Bioinformatics: Definitions, Challenges and Impact on Health Care Systems

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Bioinformatics Definitions, Challenges and
Impact on Health Care Systems

• Joyce Mitchell, Ph.D.
• University of Utah
• Sept 29, 2005
• NLMs Woods Hole Informatics Course

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Outline for Talk

• What is Bioinformatics?
• Health Informatics compared to Bioinformatics
• Problems considered in Bioinformatics
• Genomics, proteomics, transcriptomics, etc
• Genomics data and patient care
• Impact of Bioinformatics on Health Information
  Systems

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Central Dogma of Molecular Biology
Transcription
DNA
RNA
Protein
Phenotype
Phenotype
Translation
Replication
This happens in Cells.
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1. What is Bioinformatics?

• Definitions first

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NIH Working Definition

• Bioinformatics Research, development, or
  application of computational tools and approaches
  for expanding the use of biological, medical,
  behavioral or health data, including those to
  acquire, store, organize, archive, analyze, or
  visualize such data.
• http//www.bisti.nih.gov/CompuBioDef.pdf

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Another Definition

• An interdisciplinary area at the intersection of
  biological, computer, and information sciences
  necessary to manage, process, and understand
  large amounts of data, for instance from the
  sequencing of the human genome, or from large
  databases containing information about plants and
  animals for use in discovering and developing new
  drugs. www.isye.gatech.edu/tg/publications/ecolo
  gy/eolss/node2.html

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Another definitionNCBI (National Center for
Biotechnology Information

• Bioinformatics is the field of science in which
  biology, computer science, and information
  technology merge into a single discipline. The
  ultimate goal of the field is to enable the
  discovery of new biological insights and to
  create a global perspective from which unifying
  principles in biology can be discerned. There are
  sub-disciplines in bioinformatics.
• http//www.ncbi.nlm.nih.gov/About/primer/bioinform
  atics.html

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2. Health Informatics Compared to Bioinformatics

• Same methods, different application domains

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Different Areas of Strengths

• Bioinformatics has much more data available on
  the Internet than Health Informatics
• Much more progress on database integration across
  multiple data sources
• Health Informatics has much more need for
  aggregation of national statistics
• Much more progress on terminologies for
  integration of data

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Bioinformatics Health Informatics

• Bioinformatics is the study of the flow of
  information in biological sciences.
• Health Informatics is the study of the flow of
  information in patient care.
• These two field are on a collision course as
  genomics data becomes used in patient care.
• Russ Altman,MD, Ph.D., Stanford Univ.

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3. Problems Considered in Bioinformatics

• OMES and OMICS

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Omes and Omics

• Genomics
• Primarily sequences (DNA and RNA)
• Databanks and search algorithms
• Proteomics
• Sequences (Protein)
• Mass spectrometry, X-ray crystallography
• Databanks, knowledge bases, terminologies
• Functional Genomics (transcriptomics)
• Microarray data
• Databanks, analysis tools, traversal techniques
• Systems Biology (metabolomics)
• Metabolites and interacting systems
  (interactomics)
• Graphs, visualization, modeling, networks of
  entities

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Central Dogma of Molecular Biology
DNA
RNA
Protein
Phenotype
Phenotype
Genomics
Proteomics
Transcriptomics Functional Genetics
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Genome and Genomics

• Genome entire complement of DNA in a species
• Both nuclear and mitochondrial/chloroplast
• Variants among individuals
• Genomics study of the sequence, structure and
  function of the genome. Study of whole sets of
  genes rather than single genes.
• Comparative genomics study of the differences
  among species. Usually covers evolutionary
  studies of differences conservation over time.

