Research In the Post-Genomics

EraMartina McGloughlin,

Biotechnology Program and Life Sciences Informatics Program

UC Davis

2

“Biology in the 21st century will increasingly become an information science”

Leroy Hood, Jan 11, 1999

“Any cell has in it a billion years of experimentation by its ancestors”

Max Delbruck, 1949

“Biology in the 21st century will increasingly become an information science”

Leroy Hood, Jan 11, 1999

“Any cell has in it a billion years of experimentation by its ancestors”

Max Delbruck, 1949

UC Davis Biotechnology Program

UC Systemwide Life Sciences Informatics Program

UC Davis Biotechnology Program

UC Systemwide Life Sciences Informatics Program

3

The massive interest and commitment of resources in both the public and private sectors flows from the generally-held perception that genomics will be the single most fruitful approach to the acquisition of new information in basic and applied biology in the next several decades.

If genomics were only to be a tool for the basic biologist, the benefits of this approach would be staggering, yielding new insights into fundamental processes such as cell division, differentiation, transformation, the development and reproduction of organisms and the diversity of populations.

The rewards in applied biology, however, have clearly attracted the private sector and public interest. These include the promise of facile new approaches for drug discovery, new understanding of metabolic processes and new approaches to determining qualitative and quantitative traits in plants and animals for breeding and genetic engineering.

The massive interest and commitment of resources in both the public and private sectors flows from the generally-held perception that genomics will be the single most fruitful approach to the acquisition of new information in basic and applied biology in the next several decades.

If genomics were only to be a tool for the basic biologist, the benefits of this approach would be staggering, yielding new insights into fundamental processes such as cell division, differentiation, transformation, the development and reproduction of organisms and the diversity of populations.

The rewards in applied biology, however, have clearly attracted the private sector and public interest. These include the promise of facile new approaches for drug discovery, new understanding of metabolic processes and new approaches to determining qualitative and quantitative traits in plants and animals for breeding and genetic engineering.

GenomicsGenomics

4

Typed in 10-pitch font, one human sequence would stretch for more than 5,000 miles. Digitally formatted, it could be stored on one CD-ROM. Biologically encoded, it fits easily within a single cell.

One Human Sequence

5

Organism #of genes % genes with Comp. date inferred function for genome

sequencing

E. Coli 4,288 60 1997

Yeast 6,600 40 1996

C. Elegans 19,000 40 1998

Drosophila 12,000-14,000 25 1999

Arabidopsis 25,000 40 2000

Mouse 26,000-40,000 10-20 2002

Human 26,383-39,114 10-20 2001

Organism #of genes % genes with Comp. date inferred function for genome

sequencing

E. Coli 4,288 60 1997

Yeast 6,600 40 1996

C. Elegans 19,000 40 1998

Drosophila 12,000-14,000 25 1999

Arabidopsis 25,000 40 2000

Mouse 26,000-40,000 10-20 2002

Human 26,383-39,114 10-20 2001

6

Paradigm Shift in Biology

The new paradigm, now emerging, is that all the ‘genes’ will be known (in the sense of being resident in databases available electronically), and that the starting point of a biological investigation will be theoretical. An individual scientist will begin with a theoretical conjecture, only then turning to experiment to follow or test that hypothesis.

The new paradigm, now emerging, is that all the ‘genes’ will be known (in the sense of being resident in databases available electronically), and that the starting point of a biological investigation will be theoretical. An individual scientist will begin with a theoretical conjecture, only then turning to experiment to follow or test that hypothesis.

Walter Gilbert. 1991. Towards a paradigm shift in biology. Nature, 349:99.Walter Gilbert. 1991. Towards a paradigm shift in biology. Nature, 349:99.

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Paradigm Shift in Biology

To use [the] flood of knowledge, which will pour across the computer networks of the world, biologists not only must become computer literate, but also change their approach to the problem of understanding life.

To use [the] flood of knowledge, which will pour across the computer networks of the world, biologists not only must become computer literate, but also change their approach to the problem of understanding life.

Walter Gilbert. 1991. Towards a paradigm shift in biology. Nature, 349:99.

Walter Gilbert. 1991. Towards a paradigm shift in biology. Nature, 349:99.

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What’s Really Next

The post-genome era in biological research will take for granted ready access to huge amounts of genomic data.

The challenge will be understanding those data and using the understanding to solve real-world problems...

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Fundamental Dogma

DNA

RNA

Proteins

Circuits

Phenotypes

Populations

GenBankEMBLDDBJ

MapDatabases

SwissPROTPIR

PDB

Gene Expression?

