data in the right format and in the right hands at the right time, saves lives. #us2ts2019 @hdeus
- 44. Acknowledgements (strings - for humans only) Bioinformatics Lab Ajit Singh Marco Brandizi Sandeep Amberkar Emma Bailey Dan Smith David Hughes Rob King Colin Li Chris Rawlings William Brown (ITS) Follow us on Twitter: @KnetMiner Collaborators & Contributors Richard Holland (NFVL) Misha Kapushesky (Genestack) Kevin Dialdestoro (Genestack) Vasiliki Koutra (KCL) Martin Castellote (INTA) Philipp Bayer (UWA) Jean-Luc Jannink (Cornell) Clay Birkett (Cornell) Cyril Pommier (INRA) Ramil Mauleon (IRRI) Jan Taubert (KWS) Uwe Scholz (IPK) Matthias Lange (IPK) Kumar Saurabh Singh (Exeter) Monika Mistry DFW WP4 members Data Contributors Clay Birkett (Cornell) Cristobal Uauy (JIC) Philippa Borrill (JIC) Andrea Bräutigam (Uni Bielefeld) Philipp Bayer (UWA) Ramil Mauleon (IRRI) Funding Designing Future Wheat (BBSRC) Pest Genomics Initiative Users Andy Phillips (RRes) Rowan Mitchell (RRes) Steve Hanley (RRes) Richard Barker (NASA)
- 45. Acknowledgements (things - for machines &humans) www.knetminer.org Twitter: @KnetMiner
Notas do Editor
- Good afternoon. Thank you for the kind introduction. Coincidently, I’ve been with Rothamsted 11-years today. I joined the BAB department in 2008, and now as Head of Bioinformatics, my team and I have worked and collaborated with many of you. Researchers at Rothamsted and around the world are working to push the boundaries of human knowledge and tackle key challenges like sustainable agriculture, climate change and food security. You would think they have access to the best available tools to help them in their quest for knowledge. In reality the opposite is often true: the research tools at our disposal are only substandard - finding and accessing relevant information can be extremely tedious. In this seminar, I am going to present how we developed an intelligent data model, known as the KnetMiner Knowledge Graph, that helps you as a researcher to discover new information quickly and easily.
- My talk is structured in 5 sections What do we understand with biological knowledge discovery? What are graphs and how are they useful? How do we build the KnetMiner knowledge graph? Finally I will show some use cases and discuss future work.
- There are many types of biological knowledge discovery, e.g. Drug discovery, trait discovery or gene discovery. Today I will mostly focus on gene discovery, so what is gene discovery?
- Let me use the metaphor “Genes are rarely single actors” to explain what we mean with gene discovery.
- Assembling the evidence is a tedious job for humans. Machines can help but face challenges. Let’s review the type of evidences needed to go from data to understanding. Clifford Stoll, an American Astronomer, said: “Data is not information, [...]” Data comes in many formats Excel spreadsheets, FASTA, GFF, Tabular, XML and you name it. FAIR. Information is in many locations and interconnected Over 1600 biological databases (NAR Database Issue 2019) Knowledge can be buried in free text or in structured form (like ontologies) Over 29 million articles (PubMed) - Over 750 biomedical ontologies (BioPortal) Ontologies are structured keyword lists that life scientists have created to define and connect concepts in their subject areas. Understanding needs intelligence Human vs artificial If all this layers are structured such that machines can understand what we mean (FAIR), then machines can play a crucial role in helping us to understand traits.
- KnetMiner, with a silent "K" and standing for Knowledge Network Miner, is a web-app that makes biological knowledge discovery faster and fun. It is used by researchers at Rothamsted and around the world, 68% of users are from outside of UK Ajit Singh works on the front-end and usability of KnetMiner. Marco Brandizi works on the back-end technologies and interoperability of KnetMiner.
- KnetMiner is NOT a black box. It is powered by a large integrated knowledge graph and graph analytics algorithms to search and visualise knowledge.
- Graph analytics has a history dating back to 1736, when Leonhard Euler solved the “Seven Bridges of Königsberg” problem.