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A Genome Database (e.g., GenBank)

• Consists of long strings of DNA bases ATCG..
• Consists of annotations of this database to
  attach meaning to the sequence data.
• Example entry from GenBank
• http//www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?val
  NM_000410doptgb Hemochromatosis gene HFE

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Human Genome Project

• Human Genome Project - International research
  effort
• Determine sequence of human genome and other
  model organisms
• Began 1990, completed 2003
• Next steps for 20,000 genes
• Function and regulation of all genes
• Significance of variations between people
• Cures, therapies, genomic healthcare

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The Human Genome Project has catalyzed striking
paradigm changes in biology - biology is an
information science.

• Leroy Hood, MD, PhD
• Institute for Systems Biology
• Seattle, Washington

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Genomes In Public Databases

• Published complete genomes
• Ongoing prokaryotic genomes
• Ongoing eukaryotic genomes

1560
http//www.genomesonline.org/
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Genomics activities

• Sequence the genes and chromosomes done by
  breaking the DNA into parts
• Map the location of various gene entities to
  establish their order
• Compare the sequences with other known sequences
  to determine similarity
• Across species, conserved sequence motifs
• Predict secondary structure of proteins
• Create large databases GenBank, EMBL, DDBJ
• Develop algorithms and similarity measures
• BLAST and its many forms

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Central Dogma of Molecular Biology
DNA
RNA
Protein
Phenotype
Phenotype Tissues Organs Organisms
Genomics
Proteomics
Transcriptomics Functional Genetics
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Proteome and Proteomics

• Proteome the entire set of proteins (and other
  gene products) made by the genome.
• Proteomics study of the interactions among
  proteins in the proteome, including networks of
  interacting proteins and metabolic
  considerations. Also includes differences in
  developmental stages, tissues and organs.

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Protein Functions

• Catalysis
• Transport
• Nutrition and storage
• Contraction and mobility
• Structural elements
• Cytoskeleton
• Basement membranes

• Defense mechanisms
• Regulation
• Genetic
• Hormonal
• Buffering capacity

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Protein Databases

• SwissProt
• PIR
  http//www.pir.uniprot.org/
• GENE http//www.ncbi.nlm.nih.gov/gene
• InterPro http//www.ebi.ac.uk/interpro/
• Correspond to (and derived from) Genome data
  bases
• All connected by Reference Sequences (NCBI)

UniProt
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Gene/Protein Database entries

• HFE record in Entrez GENE (NCBI)
• http//www.ncbi.nlm.nih.gov/entrez/query.fcgi?db
  genecmdretrievedoptGraphicslist_uids3077

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Structure Function Determination

• X-ray crystallography
• Nuclear magnetic resonance spectroscopy and
  tandem MS/MS
• Computational modeling
• Sequence alignment from others
• Homology modeling

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Structure Databases

• Contain experimentally determined and predicted
  structures of biological molecules
• Most structures determined by X-ray
  crystallography, NMR
• Example MMDB molecular modeling db
  http//www.ncbi.nlm.nih.gov/Structure/MMDB/mmdb.sh
  tml
• HFE Entry
• http//www.ncbi.nlm.nih.gov/Structure/mmdb/mmdbsrv
  .cgi?form6dbtDoptsuid9816

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Protein Interaction Databases

• Record observations of protein-protein
  interactions in cells
• Attempts to detail interactions observed in
  thousands of small-scale experiments described in
  published articles
• Examples
• BIND Biomolecular Interaction Network Database
• DIP Database of Interacting Proteins
• MIPS Munich Information Center for Protein
  Sequences
• PRONET Protein interaction on the Web
• Many others, both academic and commercial

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Central Dogma of Molecular Biology
DNA
RNA
Protein
Phenotype
Phenotype
Genomics
Proteomics
Transcriptomics Functional Genetics
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Proteome vs Transcriptome

• Functional genomics (transcriptomics) looks at
  the timing and regulation of the gene products
  (both RNA and proteins)
• This is different from looking at what gene
  products can be produced it looks at the
  circumstances under which production occurs.
• Involves experimental conditions.

