KnetMiner Overview Oct 2017
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KnetMiner, with a silent "K" and standing for Knowledge Network Miner, is a suite of open-source software tools developed at Rothamsted Research for integrating and visualising large biological datasets in order to accelerate gene discovery. The software mines the myriad databases that describe an organism’s biology to present links between relevant pieces of information, such as genes, biological pathways, phenotypes or publications. The aim is to provide leads for scientists who are investigating the molecular basis for a particular trait or ways of improving the organism’s performance in some way
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KnetMiner Overview Oct 2017
- 1. KnetMiner – Knowledge Network Miner Keywan Hassani-Pak http://knetminer.rothamsted.ac.uk/ @KnetMiner
- 2. About Rothamsted Research • Rothamsted is the longest running agricultural research station in the world (est. 1843) • Strategic research to address global food security demands • Improving crops to be tolerant to drought, heat and pests while still providing optimum nutrition • Interdisciplinary research from gene to field
- 3. Outline 1. Routes to candidate gene discovery 2. Building genome-scale knowledge networks 3. Overview and demo of KnetMiner 4. Extending networks with text-mining 5. Candidate gene prioritization 6. Discussion
- 4. Routes to candidate gene discovery
- 5. Routes to candidate gene discovery Many gene discovery routes can identify candidate genes for complex traits Gene Expression Genetic Methods Candidate Genes Prioritization Validation Markers for Diagnosis, Breeding, GM Phenotype 1 2 Research Literature Published Data 3 4
- 6. Quantitative Trait Locus (QTL) Mapping 1. Developing of experimental population 2. Collection of phenotypic and genotypic data 3. Construction of linkage map 4. Correlation of marker/trait 5. Identification of QTL 1 QTL region can encompass 10s to 100s of genes. How to prioritize them?
- 7. Genome Wide Association Studies (GWAS) FLC gene expression (FLC) Leaf Number (LN22) Atwell et al., Nature 2010 • GWAS results can be simple and complex to interpret • Peaks can be diffuse covering several hundred kb without a clear centre • Causal polymorphisms have not always strongest association AvrRpm1
- 8. Gene expression analysis 2 different tissues or genotypes time points after infection or treatment Genes • Gene expression studies can be complex to interpret • 100s to 1000s of differentially expressed genes that are somehow related to phenotype • What are the key pathways leading to observed phenotypes?
- 9. Text Mining - Trait and Gene Functions • Publications (free text) are most up-to-date resource for information • Finding sentences that link phenotypes (flowering time) and gene function (circadian clock) to genes (CONSTANS) • Term variability and ambiguity can produce missing or false associations 3
- 10. Life Sciences Databases • Plethora of public Life Sciences databases in various formats • Databases constantly growing in size and content • Challenging to keep up-to- date with growing body of knowledge 1500+ databases published in NAR 4
- 11. Which associations (genes) are worth following up? Often a highly subjective decision Evaluation of all available information is expensive How is genotype translated to phenotype? Often involves direct and indirect interactions Data integration and knowledge discovery is technically challenging
- 13. Biological knowledge network/graph Genotype • QTL • GWAS Omics • Transcriptomics • Proteomics • Metabolomics Phenotype • Disease • Development • Stress tolerance Biological Knowledge Network • Prior knowledge • Structured, unstructured data • Cross-species data IntegrationIntegration
- 14. The approach is generic and works similarly for other species
- 15. Ondex – Data Integration Platform • Free and open source • Data warehousing using a graph-database • Platform to integrate public and private datasets in various formats • Provides a GUI, CLI, APIs and workflows for reproducible data integration Ondex www.ondex.org
- 16. Let’s start with some GWAS data… http://plants.ensembl.org/biomart Example Arabidopsis #SNP=66,816 | #Gene=27,502 | #Phenotype=107
- 17. … transform into a network (SNP) (Phenotype) associated
- 19. (SNP) (Phenotype) associated … add biological interactions
- 20. … add differential gene expression data early vs late flowering
- 21. … add other linked data • Gene-GO • Gene-Phenotype • Gene knock-out or overexpression • Text mining publications • Gene-Publication • Gene-Pathway • Gene-Expression • Protein-Small Molecule • Homology to other species >800k nodes >3 million edges Genome-Scale Knowledge Network (GSKN)
- 22. Same principles for other species Knowledge graph of LRRK2 human gene
- 23. How to search and interpret too much information? • Methods needed to evaluate millions of relationships in knowledge network, prioritize genes and extract relevant subnetworks • Interactive and exploratory tools needed to enable knowledge discovery and decision making • Interpretation should be the task of domain experts i.e. biologists!
