ariadne genomics technology: extraction from the literature and network analysis dr. anton yuryev

©2006 Ariadne Genomics. All Rights Reserved.

Ariadne Genomics technology:Extraction from the literature and

network analysisDr. Anton Yuryev

Ariadne Genomics Inc.


2©2006 Ariadne Genomics. All Rights Reserved.

Pathway Studio product line

• Pathway Studio desktop• Pathway Studio workgroup• Pathway Studio enterprise

Main functionality:1) Data mining and pathway building2) Analysis of high-throughput data3) Text-mining, fact extraction and database

building



Ariadne Corporate OfferingSoftware solution for Knowledge management and pathway

analysis of the high-throughput data

Knowledge Databases

ResNetBiological Association

Networks

Pathway BuildingPathway collection

MedScan

1000 abstracts/min

Proprietary data

Public interaction data

Analysis of High-Throughput data

Text-mining



Ariadne Database Construction• Automatic fact extraction by MedScan from organism-specific subset of

PubMed and full-text journals • Import of Ariadne proprietary curated data

– Curated physical interaction– 712 signaling line pathways

• Import of publicly available curated interaction data: Entrez Gene, BIND, HPRD, KEGG, Gene Ontology

• Import of publicly available high-throughput interaction data (Y2K, Mass-spec etc)

• Import of user proprietary data:– Proprietary or publicly available experimental data in PSI, BioPax or Tab-

delimited formats– Data mined by MedScan tool from literature sources not included with database

• User manual curation



Additional Commercial datasets

• > 130 KEGG metabolic pathways

• >70 STKE pathways (AAAS)

• >10,000 ERGO pathways for 587 organisms (Integrated genomics)

• >100,000 protein interactions from Hynet (Prolexys)

• >600 disease pathways PathArt (Jubilant)



Pathway Studio Enterprise distinctions

• Web-client for instant pathway publishing– Connection between multiple geographical sites

• 3-tier architecture with Java API to connect third party applications and algorithms

• MedScan Enterprise license:– open MedScan dictionaries and pattern rules files for

customization– distribution of MedScan data across entire company

• GSEA, NEA and network clustering algorithms for analysis of high-throughout data



Pathway Studio Enterprise Architecture

Bioinformaticians via Pathway Studio

Database

Application server

Read-only users via web browser

Data editorsvia web browser

Third party tools,in-house applications,API SQL interface,

bulk data management



“Everyone is an Expert” decentralized deployment schema

Hundreds or thousands of users some with read only and some with editor or publishers roles accessing one central database via Pathway Studio and/or Web browser to analyze experiments, browse pathway collection, do literature mining, sharing the data and analysis results.



“Bioinformatics service group” centralized deployment schema

Bioinformatics group servicing scientists for entire company by analyzing their experimental data and literature mining. Analysis results are published via Web browser interface for end users

Bioinformatics group1) Analysis of experimental data2) Text-mining and Pathway

Building

View only access to pathways and analysis networks annotated with experimental data via web browser and links to PathwayExpert Web Services

1) Experimental data2) Search requests

End users



“Disease area” decentralized clusters deployment schemaDisease area groups have bioinformatics, biologists and chemists working as a

team with focus on one disease

Cardiovascular group Cancer group

Digestive disorders group CNS group



Plan of the talk

1) Text-mining, fact extraction and database building- Stay current with the literature- Build focused literature networks- Build focus databases

2) Data mining and pathway building- Understand molecular mechanisms of disease and processes- Maintain pathway collection- Build focus databases

3) Analysis of high-throughput data- Functional ontology analysis- Network analysis


Introduction to MedScan technology



How MedScan extracts facts from text?• Sentence in PubMed:

“Axin binds beta-catenin and inhibits GSK-3beta.”• Identify Proteins in Dictionary (in red):

“Axin binds beta-catenin and inhibits GSK-3beta.”• Identify Interaction Type (in black):

“Axin binds beta-catenin and inhibits GSK-3beta.”