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Functional Genomics Microarrays

• Transcriptome and transcriptomics
• High throughput technique designed to measure the
  increase in RNA (or sometimes proteins, tissues,
  etc) in a cell in response to an experiment.
• Also called gene expression analysis
• Microarrays called gene chips (although now
  there are protein and tissue chips)

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How Do Microarrays Work?

• Conceptual description
• Set of targets (cDNA, proteins, tissues, etc) are
  immobilized in predetermined positions on a
  substrate
• Solution containing tagged molecules capable of
  binding to the targets is placed over the targets
• Binding occurs between targets and tagged
  molecules.
• Fluorescent tags allow you to visualize which
  targets have been bound (and tell you something
  about the molecules that were present in your
  solution).

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Animation of Microarrays

• http//www.bio.davidson.edu/courses/genomics/chip/
  chip.html

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How Do Microarrays Work?

• Conceptual description
• Set of targets (cDNA, proteins, tissues, etc) are
  immobilized in predetermined positions on a
  substrate
• Solution containing tagged molecules capable of
  binding to the targets is placed over the targets
• Binding occurs between targets and tagged
  molecules.
• Fluorescent tags allow you to visualize which
  targets have been bound (and tell you something
  about the molecules that were present in your
  solution).

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How Spotted Arrays Work

• Result
• Spots where cDNA from the reference sample
  hybridized look green
• Spots where cDNA from the experimental sample
  hybridized look red
• Spots where cDNA from both samples hybridized
  look yellow (greenredyellow)
• Spots with little/no cDNA hybridized look black

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Uses of Expression Profiling

• Pharmaceutical research
• ID drug targets by comparing expression profile
  of drug-treated cells with those of cells
  containing mutations in genes encoding known drug
  targets
• Disease Dx and Tx
• Distinguish morphologically similar cancers
• DLBCL (Poulsen et al (2005) Microarray-based
  classification of diffuse large B-cell lymphomas
  European Journal of Haematology 74(6)453-65.))
• Therapy potential
• Rabson AB, Weissmann D. From microarray to
  bedside targeting NF-kappaB for therapy of
  lymphomas. Clin Cancer Res. 2005 Jan 111(1)2-6.

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Future Applications

• Diagnostic tool to screen for infective agents
• Chip imprinted with set of pathogenic genomes
  used to identify bacterial, viral, or parasite
  genomic material in patients body fluids
• Diagnostic chip to check for mutations involved
  in drug-gene interactions.

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Experimental Design (2)

• A fundamental challenge of microarray
  experiments underdetermined systems

Kohane IS, Kho AT, Butte AJ. Microarrays for an
Integrative Genomics. (The MIT Press Cambridge,
MA 2003), p. 11.
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MGED Microarray gene expression data
http//www.mged.org/Workgroups/MAGE/mage.html
http//www.mged.org/Workgroups/MIAME/miame.html
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Public Microarray Data Repositories

• Major public repositories
• GEO (NCBI)
• http//www.ncbi.nlm.nih.gov/geo/
• ArrayExpress (EBI)
• http//www.ebi.ac.uk/arrayexpress/

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Standards and Repositories

• Brazma, A, et al. Minimum information about a
  microarray experiment (MIAME)-toward standards
  for microarray data. Nature Genetics. 2001
  Dec29(4)373.
• http//www.nature.com/cgi-taf/DynaPage.taf?file/
  ng/journal/v29/n4/full/ng1201-365.html
• Ball, CA, et al. Submission of Microarray Data to
  Public Repositories. PLoS Biology. 2004
  September 2 (9) e317
• http//www.pubmedcentral.nih.gov/articlerender.fc
  gi?toolpubmedpubmedid15340489

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Controlled Vocabularies

• Genomics, proteomics, and especially microarray
  techniques have created a large need for
  controlled vocabularies to assist the analyses
  across multiple entities species.
• Taxonomy systematic classification of objects
  according to relationships.
• Ontologies
• An organizational framework for concepts

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Controlled Vocabularies in Bioinformatics