- 24. Overview and Demo of KnetMiner
- 25. Web Browser Server Servlets and JSP Page Java Socket Knowledge Graph DBOndex API JavaScript Apache Tomcat Multithreaded Java Server HTML, JSON, XML and images over HTTP via Ajax Views Java Socket KnetMiner System Overview Client Client • Compatibility with all major web browsers • Based on D3.js, cytoscape.JS, node.JS • Interactive and touch-enabled Server • Fast and scalable Java multi- threaded server • Pre-indexing of knowledge graph • Scoring and information extraction
- 26. KnetMiner UI Overview Search Select Explore
- 27. Google-like search interface Search knowledge graph using trait- based keywords Real-time user feedback and query suggestions Trait related keywords Query term suggestions
- 28. KnetMiner Map View (GenoMaps) New touch-friendly web App for Map View in KnetMiner Visualize genes, SNP, QTL, GWAS data. Select genes within QTL regions and overlapping with SNP’s and explore their network
- 29. KnetMiner Network View (KnetMaps) Touch-friendly web App for Network View in KnetMiner Explore networks linking genes to proteins, SNPs, phenotypes, publications, etc.
- 32. Extending knowledge networks with text-mining
- 33. Text-mining workflow in Ondex • Ondex plugins to extract structured information from unstructured free text • Developed workflows to enrich knowledge networks with novel links using the scientific literature Import •Ondex Graph •PubMed •Ontology •Tabular Mapping •NER-method •Concept Class Transformer •Publication •Abstract •Sentence Filter •Relation Type •Attribute Value •Unconnected Export •OXL •RDF •JSON Hassani-Pak et al., JIB 2010
- 34. Ondex text-mining method Input data • 27,416 Arabidopsis gene names from Phytozome • 52,561 Abstracts from PubMed that contain Arabidopsis • 22,201 curated citations from TAIR • 1,349 Trait Ontology terms from Planteome text-mining x y BA occurrs_in Publication Concepts published_in weighted association network IP=1.7; M=1.2; N=2 yx BAGeneTO TO Hassani-Pak et al., JIB 2010
- 35. Text-mining output These steps connect 5553 Arabidopsis genes to 409 TO terms based on 18,341 co-citations (12,190 on sentence level)
- 36. Text-mining discussion • TM method is flexible and can easily enhance data integration workflows and knowledge networks • TM is one of many evidence types in a knowledge network • TM provides access to brand-new information that is not yet available in structured databases • Modest post-TM-filtering is required to retain high-quality relations • TM for gene-phenotype adds 12k high-quality relations that were previously absent in the knowledge network
- 38. Definition of gene-evidence network 1. Gene-evidence network: Biologically plausible paths (semantic motifs) starting with a Gene node and ending with Evidence nodes, e.g. 57 semantic motifs were defined in the wheat network 2. Gene-evidence networks are extracted using the Metadata-based Graph Query Engine (Hindle 2012) 3. Evidence nodes can be part of one (high specificity) or many (low specificity) gene-evidence networks • Gene-evidence network of Gene X contains 5 nodes • Neighbourhood network (n=3) of Gene X contains 9 nodes X
- 39. Searching gene-evidence networks for keywords 1. Knowledge graph indexed and searched for user search terms using Lucene 2. A proportion of nodes in the gene-evidence network can contain the search term auxin cytokinin strigolactone CCD MAX subapical shoots axillary branching shoot branching pathway X Gene-evidence network User search terms Gene
- 40. Gene scoring function (KNETScore) 1. Uses TF*IDF (Sparck & Jones, 1972) to rank documents in gene- evidence network by their relevance to a search term 2. Uses the specificity of documents to a gene (IGF: Inverse Gene Frequency) 3. Uses the frequency of evidence concepts, normalised by size of gene- evidence network (EDF: Evidence Document Frequency) 4. Calculates KNETScore (TFIDF*EDF*IGF) for every gene
- 41. Gene ranking – Example Score: 5.72 Score: 2.71 … the left gene scores higher because it has a smaller gene-evidence network and more specific evidence documents Two genes have a similar number of evidence documents containing the search terms…
- 42. Discussion – Candidate Gene Prioritization • In use case study KNETScore ranked causal gene in 3rd place out of 75 genes within a petal size QTL • High overlaps between KnetMiner top 100 genes for “gibberellin” and “lipid” search terms with curated gene lists • Smart pre-indexing of the knowledge network has reduced the computation of the score from O(2n(|V|+|E|)) to O(1) • Many ways to improve the scoring function, e.g. using weights for different evidence types, distance of evidence to gene and edge-attribute information
- 43. Summary • Web application for very fast search of large genome-scale knowledge graphs • Ranking of candidate genes based on knowledge mining • Interactive visualisation of genome and knowledge maps • Facilitates knowledge discovery and hypothesis generation
- 44. KnetMiner – Makes Gene Discovery Faster & Fun International academic collaborations Interest from industry and start-ups http://knetminer.rothamsted.ac.uk/
- 45. KnetMiner 2.0 – BBSRC BBR (GCRF) Proposal SNP-Seek Genetic diversity Novel traits Phenotype data KnetMiner 2.0 Interactions Pathways Literature Scientist/Breeder Novel genes Better crops Faster discoveries Ensembl Plants Reference genomes Model Species Homology data Data Information Knowledge Insight A pangenomic and network based approach to search for novel genes and clues to design better rice varieties.