Clinical Data ?

Regulatory Pathways?Metabolism?

Biodiversity?

Neuroanatomy?

Development ?

Molecular Epidemiology?

Comparative Genomics?

the post-genomic era will need many more to collect, manage, and publish the coming flood of new findings.

the post-genomic era will need many more to collect, manage, and publish the coming flood of new findings.

Although a few databases already exist to distribute molecular information,

Although a few databases already exist to distribute molecular information,

If this extension covers functional genomics, then “functional genomics” is equivalent to biology.

If this extension covers functional genomics, then “functional genomics” is equivalent to biology.

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There are Problems with the HGP . ..

• The actual sequence data makes up only 16% of the content; the other 84% is annotated.

• The is leads to a number of issues:

– How to structure databases for mining

– How to establish control vocabularies to establish integrity of searches

– What new algorithms are needed to facilitate processing and correlating the petabytes (1015 bytes) of information

– How can protein function be extracted for the purposes of diagnostics, drug discovery and therapeutics

Bioinformatics - Two ViewsUSERS

• of Information

• of Tools

• of Instrumentation

• In-Silico Modeling

INTERPRETERS

• of Information

DEVELOPERS*

• of Information

• of Tools

• of Instrumentation

• of Architecture/Storage

• Algorithms

• Modeling Strategies

• Visualization

Per Pete Smietana, VP Lumicyte

*

*These people are in highest demand

12

Typical BioinformaticsMulti-Disciplinary Training

•Scientists – Biology, Molecular Genetics, Clinical

Biochemistry, Protein Structure Chemistry

•Mathematicians – Statistics, Algorithms, Image processing

•Computer Scientists– Database, User Interface/Visualizations,

Networking (Internets/Intranets), Instrument Control

Typical BioinformaticsMulti-Disciplinary Training

•Scientists – Biology, Molecular Genetics, Clinical

Biochemistry, Protein Structure Chemistry

•Mathematicians – Statistics, Algorithms, Image processing

•Computer Scientists– Database, User Interface/Visualizations,

Networking (Internets/Intranets), Instrument Control

13

Typical BioinformaticsMulti-Disciplinary Functions

•Scientists– Experimental Design & Interpretation– Laboratory Protocols & Standards/Controls

•Mathematicians– Analysis & Correlation of Data– Validation methodologies

•Computer Scientists– Information Storage / Control Vocabulary– Data Mining

Typical BioinformaticsMulti-Disciplinary Functions

•Scientists– Experimental Design & Interpretation– Laboratory Protocols & Standards/Controls

•Mathematicians– Analysis & Correlation of Data– Validation methodologies

•Computer Scientists– Information Storage / Control Vocabulary– Data Mining

Bioinformatics Functional Organization

InfrastructureSupport

Computer operationsDatabase Admin

SkillsetComputerNetworkDatabase

ApplicationsSupport

Help DeskTraining

SkillsetProgram knowledge

CommunicationTeaching

ResearchSupport

Scientific supportGene discoveryData Smelting

SkillsetMolecular Biology

ComputerCommunications

ResearchBioinformatics research

Algorithm develompment

New TechnologiesSkillset

Computational BiologyBioinformaticsProgramming

SystemsDevelopment

Program developmentSystem integration

Database designSkillset

Systems analysisDatabase development

Programming

Gene Discovery Genomics

SequencingMolecular BiologyHigh Throughput

Screening

DatabaseSupport

AdministrationCurationSkillset

Molecular BiologyComputer

Communications

15

TGT AAT AGT TAT ATT TTCATT ATA AAT TGT GTT TGT AGA CAT CAT AAA TTT AAAACA TGG CTT TTT AAC CTGATA AAT CCT ACG AAT ATTTGT AAT AGT TAT GTT ATTGCA GTA AGT ACC GTT TGT ATT ATA AAT TGT GTT CTG

TGT AAT AGT TAT ATT TTCATT ATA AAT TGT GTT TGT AGA CAT CAT AAA TTT AAAACA TGG CTT TTT AAC CTGATA AAT CCT ACG AAT ATTTGT AAT AGT TAT GTT ATTGCA GTA AGT ACC GTT TGT ATT ATA AAT TGT GTT CTG

Which genes are turned off then on ? Courtesy of Dr. Young Moo Lee

16

0

200,000,000

400,000,000

600,000,000

800,000,000

1,000,000,000

1,200,000,000

1,400,000,000

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105

GenBank Release Numbers

9493929190898887 95 96 97

Growth in GenBank is exponential. Recently more data were added in ten weeks than were added in the first ten years of the project.

