the power of graphs to analyze biological data
DESCRIPTION
This talk will illustrate the power and flexibility of Graph Databases and Neo4j specifically to help in the overall analysis of biological data sets. Davy will show how to build a visual exploration environment that helps researchers at identifying clusters within various biological data sets, including gene expression and mutation prevalence data. Additionally, he will demo BRAIN (Bio Relations and Intelligence Network), a powerful data exploration platform that combines various scientific data sources (including Pubmed, Swissprot and Drugbank). It uses Neo4J under the cover to both store and enable powerful querying capabilities that provide key insights and deductions.TRANSCRIPT
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Grap
hCon
nect
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the power of graphs to analyze biological data
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about me
who am i ...
Davy Suvee@DSUVEE
➡ big data architect @ datablend - continuum• provide big data and nosql consultancy
• 5 years of hands-on expertise in the pharma/biotech sector
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massive data
big data in pharma
full genome sequencing
complex databiological networks
scalable number crunching platform
visual insights-driven platform
graphs!!
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outlier detection platform
big data in pharma (2 specific use cases)
neo4j, mongodb/cassandra and gephi
euretos - brainneo4j, mongodb, solr and prefuse
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gene expression clustering
★ 4.800 samples★ 27.000 genes
➡ oncology data set:
➡ Question:★ for a particular subset of samples, which genes are co-expressed?
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storing gene expressions (mongodb)
{ "_id" : { "$oid" : "4f1fb64a1695629dd9d916e3"} , "sample_name" : "122551hp133a21.cel" , "genomics_id" : 122551 , "sample_id" : 343981 , "donor_id" : 143981 , "sample_type" : "Tissue" , "sample_site" : "Ascending colon" , "pathology_category" : "MALIGNANT" , "pathology_morphology" : "Adenocarcinoma" , "pathology_type" : "Primary malignant neoplasm of colon" , "primary_site" : "Colon" , "expressions" : [ { "gene" : "X1_at" , "expression" : 5.54217719084415} , { "gene" : "X10_at" , "expression" : 3.92335121981739} , { "gene" : "X100_at" , "expression" : 7.81638155662255} , { "gene" : "X1000_at" , "expression" : 5.44318512260619} , … ]}
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correlating samples (mongodb/map-reduce)
pearson correlation
x y
43 99
21 65
25 79
42 75
57 87
59 81
0,52
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co-expression graph (neo4j)
➡ create a node for each sample➡ if correlation between two samples >= 0.8
create an edge between both nodes
122552
122553
122551
correlated
value : 0,86
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co-expression visualisation (gephi)
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euretos - brain
➡ pubmed: 23 million biomedical articles• 1300 new ones added every day• google-like search interface
➡ reading an article ...• malaria is transferred by mosquitoes
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euretos - brain
authors references
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euretos - brain
ooooooh crap ...
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euretos - brain
➡ nanopub (nanopub.org)• the smallest unit of publishable information
➡ assertion• subject: malaria• predicate: transferred by• object: mosquito
➡ provenance• how this came to be (meta-data)
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euretos - brain➡ unfortunately, malaria is encoded in various ways ...
malaria P22384 AQ879
db1 db2 db3
malaria
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euretos - brain
malaria mosquitotransferred by
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euretos - brain
➡ brain (http://www.euretos.com/brain)• exploration and analysis platform• millions of concepts/triples/nanopubs• pubmed, uniprot, omim, pubchem, ...
➡ architectural stack• meta-data is stored in mongodb• graph in neo4j• swing interface connecting to rest endpoints
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brain
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brain
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brain
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brain
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brain
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brain
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brain
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brain
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Questions?
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