- 46. Acknowledgements John Doonan Sergio Feingold Martin Castellote Uwe Scholz Matthias Lange Keywan Hassani-Pak Ajit Singh Marco Brandizi Monika Mistry Lisa Lill Chris Rawlings Dave Edwards Philipp Bayer Misha Kapushesky Kevin Dialdestoro @KnetMiner Jan Taubert Artem Lysenko Matthew Hindle Catherine CanevetRamil Mauleon Kenneth McNally Nickolai Alexandrov Andy Law
Editor's Notes
- https://www.thesius.de/blog/articles/verteidigung-dissertation-doktorarbeit-so-gelingts/
- http://www.nature.com/nrg/journal/v2/n5/fig_tab/nrg0501_370a_F1.html The basic strategy behind mapping quantitative trait loci (QTL) is illustrated here for a | the density of hairs (trichomes) that occur on a plant leaf. Inbred parents that differ in the density of trichomes are crossed to form an F1 population with intermediate trichome density. b | An F1 individual is selfed to form a population of F 2 individuals. c | Each F2 is selfed for six additional generations, ultimately forming several recombinant inbred lines (RILs). Each RIL is homozygous for a section of a parental chromosome. The RILs are scored for several genetic markers, as well as for the trichome density phenotype. In c, the arrow marks a section of chromosome that derives from the parent with low trichome density. The leaves of all individuals that have inherited that section of chromosome from the parent with low trichome density also have low trichome density, indicating that this chromosomal region probably contains a QTL for this trait.
- GWAS results can also be complex to interpret LD in Arabidopsis decays within 10 kb on average https://www.ncbi.nlm.nih.gov/pubmed/17676040
- http://www.oxfordjournals.org/nar/database/c
- Worth = Have a positive impact on the biological outcome in the whole organism without producing negative side effects. Significant SNPs are rarely located within the causal gene sequence… Consider LD, closest gene is not always the correct candidate… Consider cofounding, strongest association not always the main causal effect…
- Many phenotypes are complex, polygenic and the result of complex interactions on cellular level Linking genotype and phenotype is one of the greatest challenges in biology
- SNP-Phenotype relations (122,919 relations) of significant SNPs (as defined by Ensembl, p-value<0.05?) linked to 107 phenotypes; on average 1,150 SNPs per phenotype. SNP-Gene relations are based on genes in close proximity to SNPs <1000bp (96,047 relations) How to integrate GWAS and biological interaction data
- Using Ondex
- Recent work: Brought in differential gene expression data for Arabidopsis in KnetMiner. Added capability in KnetMiner Network View (KnetMaps) to visualize this data as a new concept type: DGES and “differentially_expressed” relation. The slide shows example of DGES: early vs late flowering in Arabidopsis.
- http://www.sciencedirect.com/science/article/pii/S2212066116300308
- Highlight text-mining Add gene expression Mention xrefs and that they cn be collapsed
- Scale: As these networks combine a wide variety of entities which are derived by parsing large volumes of data, they are quite large. A typical plant knowledge network created using Ondex has around 100,000 genes linked amongst 500,000 concepts (can be genes, proteins, SNP’s, publications) in approx. 1-1.5 million interactions/ relations. KnetMiner was developed to enable users to interactively explore such large, genome-scale knowledge networks and extract relevant plausible pathways linking candidate genes to agronomic traits. KnetMiner works by querying a species-encompassing knowledge network to mine its data and retrieve “relations” (i.e., links/ connections) between the various entities (called “concepts”) within the network, such as genes, proteins, phenotypes, SNP’s and publications. This helps create a pathway that can be visually traversed to understand how various biological entities are inter-linked and how a gene might influence a specific phenotype/ trait.