• Extracted Facts: Axin - beta-catenin relation: Binding Axin -> GSK-3beta relation: Regulation, effect: Negative

Syntactic Layer Noun Phrase Verb Phrase Noun Phrase

Semantic Layer Protein Protein Relations

Protein



Filtering by Number of references controls the network confidence in Pathway Studio

Binding (references: 77)Owner: public, Entities E2F1-RB1This stabilization of the pRB-E2F-1 complex by AAV expression in adenoviral-infected cells should lead to a decrease in E2F-1- mediated expression of cell cycle-specific genes.



MedScan Architecture

Entity recognizer

Semantic processor

Pattern matcher

Entity detection

Relationship extraction

Dictionaries

Rules

Patterns

Modules

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Cartridges

Future:•New modules: ConceptScan•New cartridges: Immunology, Clinical

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Customizable by user

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RNEFXML



Describing MedScan

• Manually curated: dictionaries and grammar rules

• Fast: 14 mln PubMed abstracts in 2 days on modern PC

• Comprehensive: facts recovery rate > 90%90% = 70% sentence recovery rate + 20% literature redundancy

• Removes redundancy: 7,647,282 non-distinct relations =>1,000,000 distinct relations

• Accurate: false positive rate – 10%

• Customizable: dictionaries and patterns



MedScan ApplicationsPubmed

Open access

Google

MedScan

Entity-based indexSemantic Index

Automatic reader’s digest Document Summary

Indexing the scientific literature

Extracting interactions to create databases for systems biology


Pathway Building in Pathway Studio• Manual• Automatic using Graph navigation tools• Using text-mining with MedScan



Viewing and editing pathways in Pathway Studio• Viewing entities in the List Pane• Entity and relation tables• Show all references• Pathway Reference summary• Export protein list• Display styles: By type, By effect, By

reference count• UI options:

– magnifier– fit text to entities– simple and full graph view– fit to window– rotate– move– zoom by rectangle– advanced graph scaling

• resizing nodes in pathway pane


Pathway Building by text-miningNon-melanoma skin cancer

>1,000,000 cases, (<2,000 deaths), in USA



MedScan Reader: PubMed search

Keep searching and adding relations

At the end Send extracted relations to Pathway Studio



MedScan Reader: Import top 100 Hits from Google Scholar search: downloads found articles and processes them with MedScan



MedScan Reader: Import top 30 Hits from Google search: downloads found web-pages and processes them with MedScan



Full-text article found on Highwire press with “non-melanoma skin cancer” text search



MedScan customization by focused literature source:“Nonmelanoma skin cancer” literature network – result of

targeted text-mining by MedScan Reader

Every entity in this network was mentioned in the context of non-melanoma skin cancer:-Find hubs-Compare with patient data



MedScan customization by focused literature source:Protein network for non-melanoma skin cancer

Compare this pathway with your experimental patient data


Automatic Pathway Building using Graph navigation

Build pathway tool



Mining regulatory relations in databaseBasic principal

Regulatory interactions are mediated by physical interaction network

– Regulomes– Biological processes pathways– Disease networks



Regulome pathways: algorithm input



Regulome pathways:Connecting IL10 targets with physical interaction relations



Building pathways by Data miningconverting regulatory network to protein physical interaction network for Cell Processes, Diseases, Regulomes



Disease networks2300 diseases, 230 cancers in ResNet 5.0 database

converting regulatory network to protein physical interaction network for Diseases

Endothelial cells cancer



Endothelial cells cancer network



Applied information retrieval and multidisciplinary research: new mechanistic hypotheses in Complex Regional Pain Syndrome

J Biomed Discov Collab. 2007; 2: 2. Kristina M Hettne, Marissa de Mos, Anke GJ de Bruijn, Marc Weeber, Scott Boyer, Erik M van Mulligen, Montserrat Cases,

Jordi Mestres, and Johan van der Lei

Resulting network of CRPS concepts


High-throughput data analysis in Pathway Studio

• Identification of responsive genes• Functional ontology analysis• Network analysis



Supports analysis of all types of experiment data

• Gene expression

• Metabolomics

• Proteomics

• SNP and CNV analysis

• Methylation arrays

• Phosphorylation arrays

Support for all microarray platforms:•Affymetrix•Agilent•Illumina•Nimblegen•Superarray•Custom design chips



Analysis of gene expression microarray data: STEP 1: Identification of responsive genes

• Expression data import (tab, xls, cel)