Growth in GenBank is exponential. Recently more data were added in ten weeks than were added in the first ten years of the project.

Base Pairs in GenBank

17

Rhetorical Question

Which is likely to be more complex:

• identifying, documenting, and tracking the whereabouts of all parcels in transit in the US at one time

• identifying, documenting, and analyzing the structure and function of all individual genes in all economically significant organisms; then analyzing all significant gene-gene and gene-environment interactions in those organisms and their environments

Which is likely to be more complex:

• identifying, documenting, and tracking the whereabouts of all parcels in transit in the US at one time

• identifying, documenting, and analyzing the structure and function of all individual genes in all economically significant organisms; then analyzing all significant gene-gene and gene-environment interactions in those organisms and their environments

18

Business Factoids

United Parcel Service:

• uses two redundant 3 Terabyte (yes, 3000 GB) databases to track all packages in transit.

• has 4,000 full-time employees dedicated to IT

• spends one billion dollars per year on IT

• has an income of 1.1 billion dollars, against revenues of 22.4 billion dollars

United Parcel Service:

• uses two redundant 3 Terabyte (yes, 3000 GB) databases to track all packages in transit.

• has 4,000 full-time employees dedicated to IT

• spends one billion dollars per year on IT

• has an income of 1.1 billion dollars, against revenues of 22.4 billion dollars

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Examples of Biotech/IT Fusion Technologies

Genomics, proteomics and bioinformatics

Combinatorial –chemistryPeptide libraries- tea bags, beads

Combinatorial -biologyDirected evolutionDNA Shuffling, Molecular Breeding

High throughput analysis Nucleic Acid based

SequencingMicroarrays

•Photolithography•Mirrors•Spotted Chips•Semi-conductor

Protein based2-D, electrospray/nanospray MS: MALDI-TOF, LC/MS/MS, SELDI

Imaging/optical biology

Biosensors, Bioelectronics and Bionetworks (Nanotechnology)

Examples of Biotech/IT Fusion Technologies

Genomics, proteomics and bioinformatics

Combinatorial –chemistryPeptide libraries- tea bags, beads

Combinatorial -biologyDirected evolutionDNA Shuffling, Molecular Breeding

High throughput analysis Nucleic Acid based

SequencingMicroarrays

•Photolithography•Mirrors•Spotted Chips•Semi-conductor

Protein based2-D, electrospray/nanospray MS: MALDI-TOF, LC/MS/MS, SELDI

Imaging/optical biology

Biosensors, Bioelectronics and Bionetworks (Nanotechnology)

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Genomics, Proteomics and Bioinformatics Genomics is operationally defined as investigations into the

structure and function of very large numbers of genes undertaken in a simultaneous fashion.

Structural genomics includes the genetic mapping, physical mapping and sequencing of entire genomes.

Comparative genomics means information gained in one organism can have application in other even distantly related organisms. This enables the application of information gained from facile model systems to agricultural and medical problems. The nature and significance of differences between genomes also provides a powerful tool for determining the relationship between genotype and phenotype through comparative genomics and morphological and physiological studies.

Functional genomics Phenotype is logically the subject of functional genomics. Genome sequencing for most organisms of interest will be complete within the near future, ushering in the so called "post-genome era." Walter Gilbert directly speculated on the nature of biology in the "post-genome era": "The new paradigm, now emerging, is that all genes will be known (in the sense of being resident in databases available electronically), and that the starting point of a biological investigation will be theoretical.“

Genomics, Proteomics and Bioinformatics Genomics is operationally defined as investigations into the

structure and function of very large numbers of genes undertaken in a simultaneous fashion.

Structural genomics includes the genetic mapping, physical mapping and sequencing of entire genomes.

Comparative genomics means information gained in one organism can have application in other even distantly related organisms. This enables the application of information gained from facile model systems to agricultural and medical problems. The nature and significance of differences between genomes also provides a powerful tool for determining the relationship between genotype and phenotype through comparative genomics and morphological and physiological studies.

