Fernando J. Martin-Sanchez, Ph. D. Head, Health Bioinformatics Dept. National Institute of Health

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Fernando J. Martin-Sanchez, Ph. D.Head, Health
Bioinformatics Dept. National Institute of
Health Carlos IIIMinistry of Health and
Consumer AffairsMadrid, SPAIN EC IST BIOINFOMED
Study Coordinator

• Health Information Systems in the age of
  Post-Genomic Research
• CEN/TC251
• Joint Working Group Meeting
• Madrid
• June 3, 2002

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Agenda

• Presentation
• Overview of Bioinformatics
• Molecular Medicine and Individualised Healthcare
• The convergence between Medical Informatics and
  Bioinformatics
• Issues on integrating genetic data into health
  information systems
• EC IST BIOINFOMED Study

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Institute of Health Carlos IIIMinistry of
Health and Consumer Affairs

• Public Research Institute
• Scientific and technological support to the
  National Health System
• Competences in
• Epidemiology, Public health laboratories (Food,
  Microbiology, Environmental Health)
• Health Technology Assesment
• Biomedical research funding and coordination
• School of Public Health, Health Sciences Library
• New technologies - Telemedicine, Bioinformatics
  and genomics, Health information systems

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Forces driving the bioinformatics revolution

• The promise of health applications of the human
  genome information
• Proof of concept with the human genome
  sequencing
• Availability of new raw information
•         Sequences (genome projects), SNPs
  (variability)
•        Gene expression data (DNA arrays),
  Proteomics
• Interest of new users
•         Pharma and Biotech
•         Biomedical research centers
•         IT firms
•         Clinical (not yet?)
• New tools
•         Internet
•         Data mining
• New discipline
•         Innovation brand
•         Search for funding

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Overview of Bioinformatics

• Bioinformatics, Biocomputing, Computational
  Biology
• Interface between biotechnology and computer
  science
• Flavours
• Integration of relevant biomedical information
• Platform for in silico biology
• Focus on Health Applications
• From Genetics to Genomics to Postgenomics to
  Molecular Medicine

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The role of bioinformatics supporting genetics
Sequences
Alignments
Structures
Phylogenetic trees
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The role of Bioinformatics in support of genomics
Gene prediction
Sequencing
ATCGCGCTA
Annotation
Genome databases
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The Post-Genomics Era
Comparative Genomics (homology, evolution)
Proteomics (proteins)
Genome Project (DNA Consensus sequence)
Individual Genomics (mutations, SNPs)
Functional Genomics (mRNAs)
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Bioinformatics in support of Post-Genomic Research
Genomes
Proteomics
SNPs
DNA microarrays
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Bioinformatics in support of Systems Biology
Metabolic Pathways
Signaling pathways
Genetic Networks
Interactions
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New opportunities for health informatics

• Genome Project
• Interest for biologists
• One gene at a time
• Monogenic diseases
• Tedious genotyping
• DNA level
• Bioinformatics explosion

• Post-Genomics
• Clinical interest
• Hundreds or thousands of genes simultaneously
• Complex diseases
• High throughput genotyping
• DNA, RNA, Proteins
• Integration of clinical and genetic information

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Overview
Human Genetic Variation
Technologies
Data
Applications
Diagnosis Pharmaco-genetics
Individual genomics (SNPs and mutations)
Individualised healthcare
Genome
BIOINFORMATICS MEDICAL INFORMATICS
Functional genomics proteomics
Disease classification Pharmaco-genomics
Gene Expression DNA arrays MS, 2D ef
Information
Molecular medicine
Molecular causes of diseases
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Molecular Medicine and Individualised Healthcare

• New approaches Pharmacogenetics, DNA arrays,
  proteomics, SNPs, genetic diag.
• Molecular Medicine - Effort in explaining life
  and disease in terms of the presence and
  regulation of molecular entities
• Individualised Healthcare Application of
  genomics to identify individual predispositions
  to disease and to design therapies adapted to the
  genetic profiles of patients and that could be
  prescribed with guarantee of security and
  efficiency

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The convergence between MI and BI
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A model for studying interactions
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The application of informatics in Molecular
Medicine
?
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Bioinformatics in Health
Chris Sander, Bioinformatics (Editorial). Vol 17.
Nº1. 2001, p1-2
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Karolinska Institute, Sweden
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Bioinformatics and medical information

• EBI - SOFG - Standards and Ontologies for
  Functional Genomics, November, 2002, Hinxton,
  Cambridge, UK
• Topics include vocabularies such as chemical and
  biochemical nomenclature and the molecular
  biology vocabularies developed by the Gene
  Ontology Consortium
• But also vocabularies for phenotypes, anatomies
  and developmental stages and other areas such as
  diseases, pathology and toxicology

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Why do we prefer HI to MI?

• Continuity of healthcare (Patient, GP, secondary,
  Terciary)
• Growing information processing role of ALL health
  professionals (nurses, pharmacists, biologists,
  ...)
• Convergence with bioinformatics made easier
• Integration of environmental and genetic data

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Health Informatics subdivisions

• Information level
• (molecular, cellular, tissue, organ. Patient,
  disease, population)
• Clinical specialties or diseases
• (cancer informatics, cardiovascular, ...)
• User
• (patient, clinical, pharma, nurse, bio)
• Agent
• (HMO, hospital, government, ...)
• Technology
• (AI, Telemedicine, Decision making, imaging,
  genomics)

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Health Information levels
Public Health Informatics
Medical Informatics
Medical Imaging
Bioinformatics
Martin-Sanchez et al, Methods. Inf. Med. 2002,
4125-30
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(No Transcript)
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Types of medical data

• lab results
• administrative orders, appointments
• images
• signals, EKG
• microbiology results
• demographic
• familiar
• history of prescriptions
• GENETIC

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Genetic data special features

• multiple sources
• large amounts
• More static
• probabilistic
• Multilevel (DNA, RNA, Prot)
• Accumulative (SNPs, multifactorial diseases)
• Context-dependent
• Needs comparison with public databases

• Not quantitative
• Complex
• Informs about relatives, not only about patients
• Predictive power, even in the absence of clinical
  signs or symptoms
• Has the potential to generate a unique identifier
  profile for individuals.

