Neo4j GraphTalk Basel - Using Graph Technology to drive Diabetes Reserach
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This document discusses using graph technology to drive diabetes research at the German Center for Diabetes Research (DZD). It summarizes that graph databases allow researchers to connect diverse data types, like clinical studies, biosamples, and molecular data. This facilitates querying across data silos and mapping relationships between species. The DZD has created a Neo4j graph database called DZDconnect to integrate over 800 million nodes and relationships from internal and external sources to further diabetes research and precision treatment.
![Goals:
1. Connect data from our clinical studies and biobanks
2. Researches can easily browse through measured parameters and available biosamples
3. Meta data of parameters helps to assess which samples are comparable
How many biosamples were aquired in visit 17 of ‘PLIS‘ and which
parameters were measured?
match (s:Study{name:’PLIS’})->[ ]->(v:Visit {no:17})-[:AQUIRED_BIOSAMPLE]->(b:BioSample)-[:MEASURED_PARAMETER]->(p:Parameter)
return count(b), p](https://image.slidesharecdn.com/20190508neo4jbaselexport-190516143128/85/Neo4j-GraphTalk-Basel-Using-Graph-Technology-to-drive-Diabetes-Reserach-19-320.jpg)
Neo4j GraphTalk Basel - Using Graph Technology to drive Diabetes Reserach
- 1. Using graph technology to drive diabetes research Dr. Alexander Jarasch Head of data and knowledge management German Center for Diabetes Research (DZD)
- 4. Numbers worldwide 1 in 11 adults has diabetes (425 million) Since 1980 quadrupled 12% of global health expenditure is spent on diabetes ($727 billion) Over 1 million children and adolescents have type 1 diabetesTwo-thirds of people with diabetes are of working age (327 million) 2017 Three quarters of people with diabetes live in low and middle income countries 1 in 2 adults with diabetes is undiagnosed (212 million) International Diabetes Federation (IDF) 2017
- 5. Some numbers (USA and Germany) 30 million have diabetes (9.4 % of adults )1 +1‘500‘000 p.a. 84 mio. prediabetes2 $327 billion USD costs p.a.1 ($237 bn. medical costs, $90 bn. reduced productivity)2 16 billion € costs p.a.1 7 million have diabetes (7.4 % of adults)1 +500‘000 p.a. ~ 7 mio. prediabetes and undiagnozed
- 6. Overweight/obesity in the US obese adults in the US (BMI* >= 30) *BMI=30: 5”11 = 220,46 lbs (180cm = 100 kg)
- 7. Complications kidney Diabetic nephropathy 40 % of kidney failure/dialysis feet 70 % of all foot amputations eyes Diabetic retinopathy 30 % of loss of sight brain 2-4 fold increased risk for stroke acute cardiac death Main reason of death of diabetic patients (33 % of all heart attacks) nerves Diabetic Neuropathy Amputations of extremeties
- 8. Who we are
- 9. German Center for Diabetes Research 5 Partners, 5 associated partners – 400 researchers (basic research and university hospitals) DZD bundles competencies so that those affected benefit more quickly from research results. academic, non-profit
- 10. German Center for Diabetes Research diabetes treatment diabetes prevention prevention of complications hospitals prevention nutrition / diet beta cells genetics therapy clinial studies cohorts basic researchhealthcare
- 11. Goal: Better Prevention and therapy Precision prevention and therapy identify and cluster diabetes subtypes Precision treatment of subtypes
- 12. How do we use graph technology?
- 13. lipid metabolism This is how data in biology actually looks like…
- 14. A zoom…
- 15. Hospitals Basic Research Data Analysis “Patient“ 64kg, 178cm, male “drug“ Metformin “Study“ T2D insulin resistance “Gene“ AAGCTTCACATGG “Metabolite“ C6H12O6 cell inactive mice prediabetic pig “statistics“ microscope image complications Why (the heck) is everything stored in relational data silos?
- 16. DZDconnect - a Neo4j graphDB “Patient“ 64kg, 178cm, male “drug“ Metformin “Study“ T2D “statistics“ “Gene“ AAGCTTCACATGG “Metabolite“ C6H12O6 insulin resistance cell inactive mice prediabetic pig microscope image complications
- 17. For the public
- 18. What questions can we answer?
- 19. Goals: 1. Connect data from our clinical studies and biobanks 2. Researches can easily browse through measured parameters and available biosamples 3. Meta data of parameters helps to assess which samples are comparable How many biosamples were aquired in visit 17 of ‘PLIS‘ and which parameters were measured? match (s:Study{name:’PLIS’})->[ ]->(v:Visit {no:17})-[:AQUIRED_BIOSAMPLE]->(b:BioSample)-[:MEASURED_PARAMETER]->(p:Parameter) return count(b), p
- 20. Study Person Visit BioSample Experiment Parameter Query clinical parameters and biosamples
- 21. Mapping of molecules between species through metabolic pathways
- 22. genomics transcriptomics metabolomics proteomics Extend in-house knowledge ~800 mio. nodes ~800 mio. relationships
- 23. Mapping between human and prediabetic pig SNPs targeted metabolomics n=104 annotated: diabetes genomics transcriptomics metabolomics pathway analysis DZD experiments GWAS cataloge metabolite n=16 Biocrates experim ent ENSEMBLE gene IDs KEGG gene IDs KEGG protein IDs KEGG metabolite IDs metabolite n=7 (of 16) Xxaa C11:0 Xxaa C11:1 Xxaa C11:2 Xxaa C11:3 Xxaa C11:4 Xxaa C11:5 Xxaa C11:6 ENSEMBL gene n=97 m apping KEGG gene n=96 m apping KEGG enzyme n=16 translated in KEGG metabolite n=63 connected to KEGG metabolite n=31 mapping
- 24. Incorporating external knowledge to our in-house data
- 25. Natural language processing Abstract Identification of genetic elements in metabolism by high-throughput mouse phenotyping. Metabolic diseases are a worldwide problem but the underlying genetic factors and their relevance to metabolic disease remain incompletely understood. Genome-wide research is needed to characterize so-far unannotated mammalian metabolic genes. Here, we generate and analyze metabolic phenotypic data of 2016 knockout mouse strains under the aegis of the International Mouse Phenotyping Consortium (IMPC) and find 974 gene knockouts with strong metabolic phenotypes. 429 of those had no previous link to metabolism and 51 genes remain functionally completely unannotated. We compared human orthologues of these uncharacterized genes in five GWAS consortia and indeed 23 candidate genes, like ABC1, XYZ2, are associated with metabolic disease. We further identify common regulatory elements in promoters of candidate genes. As each regulatory element is composed of several transcription factor binding sites, our data reveal an extensive metabolic phenotype-associated network of co- regulated genes. Our systematic mouse phenotype analysis thus paves the way for full functional annotation of the genome. Metabolic disorders, including obesity and type 2 diabetes mellitus, are major challenges for public health. Rozman and Hrabe de Angelis, Nat Commun. 2018 NLP method by GraphAware Keywords Abstracts
- 26. Find connections to other diseases Alzheimer‘s cancer cardio vascular diseases diabetes Lung diseases infectious diseases
- 27. Take home message From 2D data representation to graphs! Across locations, disciplines and species (diseases) Enabling a new level of data analysis