Functional genomics Phenotype is logically the subject of functional genomics. Genome sequencing for most organisms of interest will be complete within the near future, ushering in the so called "post-genome era." Walter Gilbert directly speculated on the nature of biology in the "post-genome era": "The new paradigm, now emerging, is that all genes will be known (in the sense of being resident in databases available electronically), and that the starting point of a biological investigation will be theoretical.“

21

Genomics, Proteomics and Bioinformatics

Proteomics At the molecular level, phenotype includes all temporal and spatial aspects of gene expression as well as related aspects of the expression, structure, function and spatial localization of proteins. The Proteome is the set of all expressed proteins for a given organism.

The next hierarchical level of phenotype considers how the proteome within and among cells cooperates to produce the biochemistry and physiology of individual cells and organisms. “Physiomics" is a descriptor for this approach. “Phenomics" The final hierarchical levels of phenotype include anatomy and function for cells and whole organisms.

Bioinformatics: Computational or algorithmic approaches to the production of information from large amounts of biological data, include prediction of protein structure, dynamic modeling of complex physiological systems or the statistical treatment of quantitative traits in populations in order to determine the genetic basis for these traits.

Unquestionably, bioinformatics will be an essential component of all research activities utilizing structural and functional genomics approaches

Genomics, Proteomics and Bioinformatics

Proteomics At the molecular level, phenotype includes all temporal and spatial aspects of gene expression as well as related aspects of the expression, structure, function and spatial localization of proteins. The Proteome is the set of all expressed proteins for a given organism.

The next hierarchical level of phenotype considers how the proteome within and among cells cooperates to produce the biochemistry and physiology of individual cells and organisms. “Physiomics" is a descriptor for this approach. “Phenomics" The final hierarchical levels of phenotype include anatomy and function for cells and whole organisms.

Bioinformatics: Computational or algorithmic approaches to the production of information from large amounts of biological data, include prediction of protein structure, dynamic modeling of complex physiological systems or the statistical treatment of quantitative traits in populations in order to determine the genetic basis for these traits.

Unquestionably, bioinformatics will be an essential component of all research activities utilizing structural and functional genomics approaches

Medical Bioinformatics: What is it?

Laboratory/Clinical Laboratory/Clinical ExperimentsExperiments

Biological Biological InterpretationInterpretation

InformaticsInformatics

•Hi-throughput Screening Data Hi-throughput Screening Data •genotype sequencinggenotype sequencing• functional assaysfunctional assays•DNA libraryDNA library

•Patient Clinical DataPatient Clinical Data•cancer phenotypecancer phenotype•outcomes, treatments, ageoutcomes, treatments, age

•Patient SamplesPatient Samples•TissuesTissues•TumorsTumors

•Model SystemsModel Systems•RatsRats•cultured tissuescultured tissues

•Published LiteraturePublished Literature•Scientific/Medical ExpertsScientific/Medical Experts

•Sample/Experiment TrackingSample/Experiment Tracking•Data Processing, Quality ControlData Processing, Quality Control•Statistical AnalysesStatistical Analyses

•Sequence Matching/AnnotationSequence Matching/Annotation•Functional SignificanceFunctional Significance

•User Access to ResultsUser Access to Results

What’s in a name?

