A Reason Able View To The Web Of Pathway Data

Vassil Momtchev Group Leader, Semantic Life Science Applications A Reason-Able View to the Web of Pathway Data 2009/10/08

Objectives WITH NO 2009/10/08 Bio-IT World, Hannover

A Typical Question? Select drugs related to asthma that are linked to a curated molecular interaction in the literature where the protein is known to cause inflammatory response… 2009/10/08 Bio-IT World, Hannover

A Typical Answer!

Find all drugs related to asthma

Extract all proteins in a text file

Compose a long query to list the proteins by OR

Get a filtered list of genes

For each gene send a query in molecular interaction database

2009/10/08 Bio-IT World, Hannover

A Typical Question? Select all human genes, which code for proteins with known molecular interactions and are analyzed with molecular techniques like ‘Transfection‘; Restrict the results just to gene or proteins which are known drug targets… 2009/10/08 Bio-IT World, Hannover

Wrap-up: Typical Problems

Link data between different silos applications

Hard to collaborate across domains due to information silos

Combine public and private information

Expensive process that could not be done on the fly

Put the information into context

Query cross-domain information

Make more interesting queries

No easy way to interpret the information

Analyze the knowledge locked into unstructured data

Database information is often outdated

Relationships are not enough to capture the case study questions

Hard to find information

Agenda

Ontotext

Put your data into context

Linked Life Data platform and Pathway and Interaction dataset

LifeSKIM an application to find any existing information

Who Are We?

Ontotext is a semantic technology provider

Established in year 2000 as part of Sirma Group

Sirma is a top-3 software house in Bulgaria , est. 1992, ~300 persons

Since September 2008 a separate company

Staff: 40 employees in Bulgaria

Multiple affiliates and contractors around the world

Over 150 person-years invested in product development

Bio-IT World, Hannover 2009/10/08

What is Our Position?

Unique coverage of technology areas:

Semantic Databases : high-performance RDF DBMS, scalable reasoning

Semantic Search : text-mining (IE), Information Retrieval (IR)

Semantic Web Services and BPM : WS annotation, discovery, etc.

Web Mining : focused crawling, wrapping

Knowledge fusion : identity resolution, record linkage

Core business: development of semantic engines

Mostly product development and sales

Complemented by professional services

Joint ventures for vertical applications

Our Application Domains

Ontotext technologies are used for various applications :

Data Integration (consolidation of multiple databases)

Knowledge & Content Management (enterprise search)

Business Intelligence

Web-mining/Web-intelligence

Major industries/markets

Life sciences and health care

Telecommunications

Media Archives, Media Research

Online recruitment

IP/Patent Research

Web search, Web 2.0 and Semantic Web start-ups

Linked Life Data Pathway and Interaction Knowledge Base

Linked Life Data

RDF warehouse solution

Powered by OWLIM semantic database

Pathway and Interaction Knowledge Base

Dataset that integrates molecular data

Part of linked data cloud

Test with case studies developed

in collaboration with AstraZeneca

Select drugs related to asthma that are linked to a curated molecular interaction in the literature where the protein is known to cause inflammatory response… 2009/10/08 Bio-IT World, Hannover

Select all human genes, which code for proteins with known molecular interactions and are analyzed with molecular techniques like ‘Transfection‘; Restrict the results just to gene or proteins which are known drug targets… 2009/10/08 Bio-IT World, Hannover

Holistic View of the Scientific Problems

Use data of more than 20 sources

Inter-link the information

Put it into new contexts

Switch different perspectives

Environmental Factors 2009/10/08 Bio-IT World, Hannover

The Challenge of the Holistic View

Extreme amount of data

Data is supported by different organizations

Information is highly distributed and redundant

Tons of flat file formats with special semantics

Knowledge is locked in vast data silos

Isolated communities which could not reach cross-domain understanding

Increase the data abstraction level

RDF Technology 2009/10/08 Bio-IT World, Hannover ERBB2 HER2 CD340 Q4H1F1 Q4H1F2 Protein GO:0005023 EGF receptor activity receptor activity peroxisome receptor ENSG00000141736 Gene 2064 Gene Ontology Term GO: 0004872 GO: 0005006 is_a is_a type type type type type type label label label label label label database cross-reference hasProtein hasProtein hasGene hasGene