• Selection of responsive genes– Find differentially expressed genes (significance

analysis via t-test)– Gene clustering via correlation networks – Find responsive genes in the third party software

for statistical analysis of microarray data and import it as a protein list (Tools->Import protein list)



Calculation of differentially expressed genes in Pathway Studio (significance analysis using paired and unpaired t-tests)



Gene clustering in Pathway Studio using Correlation network



Analysis of gene expression microarray data: STEP 2: Pathway Analysis of responsive genes

• Network analysis– Identification of DE expressed protein complexes and physical networks– Identification of major regulators and targets in expression network

• Via network querying (Build pathway tool)• Via Network enrichment analysis (in PS Enterprise only)

• Functional analysis– Comparison of responsive genes with ontologies and pathway collection

• Via Fisher exact test• Via Gene Set Enrichment analysis (GSEA in PS Enterprise only) • Gene ontology analysis (via Fisher’s test or GSEA)• Comparative gene ontology analysis• Via network querying (Build pathway tool)



Functional analysis: comparative GO groups analysiscomparing cell responses in GO group space



Building protein network from interesting GO groups and identification of its major expression regulator



Identification drug responsive genes



Evaluation of drug efficacy and side-effects



GSEA: Gene Set Enrichment analysis in PS Enterprise



Visualizing expression data on GSEA pathway



• Functional ontology analysis• Network analysis



Data model in ResNet database Formalized representation of biological regulatory and interaction network

Expression

PromoterBinding

DirectRegulation

ProtModification

Binding

MolSynthesis

MolTransport

Regulation

Interpretation of Gene Expression data

Interpretation of Proteomics data

Interpretation of Metabolomics data, Biomarkers prediction and validation

…MORE….



Network analysis: identification of major regulators and targets among DE genes via Build pathway



Network analysis: Identification of major regulators Network enrichment analysis

Finds regulators with most differentially expressed targets

Better Worse



Network Enrichment analysis in PS Enterprise



Visualizing expression data on NEA pathway



Network Enrichment Analysis: Example for metabolomicsIdentification of metabolism regulators

Finds regulators with most differential levels of metabolite targets

Better Worse



Network analysis: finding DE protein complexes using Build dense expressed networks in PS Enterprise



>200 publications using AGI software and ResNet database

• Analysis of gene expression microarray data (139) • Pathway Analysis (97) • Disease mechanism (84) • Publication by Ariadne Authors (18)• Human genetics (7) • Text processing (6) • Reviews (7) • Databases (3) • Drug discovery (21) • Toxicogenomics (4)



Most common workflow for microarray analysis in Pathway Studio for disease

• Identify genes differentially expressed in disease (DE genes)

• Identify genes known to associate to disease according to the literature using Pathway Studio

• Identify DE genes that are linked to known diseases genes using Pathway Studio

• Report novel disease genes



Transcriptional network governing the angiogenic switch in human pancreatic cancer. Abdollahi et al

PNAS July 31, 2007 104(31): 12890–12895



Extras



Biomarker prioritization using expression data in Pathway Studio: biomarkers for intestinal bowel disease

1) DE downstream target is better than DE regulator

2) Secreted biomarkers are better than intracellular

Dissection of the Inflammatory Bowel Disease Transcriptome Using Genome-Wide cDNA Microarrays, PLoS, August 23, 2005



EXPRESSION VARIATION OF INDIVIDUAL BIOMARKERS IN PATIENTS’ IS UNLIKELY TO AFFECT IN-DEGREE HUBs

Sources of patient variations:- genetic- dietary & life-style- stress-related



Using Chip-On-Chip data to find major regulators in the Expression experiment



Example of proprietary algorithm that can be integrated into Pathway Studio using API

Algorithm finds inconsistencies between expression data and ResNet

100% consistence between expressionResNet data

TP53 label assigned by algorithm ACTVATED

Inconsistency between expressionResNet data

TP53 label assigned by algorithm ACTVATED

Explanations for inconsistency:1) Incorrect expression data – 50% of all cases2) Incorrect ResNet data – 10% of al cases3) Posttranscriptional regulation of TP53

ariadne genomics technology: extraction from the literature and network analysis dr. anton yuryev

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