SequenceAnalysis

DatabaseHomologySearching

MultipleSequence

Alignment

HomologyModelingDocking

ProteinAnalysis

Proteomics

3DModeling

SampleRegistration &

TrackingIntegrated

DataRepositories

CommonVisual

Interfaces

IntellectualPropertyAuditing

Bio Informatics

GenomeMapping

Gene Discovery Informatics

Microdissection

Create DNA Libraries

Signature Hybridization

Clusteringby Signature

Expression Profiles

DifferentialExpression

DNASequencing

GeneAssignments

FunctionalPredictions

MicroArrays

Functional Assays

Small MoleculeDrugs

Tissues &Cell Lines

In situHybridization

ClonesDatabase

DNALibrariesDatabase

AnnotatedSequenceDatabase

Assays &ValidationDatabase

ClusteringDatabase

Tissue &Cell LinesDatabase Small

MoleculeDatabase Micro

ArrayDatabase

In SituHybridiz-

ation

IM Intensive Research Groups

PharmacologyScreening

Assay AnalysisAnimal Data

RoboticsSample Management

ChemicalInformatics

ChemistrySMDD

BioinformaticsGenomics

Gene ExpressionTarget Discovery

ComputationalBiologyModelingStructure

DesktopResearch

Experimental Data

InformationManagement

InfrastructureSupport

Cancer Gene Discovery Knowledgebase

User'sWeb Browser

DNA SequenceDNA SequenceProprietary

Relational DatabaseHomology searchesFunctional Profiles

Cancer TissueInventoryPatient DataPathologyTissue Info

Functional/ValidationMicroarrays

In situ HybridizationFunctional validation

Anti-sense RNAKnockouts

Cancer Differential Expression Data

Tissue cDNA librariesGene Expression Patterns

ExternalPublic

Databases

Bioinformatics Architecture

ExternalProprietaryDatabases

Unix servers

&SpecializedHardware

UsersWorkstation

Java & DesktopPrograms

WebBrowser

ActiveServer

Livewire CGINT

servers

ProprietaryInternal

Databases

WebServer

SharedAccess

Databases

MSAccess

Challenges in High Throughput Biotechnology R&D Volume of Data is Growing Rapidly Technology is Evolving Rapidly

Instrumentation, Informatics

Biological Definitions are Constantly Updated New Interactions and Functions Discovered Daily

New Genes Sometimes Homologous to Known Genes ->re-evaluate old data

Full Value is Realized by Integrating Multiple High-Throughput Platforms Sequencing, Functional Screens, Small Molecule Activity

Up-Front Design of Data and Quality Control Databases is Crucial to Success High Data Quality is Essential Financially Impossible to Repeat Experiments Requires Informatics Specialist who Understands Laboratory Techniques

Recent Informatics Job DescriptionDUTIES: The role of this position is to provide scientific bioinformatics support for gene

discovery research utilizing DNA microarray technology at Chiron. The successful candidate will participate in research projects within a bioinformatics team that will provide analysis and data management resources necessary to optimize research activities. REQUIREMENTS: A BS/MS in molecular biology, biochemistry and/or a field related to bioinformatics. A minimum of 1 year of biotechnology research experience working with projects and scientists in the field and 1 year experience utilizing data analysis tools in biotechnology research, specifically in the field of microarrays. Proficiency with bioinformatics programs and algorithms including both unix and desktop systems. Proficiency in data analysis programs such as Excel and statistical analysis packages used in the analysis of microarray data. Proficiency in SQL and working with relational database systems. Strong communication and teaching skills for working with colleagues and project researchers.

Scientist II, Research Microarrays

DUTIES; Develop and apply data analysis methods for interpreting high-throughput microarray experiments. Disseminate research results in presentations and writing. Should be flexible and work well in a team environment.

REQUIREMENTS: Ph.D. in Physical Sciences, Computing Sciences or Statistics. Previous experience analyzing large data sets, modeling laboratory experiments, developing quantitative assessments of data reliability, and associated computer programming tasks.

Title: Information Specialist

30

Slide 30

Funding UC-Industryresearch collaborations in

•bioinformatics•food and agricultural informatics

•environmental informatics•medical informatics

•computational aspects of imaging & modeling

LSI Research ProposalsJanuary 26, 2001

May 22, 2001October 2, 2001

Opportunity AwardsYear round

Learn more about it…http://lsi.ucdavis.edu

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“The two technologies that will shape the next century are biotechnology and information technology”

Bill Gates

“The two technologies that will have the greatest impact on each other in the new millennium are biotechnology and information technology”

Martina McGloughlin

“The two technologies that will shape the next century are biotechnology and information technology”

Bill Gates

“The two technologies that will have the greatest impact on each other in the new millennium are biotechnology and information technology”

Martina McGloughlin

32

Technology Division

•Informatics•Technology Division

Pharmaceutical Division

•Small Molecule Drugs

MillenniumBioTherapeutics, Inc(Mbio)• Proteins• Antibodies• Vaccines• Gene Therapy• Antisense

Cereon Genomics, LLC(Monsanto Subsidary)• Plant Genomics

MillenniumPredictive Medicine, Inc(MPM)• Diagnostics• Pharmacogenomics• Patient Mangagement

Millennium’s Genomics Strategies

Millennium Pharmaceuticals, Inc., the parent company, consists of a technology division and a pharmaceutical division. The technology division has two tasks: 1) developing and acquiring technologies, and 2) moving those technologies into production mode. The company’s philosophy is to industrialize discovery and development, moving as many discovery and development operations as possible into a production mode.

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34

Expression Technologies

There are currently four commonly used approaches to high throughput, comprehensive analysis of relative transcript expression levels. The enumeration of expressed sequence tags (ESTs), Serial Analysis of Gene Expression (SAGE), Differential Display Approaches, Array-based hybridization

The enumeration of expressed sequence tags (ESTs) from

representative cDNA libraries. A method of approximating the relative representation of the gene transcript within the starting cell population.