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Sources of Genetic Data

• Genome and sequence databases
• EMBL
• Protein sequence and structures
• PDB, SwissProt
• Genetic diseases
• OMIM, GeneCards, GeneReviews
• Genetic tests  
• Geneclinics, EddNal
• Mutations
• Central variation databases HGMD
• Single Locus Databases
• SNPs
• (dbSNP)

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Examples

• Gene - RPS6KA4
• Mutations
• 76AgtC 83GgtC
• 112_117delAGGTCAinsTG
• K29_M29insQSK
• SNP rs1472728
• Submitter Handle TSC-CSHL
• Submitter Method ID TSC-WUGSC-1-2
• GenBanK Accession AC011382.2
• Length 673
• Flanking Sequence Information GATGGGACCA
  CTGGTAGGAG...
• Observed C/T
• No. of Chromosomes Sampled 16
• Allele C 0.437 / T 0.563

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Genetic data in medical coding systems

• ICD
• SNOMED
• UMLS
• MeSH
• LOINC
• GALEN

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Knowledge representationin Biology

• The MGED ontology - ontologies for describing
  gene expression experiments and data.
• TAMBIS ontology (TaO) an ontology of
  bioinformatics and molecular biology.
• RiboWeb an ontology describing ribosomal
  components, associated data and computations for
  processing those data.
• EcoCyc an ontology describing the genes, gene
  product function, metabolism and regulation
  within E. coli.
• Gene Ontology (GO) an ontology describing the
  function, the process and cellular location of
  gene products from eukaryotes.

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Related scientific activities

• Dec, 2001. Brussels EC Synergy between
  Research in Medical Informatics, Bioinformatics
  and Neuroinformatics
• Manchester March 2002 meeting
• Genotype To Phenotype Linking Bioinformatics
  and Medical Informatics Ontologies
• Nov 2002. AMIA Conference
• Bio-medical informatics one discipline
• EC IST BIOINFOMED
• ACMI - NLM

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BIOINFOMED

• Prospective Analysis on the Relationships and
  Synergy between Medical Informatics and
  Bioinformatics
• URL http//bioinfomed.isciii.es (starting from
  January 2002)
• Institute of Health Carlos III Madrid SPAIN
• Polytechnical University of Madrid (Prof. Victor
  Maojo) - SPAIN
• Linkoping University (Prof. Ankica Babic) -
  SWEDEN
• State of the Art and Inventory of resources of
  interest and standardisation initiatives
• Identification of key groups and priority
  research lines
• Collaboration between experts and groups
• Final report and Workshop (Nov 2002)

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Modelling

• MI Top-down approach from clinical
  manifestations to the underlying
  pathophysiological processes
• BI - Bottom-up approach, from genomic information
  to physiological function
• An integrated approach could provide a unified
  vision

Maojo, Martin-Sanchez et al, Journal of
Biomedical Informatics. In press
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BIOINFOMED
Methods and tools
Medical Inform.
Bioinform.
Synergy



Individualised Healthcare



Application
Synergy
Molecular Medicine



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Integrating genetic data into health information
systems

• Telemedicine genetic diagnosis networks --
  telegenetics
• Accessing genetic databases using clinical inputs
• Integrating genetic data into clinical trials
  infrastructures and methods
• Genetic data in clinical records
• Genetics in Clinical practice guidelines
• Adapting terminologies, vocabularies, ontologies

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Bioinformatics. Health applications

• SNP haplotype information management and
  analysis
• Disease reclassification based on gene expression
  data
• Clinical proteomics
• Systems Biology
• Pharmacogenomics
• Clinical-genetic databases
• Genomics of pathogenic micro-organisms

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Synergy (integrated use of genetic and clinical
information with an application in individualised
healthcare and/or molecular medicine)

• Virtual Tumor databases (clinical-genetic
  analysis)
• Decision-making support tools
• Integrated clinical-genetic workstations
• Interoperability between genetic lab and health
  information systems
• Linking phenotype to genotype in patients and
  populations
• Pharmacogenetics Databases
• Genome epidemiology
• Molecular imaging
• Computer models of disease

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Molecular imaging

• Medical imaging genomics
• Imaging molecular alterations that are the basis
  of disease rather than their effects
• Weissleder, R. Radiology 2001 219316-333

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Levels and techs
Tomorrow
Today
CT US
MRI Nuclear Optical Nano
Anatomy Physiology Metabolism Molecular
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Molecular imaging

• New markers for early disease detection
• Specific markers for therapy assesment
• Drug screening
• Imaging of gene expression

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Conclusions

• Interaction between MI and BI is needed
• Not reinventing the wheel
• Not making the same mistakes
• Collaboration is better
• integrated approach to disease
• Synergy will it be possible?
• New developments from scratch
• Birth of a new discipline?
• BIOMEDICAL INFORMATICS

MI
BI
MI
BI
MI
BI