• The Gene Ontology http//www.geneontology.org/
• Knowledge capture (the ontology itself)
• Annotation of gene products (for comparisons)
• The MGED Ontology (arising from MIAME)
• http//mged.sourceforge.net/
• Annotation of microarray experiments for public
  repositories
• Clinical Bioinformatics Ontology
• Annotation of gene tests in electronic medical
  records
• http//www.cerner.com/cbo
• MIAPE from Proteomics Standards Initiative (PSI)
• http//psidev.sourceforge.net/

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4. Genomics Data and Patient Care

• From genotype to phenotype

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Bioinformatics and Patient Care

• Understanding a persons genome ushers the era of
  Personalized Medicine
• Obviously you should keep track of
  health-related genetic data in the EMR.
• The 9-11 disaster showed you need to know the
  genomic variant information as well.
• Cash et al. Forensic bioinformatics in the wake
  of the World Trade Center Disaster. PSB
  2003638-653.

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Human Disease Gene Specifics

• Genes linked to human diseases (9-2004)
• 425 in 2 yrs
• 1700/20,000 9 of loci

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Genetic Medicine is not new

• Karl Landsteiner started genetic medicine over
  100 years ago (1903)
• Blood transfusions worked off the ABO blood group
  system.
• Landsteiner got the Nobel Prize in 1930 for his
  work.
• http//nobelprize.org/medicine/laureates/1930/land
  steiner-bio.html

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Genomic Medicine is New

• What to do with all of this genetic information
  and every person being unique?
• And the information about genetic conditions is
  available on the Internet.

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Genomics Data and Patient Care

• Where do you find the data for genes causing
  human diseases?
• What do you do with genetic data in electronic
  medical records?

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Where do you find the data for genes causing
human diseases?

• Study on availability of genetic data on health
  implications of the HGP.
• Mitchell, McCray, Bodenreider. Methods Inf Med
  2003 42557-63.

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Questions

• What genes cause the condition?
• What are the normal function of the gene?
• What mutations have been linked to diseases?
• How does the mutation alter gene function?
• What laboratories are performing DNA tests?
• Are there gene therapies or clinical trials?
• What names are used to refer to the genes and the
  diseases?
• What other conditions are linked to these same
  genes?

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You can find the answers online

• but it is not easy answers in many places
• Cant navigate by genes names - must use hot
  links and numeric identifiers
• The number and function of alternate forms of the
  protein are inconsistently reported
• Synonymy (many names, same meaning) and polysemy
  (same name, different meanings) cause confusion
• Upper and lower case are used for species
  distinctions

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Major Challenges of Navigation

• Complexity of data
• Dynamic nature of the data
• Diverse foci and number of data/knowledge base
  systems
• Data and knowledge representation lack standards
• Can navigate if you know what you are looking for.

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Genetics Home Reference

• Consumer health resource to help the public
  navigate from phenotype to genotype.
• Focus on health implications of the Human Genome
  Project.
• http//ghr.nlm.nih.gov
• Mitchell, Fun, McCray, JAMIA, 2004 Nov
  11(6)439-437

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Hands-on with GHR

• Scavenger hunt with hemochromatosis and the genes
  that influence it.
• Explore the Genetics Home Reference by answering
  the following questions. Start at
  http//ghr.nlm.nih.gov .

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GHR Scavenger Hunt

• How common is hemochromatosis?
• How many genes have been proven to be involved in
  hemochromatosis when the genes are mutated?
• What are the symbols for these genes?
• Can you find the link to MedlinePlus with health
  information on hemochromatosis?

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GHR Scavenger Hunt

• What are the names of the patient support
  associations for hemochromatosis?
• One synonym for this condition is bronze
  diabetes. Can you find a reason for this?
• What kind of damage is done to the liver of
  people with hemochromatosis?