GeneTrace Systems, HHMI, IMAGE Consortium, Incyte, The Institute for Genomic Research

Serial Analysis of Gene Expression.The enumeration of serially concatenated 9-11 base tags from specially prepared cDNA libraries. The frequency of particular transcripts within the starting cell population is reflected by the number of times the associated sequence tag is encountered within the sequence pop

Genzyme Molecular Oncology, Johns Hopkins University

Expression Technologies

There are currently four commonly used approaches to high throughput, comprehensive analysis of relative transcript expression levels. The enumeration of expressed sequence tags (ESTs), Serial Analysis of Gene Expression (SAGE), Differential Display Approaches, Array-based hybridization

The enumeration of expressed sequence tags (ESTs) from

representative cDNA libraries. A method of approximating the relative representation of the gene transcript within the starting cell population.

GeneTrace Systems, HHMI, IMAGE Consortium, Incyte, The Institute for Genomic Research

Serial Analysis of Gene Expression.The enumeration of serially concatenated 9-11 base tags from specially prepared cDNA libraries. The frequency of particular transcripts within the starting cell population is reflected by the number of times the associated sequence tag is encountered within the sequence pop

Genzyme Molecular Oncology, Johns Hopkins University

35

Expression Technologies Differential Display Approaches Fragments defined by specific

sequence delimiters can be used as unique identifiers of genes, when coupled with information about fragment length or fragment location within the expressed gene. The relative representation of an expressed gene within a cell can then be estimated based on the relative representation of the fragment associated with that gene within the pool of all possible fragments. A number of different approaches have been developed to exploit this hypothesis for comprehensive expression analysis.

Curagen Corporation - Quantitative Expression Analysis (QEA)

Digital Gene Technologies, Inc. - Total Gene expression Analysis (TOGA)

Display Systems Biotech - Restriction Fragment Differential Display-PCR (RFDD-PCR)

Genaissance GeneLogic - Restriction Enzyme Analysis of Differentially-

expressed Sequences (READS)

Expression Technologies Differential Display Approaches Fragments defined by specific

sequence delimiters can be used as unique identifiers of genes, when coupled with information about fragment length or fragment location within the expressed gene. The relative representation of an expressed gene within a cell can then be estimated based on the relative representation of the fragment associated with that gene within the pool of all possible fragments. A number of different approaches have been developed to exploit this hypothesis for comprehensive expression analysis.

Curagen Corporation - Quantitative Expression Analysis (QEA)

Digital Gene Technologies, Inc. - Total Gene expression Analysis (TOGA)

Display Systems Biotech - Restriction Fragment Differential Display-PCR (RFDD-PCR)

Genaissance GeneLogic - Restriction Enzyme Analysis of Differentially-

expressed Sequences (READS)

36

Expression Technologies

Array-based hybridizationBased on the exquisite specificity of nucleotide interactions oligonucleotides or cDNA can be used to selectively identify or capture DNA or RNA of specific sequence composition. The primary approaches include array- based technologies that can identify specific expressed gene products on high density formats, including filters, microscope slides, or microchips, and solution-based technologies relying on spectroscopic analyses, such as mass spectrometry.

Affymetrix, Axon Instruments, Inc, BioDiscovery Inc. BioRobotics, Cartesian Technologies, Clontech General Scanning Inc., GeneMachines, Genetic MicroSystems Inc. ,GeneTrace Systems, Genome Systems, Genometrix, Genomic Solution, Hyseq, Inc. Hyseq/ Applied Biosystems Division of Perkin Elmer Incyte, Intelligent Automation Systems/Intelligent Bio-Instruments, Molecular Dynamics, NHGRI Laboratory of Cancer Genetics, NEN Life Science Products Protogene, Radius BioSciences, Research Genetics, Inc. Stanford University, Dr. Pat Brown, Synteni, TeleChem International, Rosetta Inpharmatics (LeeHood)

37

Expression Technologies- Proteomics Most processes manifest themselves at the level of protein

activity, but until recently, high throughput analysis of proteins was not possible. Several technologies now makes it feasible to perform mass screening of proteins

2- D Gel Electrophoresis- LifeProt Protein Expression Database provides a bioinformatics platform for investigating 2D gel images sequence data/annotation. (Incyte, Oxford Bioscience)Immobiline, ImageMaster software Amersham/ Pharmacia/Molecular Dynamics, BioRad

LC-MS/MS, MALDI-TOF mass spectrometer offers fast and reliable protein identification for high throughput proteomic studies. MD, Perkin Elmer