Linking Open Data Community Project

Use URIs to identify the information

Expose via dereferenceable URIs

Allows browsing of data spread across different servers, in the way HTML is browsed

LLD Integration Process Data Source Identification Flat files OBO files XML RDBMS RDF Special tailored transformer OBO to SKOS converter Custom XSLT RDBMS to RDF formatter RDF warehouse Reasoner Instance Mappings Semantic Annotations 2009/10/08 Bio-IT World, Hannover

Over 20 Different Sources Number of statements: 4.792.035.475 Number of explicit statements: 2.218.239.691 Number of entities: 370.230.951 2009/10/08 Bio-IT World, Hannover Data source Description RDF statements Disease Ontology Disease Ontology is a controlled 446,066 Human Phenotype Ontology The human phenotype ontology (HPO) intends 70,911 Symptom Ontology The symptom ontology was designed around 4,163 DrugBank The DrugBank database is a unique bioinformatics 493,794 Diseasome The diseasome website is a disease relationships 69,546 DailyMed DailyMed provides high quality information about 116,992 SIDER SIDER contains information on marketed medicines 96,272 BioGRID The Biological General Repository for Interaction Datasets 1,892,897 INOH INOH (Integrating Network Objects with Hierarchies) 432,456 CellMap The Cancer Cell Map contains selected 173,914 HPRD The Human Protein Reference Database 18,05,651 HumanCYC HumanCyc is a bioinformatics database that describes 341,225 IMID General Repository for Interaction Datasets. 154,408 IntAct IntAct provides a freely available, open source database 11,005,555 Reactome Reactome is a free, online, open-source, curated resource 2,538,793 NCI-Nature Nature pathway interaction database. 333,415 KEGG KEGG PATHWAY is a collection of manually drawn 18,128,735 Entrez-Gene Entrez Gene is a searchable database of genes 107,193,308 PubMed PubMed is a service of the U.S. National Library of Medicine 807,851,455 UniProt Major resource for protein sequences 1,252,667,885 UMLS Metathesaurus Database that contains information about biomedical 12,420,882 UMLS Semantic network Semantic categorization of terminology 1,368

<C1,broader,C2>

<C2,broader,C3>

<C1,broaderTransitive,C3>

<premise 1>

<premise 2>

<conclusion>

Schema Reasoning 2009/10/08 rdf:type broader umls:C0035204 inferred broader broaderTransitive Bio-IT World, Hannover COPD Bronchial Diseases Respiration Disorders umls:C0006261 umls:C0024117 Chronic Obstructive Airway Diseases

Instance Mapping biopax-2:SHORT-NAME biopax-2:XREF P29965 UNIPROT CD40L_HUMAN cpath:CPATH-94138 cpath:CPATH-LOCAL-8467065 biopax-2:PHYSICAL-ENTITY biopax-2:ID biopax-2:DB biopax-2:PHYSICAL-ENTITY cpath:CPATH-LOCAL-8749236 uniprot:P29965 CD40L_HUMAN uniprot:mnemonic TNF5_HUMAN uniprot:mnemonic TNFL5_HUMAN uniprot:mnemonic CD4L_HUMAN uniprot:mnemonic 2009/10/08 Bio-IT World, Hannover

X Y ns-x: id ns-y: id db id X Y db: id X Y accession db: id db: accession X term Y Y X Y X text to describe name name 2009/10/08 Bio-IT World, Hannover Namespace mapping Reference node Mismatched identifiers Value dereference Transitive link Semantic Annotations

Semantic Annotations 2009/10/08 Bio-IT World, Hannover broader umls:C0035204 broader broaderTransitive COPD Bronchial Diseases Respiration Disorders umls:C0006261 Chronic Obstructive Airway Diseases This an example text of document that mentions COPD disease hasDocumentText mentions Natural Language Processing Natural Language Processing Natural Language Processing Natural Language Processing

Semantic Annotations #2