- The problem asked whether it was possible to visit all four areas of a city, connected by seven bridges, while only crossing each bridge once. With the insight that only the connections themselves were relevant, Euler set the groundwork for graph theory and its mathematics. Euler modelled the problem as a graph of 4 x nodes and 7 x edges Euler proved that in any graph such walks are only possible if exactly zero or two nodes have an odd number of edges. The four areas (nodes) of Königsberg however all had odd number of bridges (edges). So it wasn’t possible! But graph analytics did not catch on immediately. Two hundred years would pass before the first graph textbook was published in 1936. In the late 1960s and 1970s, network science and applied graph analytics really began to emerge. In the last few years, there’s been an explosion of interest in graph technologies. In 2017, a survey (Forrester data) indicated that “69% of enterprises have or plan to implement graph databases within the next 12 months.” Demand is accelerating based on a need to better understand real-world networks and forecast their behaviours, which is resulting in many new graph-based solutions.
- Networks are a representation, a tool to understand complex systems and the complex connections inherent in today’s data. For example, you can represent how protein or social systems work by thinking about interactions between pairs of proteins or people. By analyzing the structure of this representation, we answer questions and make predictions about how the system works or how individuals behave within it. In this sense, network science is a set of technical tools applicable to nearly any domain, and graphs are the mathematical models used to perform analysis.
- Most graph algorithms can be classified in three major groups: pathfinding, centrality and community detection KnetMiner uses algorithms from all three categories to… Find the shortest path or evaluate route availability and quality (Pathfinding) Determine the importance of distinct nodes in the network (Centrality) Visualise graphs based on how a group is clustered or partitioned (Community detections)
- Knowledge graphs are special type of graph. They aim to structure what is known about the world. Nodes and relations have a type (Institute, Site, based in, distance to) Relations are based on known facts (nano-publications) Properties (miles, year) There are often different ways to model the real-world: example year. Always ask your knowledge engineer!
- The knowledge graph encodes relationships between entities as an explicit relationship in a database, and not just in a sentence on a document, essentially making it machine readable. KGs makes information that much easier to discover and enable world-aware inferences. Google is building their own KG, let’s take a look how it’s used. https://paul4innovating.com/2018/05/15/the-arrival-and-potential-of-knowledge-graphs-into-our-world/simple-enough-for-knowledge-graphs/
- The Knowledge Graph can help you make some unexpected discoveries. You might learn a new fact or new connection that prompts a whole new line of inquiry. Do you know where Matt Groening, the creator of the Simpsons, got the idea for Homer, Marge and Lisa’s names? It’s a bit of a surprise. All of these are linked in the graph. It’s not just a catalog of objects; it also models all these inter-relationships. It’s the intelligence between these different entities that’s the key.
- I am now going to introduce the KnetMiner KG – explain what information it captures and how we build it.
- What types of entities do we need for evidence-based gene discovery? Gene, RNA, Protein, Metabolite, SNP, Phenotype, Publication: These are some examples of things we capture in our KG. A KG can always be extended with other types of entities. The intelligence between these different entities is the key.
- Where do KnetMiner relations come from? This is taken from the wheat knetminer release notes, relations come from: Public databases Pre-publication collaborative datasets Extracted from the literature (pubmed abstracts) Generated by KnetMiner itself
- So how do we convert datasets into a graph? Explain columns The relationship between the columns is hard to understand for machines.
- But when we transform the table into a graph of things and their explicit relationships, the data becomes more meaningful to machines. And connections are easier to “see” for humans.
- If we go and take a second dataset, like PPI from BioGrid…
- We can overlay it over the graph and build a more comprehensive biological picture
- KnetMiner KGs can have millions of nodes and edges. How do we store it and make it accessible? We have made important progress towards making the KG FAIR using new technologies to replace the existing Ondex backend Neo4j graph database with Cypher graph query language Semantic Web (RDF) knowledgebase with SPARQL endpoint
- Graph Query Languages allow us to query the knowledge programmatically and build intelligent search tools that accelerate discovery. If you like to know more about this work, please read our recent publications.
- In simple terms, once we have the KG, methods are needed to evaluate…
- Hypotheses validation and generation
- Mining in public databases
- No statistically significant hits (genes) in AraGWAS But biologically significant path in the knowledge graph exist
- Analyse your private GWAS/QTL data
- By Helena Deus
- Acknowledgements: Various collaborators who have worked with us on KnetMiner, including contributing partners from science, academia and industry.
- Acknowledgements: Various collaborators who have worked with us on KnetMiner, including contributing partners from science, academia and industry. Note: Keywan (the “K”)