PerSeptive Biosystems, PE Biosystems venture, integrates robotics, mass spec, data searching technologies into 1 system for HT ID proteins, peptides

SELDI (Surface-Enhanced Laser Desorption/ Ionization) ProteinChip technology rapid separation, detection and analysis of proteins at the femtomole level directly from biological samples- Ciphergen

Variants on yeast two-hybrid system, which is widely used for analyzing protein–protein interactions in vivo

Phage Display

Expression Technologies- Proteomics Most processes manifest themselves at the level of protein

activity, but until recently, high throughput analysis of proteins was not possible. Several technologies now makes it feasible to perform mass screening of proteins

2- D Gel Electrophoresis- LifeProt Protein Expression Database provides a bioinformatics platform for investigating 2D gel images sequence data/annotation. (Incyte, Oxford Bioscience)Immobiline, ImageMaster software Amersham/ Pharmacia/Molecular Dynamics, BioRad

LC-MS/MS, MALDI-TOF mass spectrometer offers fast and reliable protein identification for high throughput proteomic studies. MD, Perkin Elmer

PerSeptive Biosystems, PE Biosystems venture, integrates robotics, mass spec, data searching technologies into 1 system for HT ID proteins, peptides

SELDI (Surface-Enhanced Laser Desorption/ Ionization) ProteinChip technology rapid separation, detection and analysis of proteins at the femtomole level directly from biological samples- Ciphergen

Variants on yeast two-hybrid system, which is widely used for analyzing protein–protein interactions in vivo

Phage Display

38

Some of the 25 new genomics faculty will belong to the UC

Davis Genome Center, the first new product of the Genomics

Initiative. ($20m set aside for faculty) Designed to establish the campus as an international leader in

functional and comparative genomics, the center will include

scientists specializing in gene studies from a multitude of

disciplines, including human and animal medicine,

engineering, agriculture, mathematics and the biological and

physical sciences. The Genome Center will also include a revitalized

pharmacology and toxicology department in the School of

Medicine and a group of bioinformatics faculty members who

will provide the computational biology and informatics

research needed to analyze the enormous amounts of data

generated by the genomics research.

Some of the 25 new genomics faculty will belong to the UC

Davis Genome Center, the first new product of the Genomics

Initiative. ($20m set aside for faculty) Designed to establish the campus as an international leader in

functional and comparative genomics, the center will include

scientists specializing in gene studies from a multitude of

disciplines, including human and animal medicine,

engineering, agriculture, mathematics and the biological and

physical sciences. The Genome Center will also include a revitalized

pharmacology and toxicology department in the School of

Medicine and a group of bioinformatics faculty members who

will provide the computational biology and informatics

research needed to analyze the enormous amounts of data

generated by the genomics research.

Genomics CenterGenomics Center

39

Proteomics Companies Location Business Approach Collaborators

Ciphergen Biosystems Inc. Palo Alto, CA Protein arrays N/A Genomic Solutions Inc. Ann Arbor, MI Automated 2-D gel/

MS platform N/A Hybrigenics SA Paris, France Protein-protein interaction Pasteur Institute

mapping and databases Small Molecule Therapeutics Inc.;

Large Scale Biology Corp. Rockville, MD Biological assay Biosource and Vacaville, CA Technologies Inc.

(parent) Oxford GlycoSciences plc Oxford, England Biological assay; Incyte Pharma

Protein databases Pfizer IncLumicyte CA Protein Arrays . Proteome Inc. Beverly, MA Protein databases N/A Proteome Systems Ltd. Sydney, Australia Biological assay; Dow AgroSciences

Protein databasesMyriad Genetics Inc. Salt Lake City Protein datbases

Utah Biological assays CuraGen Corp Biogen, Genentech

COR Therapeutics, Glaxo Wellcome, Roche, Pioneer Hi-Bred/ Dupont

40

AffymtrixAgilent Alpha Gene Alpha Innotech Amersham Pharmacia Biotech Axon Instruments Bio DiscoveryBio Roboti c sBiospace Mesure s Cartesian Technologies CellomicsCiphergenClinical Micro SensorsCLONTECHCuraGenDisplay Systems BiotechDouble TwistGeneData GeneFocusGeneMachinesGenetic Micro Systems GenometrixGenomic Solutions GSI Lumonics

Imaging Research

Iris BioTechnologies Incyte Pharmaceutical s LION AgLumicyteMicronicsNanogen NEN Life Science Pro d u c t s PHASE 1 Molecular Toxicology Phoretix International Proteome Protogene Laboratories R&D Systems Radius Biosciences Research Genetics ScanalyticslSigma - Genosys Silicon Genetics TeleChem InternationalUniversal Imaging V&P Scientific Virtek Vysis

Companies dependant on Informatics

41

IP Challenges June 5, 1995--Human Genome Sciences applies for a patent on a gene

that produces a "receptor" protein that is later called CCR5. HGS has no idea that CCR5 is an HIV receptor.