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GHR Scavenger Hunt

• For the genes involved in hemochromatosis, how
  many of them are available as a DNA test?
• Give one place where you would choose to send a
  tissue sample for DNA testing.
• What sites are listed under Research Resources
  for the TFR2 gene?
• How many alternately spliced proteins for TFR2?
• In what tissues is this gene expressed?

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GHR Scavenger Hunt

• How do people inherit hemochromatosis?
• Do the genes involved in hemochromatosis cause
  other health conditions when they are mutated?
• Can you find a protein sequence for one of the
  genes?
• What clinical trials are available for
  hemochromatosis patients close to where you live?
  

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5. Impact of Bioinformatics on Health Information
Systems

• Electronic Medical Record
• Public Health Systems

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Genetics is Impacting Medicine Today!

• 1700 genes health conditions
• gt 1100 gene tests for diagnosis
• Relate to diagnosis, therapy, drug dosage,
  occupational hazards, reproductive plans, health
  risks, .

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Well-known Examples

• Pharmacogenetics
• CYP450 alleles exaggerated, diminished or
  ultra-rapid drug responses. E.G., Warfarin. 93
  of patients are OK on standard doses. 7 of
  patients have severe hemorrhage. CYP2C92 and
  CYP2C93 most severe of 6 known mutations.
• Environmental susceptibility
• Sickle Cell trait carrier and malaria parasite
• Nutrition
• PKU and avoidance of phenylalanine

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Another Example Iressa (gefitinib)

• Non-small cell lung CA 140,000 pt/yr
• Iressa (Astra Zeneca) causes remission in 1 of 10
  patients if taken daily for life.
• Iressa efficacy correlates with EGFR mutation in
  the tumor. Now have gene testing for EGFR so can
  target appropriate people. http//www.sciencemag.o
  rg/cgi/content/full/305/5688/1222a
• BUT Astra Zeneca cant make money on only
  14,000 per year.
• http//www.ncbi.nlm.nih.gov/entrez/dispomim.cgi?id
  131550

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Collie Dog Example

• Collies are more sensitive to the anti-parasytic
  drug invermectin and loperamide (imodium) and
  other drugs
• 75 of collies in US have a mutation in the mdr1
  gene causing multiple drug sensitivity (50
  drugs). Can cause death or neurological damage.
• Now have testing available.
• http//www.wral.com/money/3565592/detail.html

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Implications for Health Care System

• More gene tests will be ordered. reports of 300
  increase in gene tests in 2003.
• Arch Pathol Lab Med 2004, 128(12)1330-1333
• The FDA will regulate panels of tests.
• http//www.fda.gov/bbs/topics/news/2004/new01149.h
  tml
• Non-discrimination laws for insurance and
  employment would open a floodgate.
• Preventive healthcare will play a larger part.
• Environmental risk factors dictate OSHA-type
  approach to worker empowerment and education
  about safe behavior

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Example Hemochromatosis

• 2 copies of mutated HFE gene - too much iron
  absorbed from diet, which accumulates. Causes
  arthritis, liver disease, diabetes, skin
  discoloration.
• (1 million people in US)
• HFE gene regulates the storage, transport and
  absorption of iron
• Labs doing gene tests use different techniques
  full sequence vs limited analysis

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A Portion of the HFE DNA Sequence

• ATGGGCCCGCGAGCCAGGCCGGCGCTTCTCCTCCTGATGCTTTTGCAGAC
  CGCGGTCCTGCAGGGGCGCTTGCTGCGTTCACACTCTCTGCACTACCTCT
  TCATGGGTGCCTCAGAGCAGGACCTTGGTCTTTCCTTGTTTGAAGCTTT
  GGGCTACGTGGATGACCAGCTGTTCGTGTTCTATGATCATGAGAGTCGCC
  GTGTGGAGCCCCGAACTCCATGGGTTTCCAGTAGAATTTCAAGCCAGATG
  TGGCTGCAGCTGAGTCAGAGTCTGAAAGGGTGGGATCACATGTTCACTG
  TTGACTTCTGGACTATTATGGAAAATCACAACCACAGCAAGGAGTCCCAC
  ACCCTGCAGGTCATCCTGGGCTGTGAAATGCAAGAAGACAACAGTACCGA
  GGGCTACTGGAAGTACGGGTATGATGGGCAGGACCACCTTGAATTCTGC
  CCTGACACACTGGATTGGAGAGCAGCAGAACCCAGGGCCTGGCCCACCAA
  GCTGGAGTGGGAAAGGCACAAGATTCGGGCCAGGCAGAACAGGGCCTACC
  TGGAGAGGGACTG