- KnetMiner is web-based app to search, select and explore a vast amount of information. We try to make the software intuitive and fun to use and embed the user into the discovery process Achim: “KnetMiner is a great example for how an easy-to-use software app should look like. Even I can figure it out without being a techie or having to read a thick manual. “
- Real-time search results, as you type. Example queries. Useful dynamic Query suggester to help refine user’s search. Add specific QTL regions to especially focus on. Provide your own Gene List to search against.
- Once we had all this new data in place and integrated in the knowledge networks in KnetMiner, a new challenge was to work on software tools/ components that enable interactive visualization and exploration of this new, highly dense data in a user-friendly manner. We collaborated with a UK-based company to develop a new tool called Genomaps.js, a lightweight, touch-friendly tool that enables the interactive visualization of high-density SNP, QTL, GWAS and gene data. After performing a search in KnetMiner and getting a list of ranked genes as output in Gene View, users can switch to Map View and explore the results in a chromosome-centric view Genomaps.js Users can now select genes within specific QTL regions and overlapping with SNP’s and explore their network in Network View to look for plausible pathways and interactions.
- In order to accommodate the new data and provide a more user-friendly way to explore these networks, the existing Network View was revamped and replaced the new KnetMaps.js KnetMaps.js is a lightweight, touch-friendly tool that allows users to visualize and explore heterogeneous networks linking genes to proteins, publications, phenotypes, biological processes, etc. Allows users to identify plausible pathways linking candidate genes to phenotypes within inter-linked networks.
- Use case 1 (screencast): An example use case in Arabidopsis, using our newly integrated GWAS data, where we will be looking to discover candidate genes controlling flowering. Showcasing Genomaps (Map View) in KnetMiner Arabidopsis. We will specifically look at FLC gene expression, a complex trait controlled by multiple genes. We will search the KnetMiner Arabidopsis knowledge network for the query: ‘flowering FLC’ and identify and explore the network of genes controlling this underlying trait. Search using keywords: flowering FLC or FT. Filter Genomaps: Change p-value, show top 100 genes. Select: FLC, SPA4, FRI, LD & launch KnetMaps.
- Use case 2 (screencast): Pre-harvest sprouting: Next use case will show how to use KnetMiner for mining genes related to grain color and PHS. This example is based on Andy Phillips (Rothamsted) RNA-seq experiment to identify differences between white and red grain wheat and links to PHS. Wheat white grain – more prone to sprouting. Showcasing new Wheat instance with TGACv1 release & new KnetMaps. Note: PHS is the result of premature germination of grain in the ear and results in loss of bread-making quality. Red grain colour is associated with increased dormancy and resistance to PHS. Grain colour is due to proanthocyanidins (condensed tannins) in the testa.
- Boosting queries found in the title TO by Laurel Cooper
- Illustration of a gene-evidence network as derived through “biologically plausible” semantic motifs. Blue nodes represent Gene concepts; red nodes are annotations such as GO, TO, EC, Pathway, Publication concepts. A path that goes via bold edges is valid (biologically meaningful path that allows annotations to be transferred to the seed gene). A path that goes via dashed edges is invalid. A gene-evidence network contains only filled nodes, whereas a gene-neighbourhood network would also contain the unfilled nodes.
- Our team has also strong expertise in the development of reusable research software. Our flagship software output is KnetMiner. The development of KnetMiner (fka QTLNetMiner) began in 2008 as part of a collaborative project with Steve H to prioritise candidate genes in willow QTL… Since then it has slowly grown to become a unique resource for gene discovery in many different species. 50% of users from the UK and rest from other countries including… Ajit has made significant contributions to the project that have helped to improve the usability and the user experience. Marco who joined us last year is responsible for improving the interoperability of KnetMiner using more standardised technologies. Monika was a sandwich student for one year and was a great help to maintain or knowledge resources.
- KnetMiner has still room to grow. As part of collaborative project with Sigrid, Gancho and scientists from IRRI, EBI and Uni Malaysia we have submitted a grant application to the Bioinformatics and Biological Resource Fund to extend KnetMiner to rice research and breeding. BBR is hugely competitive, over 300 expression of interests have been submitted under the GCRF highlight. We might need to find alternative ways to continue the Bioinformatics collaboration with IRRI.
- Acknowledgements: Various collaborators who have worked with us on KnetMiner, including contributing partners from science, academia and industry. Code available on GitHub: https://github.com/KeywanHP/KnetMiner