December 1995--U.S. researcher Robert Gallo, the co-discoverer of HIV, and colleagues find three chemicals that inhibit the AIDS virus. Don’t how the chemicals work.

February 1996--Edward Berger at the NIH discovers that Gallo's inhibitors work in late-stage AIDS by blocking a receptor on the surface of T-cells.

June 1996--In a period of just 10 days, five groups of scientists publish papers saying CCR5 is the receptor for virtually all strains of HIV.

January 2000--Schering-Plough researchers tell a San Francisco AIDS conference they have discovered new inhibitors. Merck researchers are known to have made similar discoveries.

Feb. 15, 2000--The U.S. Patent and Trademark Office grants HGS a patent on the gene that makes CCR5 and on techniques for producing CCR5 artificially. The decision sends HGS stock flying and dismays researchers.

HGS: identified in whole or in part 95% of the 100,000 or so human genes. 100 human gene patents 7,500 pending.

42

The Shape of the Wave

– 1999» JGI releases 150 Mbases draft » Celera releases the sequence of Drosophila (140 Mb)» Public “draft” effort reaches halfway point (1,500 Mb)» 20 more Microbial genomes completed (80 Mb but 60,000 genes)» First release of Celera “shotgun” (9,000 Mb)

– 2000» Public “draft” completed (1,500 Mb)» Mouse “draft” begins (500 Mb - comparisons with human)» Two more Celera shotgun releases ( 18,000 Mb)» 40 more Microbial genomes sequenced (160 Mb -120,000 genes)

43

Projected Base Pairs

110

1001,000

10,000100,000

1,000,00010,000,000

100,000,0001,000,000,000

10,000,000,000100,000,000,000

1,000,000,000,00010,000,000,000,000

100,000,000,000,000

90 95 0 5 10 15 20 25

Year

500,00050,0005,000

Projected size of the sequence database, indicated as the number of base pairs per individual medical record in the US.The amount of digital data

necessary to store 1014 bases of DNA is only a fraction of the data necessary to describe the world’s microbial biodiversity at one square meter resolution...

The amount of digital data necessary to store 1014 bases of DNA is only a fraction of the data necessary to describe the world’s microbial biodiversity at one square meter resolution...

44

How much information is there in the World?

• Library of Congress: – 3 Petabytes (3,000 TB)

» 6 billion book pages (1 PB)

» 13 million photographs (13TB)

» maps, movies, audio tapes

• Cinema

• Images– 520 Petabytes (520,000TB)

» 52 billion photographs / year / 10KB

• Broadcasting

• Sound

• Telephony

• Library of Congress: – 3 Petabytes (3,000 TB)

» 6 billion book pages (1 PB)

» 13 million photographs (13TB)

» maps, movies, audio tapes

• Cinema

• Images– 520 Petabytes (520,000TB)

» 52 billion photographs / year / 10KB

• Broadcasting

• Sound

• Telephony

Michael Lesk, Bellcore 1997

45

Business ComparisonsCompany Revenues IT Budget Pct

Bristol-Myers Squibb 15,065,000,000 440,000,000 2.92 %

Pfizer 11,306,000,000 300,000,000 2.65 %

Pacific Gas & Electric 10,000,000,000 250,000,000 2.50 %

K-Mart 31,437,000,000 130,000,000 0.41 %

Wal-Mart 104,859,000,000 550,000,000 0.52 %

Sprint 14,235,000,000 873,000,000 6.13 %

MCI 18,500,000,000 1,000,000,000 5.41 %

United Parcel 22,400,000,000 1,000,000,000 4.46 %

AMR Corporation 17,753,000,000 1,368,000,000 7.71 %

IBM 75,947,000,000 4,400,000,000 5.79 %

Microsoft 11,360,000,000 510,000,000 4.49 %

Chase-Manhattan 16,431,000,000 1,800,000,000 10.95 %

Nation’s Bank 17,509,000,000 1,130,000,000 6.45 %