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A Portion of the HFE DNA Sequence

• GCACAAGATTCGGG GGACAAGATTCGGG
• His CAU and CAC
• Asp GAU and GAC
• A Mutation in position 225 changes C to G.
• Changes a part of the protein. (histadine to
  aspartic acid at position 63)

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Amino Acid Sequence for HFE

• MGPRARPALLLLMLLQTAVLQGRLLRSHSLHYLFMGASEQDLGLSLFEAL
  GYVDDQLFVFYD H D ESRRVEPRTPWVSSRISSQMWLQL
  SQSLKGWDHMFTVDFWTIMENHNHSKESHTLQVILGCEMQEDNSTEGYWK
  YGYDGQDHLEFCPDTLDWRAAEPRAWPTKLEWERHKIRARQNRAYLERD
  CPAQLQQLLELGRGVLDQQVPPLVKVTHHVTSSVTTLRCRALNYYPQNI
  TMKWLKDKQPMDAKEFEPKDVLPNGDGTYQGWITLAVPPGEEQRYTCQV
  EHPGLDQPLIVIWEPSPSGTLVIGVISGIAVFVVILFIGILFIILRKRQG
  SRGAMGHYVLAERE
• His63Asp in ONE chromosomes
• Cys282Tyr in ONE chromosome (not shown)

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Report Back from Full Sequence Lab

• Reference sequences for transcript variant 1 for
  the HFE gene. NM_000410 NP_000401
• Consensus CDS (CCDS) CCDS4578.1
• Mutant phenotype changes
• His63Asp Cys282Tyr (2 mutations)
• Polymorphisms noted
• AA position 59 VAL53MET 157GA (freq 5)

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Special health concerns HFE

• For person with dx
• For family members

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Dilemmas

• The reference sequence ties you to external data
  sources that change
• The protein has eleven transcript variants
• Mutant phenotype is noted as an amino acid change
• Polymorphisms are noted as nucleotide change
• These results have implications for other family
  members in addition to the patient

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What Should You Store in the EMR?

• Do you put the DNA sequence for the gene into the
  EMR? Where do you put it?
• Do you just store meta-data about the DNA
  sequence? HFE test abn or (his63asp cys282tyr)
  What about the normal variants?
• If you dont store the sequence, what do you do
  when the reference sequence changes?
• How do you trigger alerts and reminders? And for
  what? People with hemochromatosis need special
  screening and check-ups.

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Genetic data in electronic medical records?

• Implications for component systems
• Laboratory
• Pharmacy
• Computerized order entry
• Documentation and notes
• Knowledge management
• Alerts and reminders
• Finding patients matching profiles
• Practice guidelines and clinical trials

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Genome Data and Other Information Systems

• Genomic information will be pervasive in all
  healthcare information systems.
• Also in public health systems
• Newborn screening
• Tissue and organ banks
• DOD requires DNA samples
• Bioterrorism and homeland security
• Identification of World Trade Center victims
• Privacy and security issues will remain with us
  always but are manageable.

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Summary

• Informatics is the enabler of personalized,
  genomic medicine.
• Personalized medicine requires a combination of
  medical informatics and applied bioinformatics
  (and a lot more).

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Informatics will be a very dynamic discipline for
eons to come!

• Your week at Woods Hole is the first step to an
  exciting future.

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The End

• Joyce Mitchell, PhD
• University of Utah