Research In the Post-Genomics Era

1. Research • In the Post-Genomics • Era Martina McGloughlin, Biotechnology Program and Life Sciences Informatics Program UC Davis

2. UC Davis Biotechnology Program UC Systemwide Life Sciences Informatics Program “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

3. Genomics • 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.

4. One Human Sequence 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.

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

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

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

8. 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...

9. DNA GenBank EMBL DDBJ Map Databases RNA Development ? Gene Expression? Proteins SwissPROT PIR PDB Circuits Metabolism? Regulatory Pathways? Phenotypes Neuroanatomy? Clinical Data ? Populations Biodiversity? Molecular Epidemiology? Comparative Genomics? Fundamental Dogma Although a few databases already exist to distribute molecular information, the post-genomic era will need many more to collect, manage, and publish the coming flood of new findings. If this extension covers functional genomics, then “functional genomics” is equivalent to biology.

10. 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 There are Problems with the HGP . ..

11. USERS of Information of Tools of Instrumentation In-Silico Modeling INTERPRETERS of Information Bioinformatics - Two Views DEVELOPERS* • of Information • of Tools • of Instrumentation • of Architecture/Storage • Algorithms • Modeling Strategies • Visualization * *These people are in highest demand Per Pete Smietana, VP Lumicyte

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

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

14. Bioinformatics Functional Organization Systems Development Program development System integration Database design Skillset Systems analysis Database development Programming Infrastructure Support Computer operations Database Admin Skillset Computer Network Database Gene Discovery Genomics Sequencing Molecular Biology High Throughput Screening Applications Support Help Desk Training Skillset Program knowledge Communication Teaching Research Bioinformatics research Algorithm develompment New Technologies Skillset Computational Biology Bioinformatics Programming Database Support Administration Curation Skillset Molecular Biology Computer Communications Research Support Scientific support Gene discovery Data Smelting Skillset Molecular Biology Computer Communications

15. TGT AAT AGT TAT ATT TTC ATT ATA AAT TGT GTT TGT AGA CAT CAT AAA TTT AAA ACA TGG CTT TTT AAC CTG ATA AAT CCT ACG AAT ATT TGT AAT AGT TAT GTT ATT GCA GTA AGT ACC GTT TGT ATT ATA AAT TGT GTT CTG TGT AAT AGT TAT ATT TTC ATT ATA AAT TGT GTT TGT AGA CAT CAT AAA TTT AAA ACA TGG CTT TTT AAC CTG ATA AAT CCT ACG AAT ATT TGT AAT AGT TAT GTT ATT GCA 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. 87 88 89 90 91 92 93 94 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. GenBank Release Numbers 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

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

19. Examples of Biotech/IT Fusion Technologies • Genomics, proteomics and bioinformatics • Combinatorial –chemistry • Peptide libraries- tea bags, beads • Combinatorial -biology • Directed evolution • DNA Shuffling, Molecular Breeding • High throughput analysis • Nucleic Acid based Sequencing Microarrays • Photolithography • Mirrors • Spotted Chips • Semi-conductor • Protein based 2-D, electrospray/nanospray MS: MALDI-TOF, LC/MS/MS, SELDI • Imaging/optical biology • Biosensors, Bioelectronics and Bionetworks (Nanotechnology)

20. 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

22. Medical Bioinformatics: What is it? Biological Interpretation Laboratory/Clinical Experiments • Published Literature • Scientific/Medical Experts • Hi-throughput Screening Data • genotype sequencing • functional assays • DNA library • Patient Clinical Data • cancer phenotype • outcomes, treatments, age • Patient Samples • Tissues • Tumors • Model Systems • Rats • cultured tissues Informatics • Sample/Experiment Tracking • Data Processing, Quality Control • Statistical Analyses • Sequence Matching/Annotation • Functional Significance • User Access to Results

23. What’s in a name? GenomeMapping ProteinAnalysisProteomics MultipleSequenceAlignment DatabaseHomologySearching 3DModeling BioInformatics HomologyModelingDocking SequenceAnalysis SampleRegistration &Tracking IntellectualPropertyAuditing IntegratedDataRepositories CommonVisualInterfaces

24. Tissues & Cell Lines Small Molecule Drugs Microdissection In situHybridization Small Molecule Database Create DNA Libraries DNA Libraries Database Micro Arrays Micro Array Database Clones Database Signature Hybridization Functional Predictions Assays & Validation Database Annotated Sequence Database Clustering by Signature Gene Assignments Expression Profiles DNASequencing Differential Expression Gene Discovery Informatics Functional Assays Tissue & Cell Lines Database In SituHybridiz-ation Clustering Database

25. IM Intensive Research Groups ComputationalBiology ModelingStructure ChemicalInformaticsChemistrySMDD Bioinformatics GenomicsGene ExpressionTarget Discovery InformationManagementInfrastructureSupport PharmacologyScreening Assay AnalysisAnimal Data Robotics Sample Management DesktopResearch Experimental Data

26. User's Web Browser Cancer Gene Discovery Knowledgebase Cancer Differential Expression Data Tissue cDNA libraries Gene Expression Patterns Functional/Validation Microarrays In situ HybridizationFunctional validationAnti-sense RNAKnockouts Cancer TissueInventory Patient DataPathologyTissue Info DNA Sequence ProprietaryRelational DatabaseHomology searchesFunctional Profiles

27. External Public Databases External Proprietary Databases Bioinformatics Architecture Java & Desktop Programs Web Browser Users Workstation MS Access SharedAccessDatabases Web Server NT servers ActiveServer Livewire CGI Unix servers & Specialized Hardware Proprietary Internal Databases

28. 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 Challenges in High Throughput Biotechnology R&D

29. Recent Informatics Job Description Title: Information Specialist DUTIES: 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.

30. Funding UC-Industry • research collaborations in • bioinformatics • food and agricultural informatics • environmental informatics • medical informatics • computational aspects of imaging & modeling Slide 30 LSI Research Proposals January 26, 2001 May 22, 2001 October 2, 2001 Opportunity Awards Year round Learn more about it… http://lsi.ucdavis.edu

31. “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. Millennium’s Genomics Strategies • Technology Division • Informatics • Technology Division • Pharmaceutical Division • Small Molecule Drugs • MillenniumPredictive • Medicine, Inc • (MPM) • Diagnostics • Pharmacogenomics • Patient Mangagement • Millennium • BioTherapeutics, Inc • (Mbio) • Proteins • Antibodies • Vaccines • Gene Therapy • Antisense • Cereon Genomics, LLC • (Monsanto Subsidary) • Plant Genomics 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.

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

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)

36. Expression Technologies • Array-based hybridization Based 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-TOFmass 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 onyeast two-hybrid system, which is widely used for analyzing protein–protein interactions in vivo • Phage Display

38. Genomics Center • 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.

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 Inc Lumicyte CA Protein Arrays . Proteome Inc. Beverly, MA Protein databases N/A Proteome Systems Ltd. Sydney, Australia Biological assay; Dow AgroSciences Protein databases Myriad Genetics Inc. Salt Lake City Protein datbases Utah Biological assays CuraGen Corp Biogen, Genentech COR Therapeutics, Glaxo Wellcome, Roche, Pioneer Hi- Bred/ Dupont

40. Affymtrix Agilent Alpha Gene Alpha Innotech Amersham Pharmacia Biotech Axon Instruments Bio Discovery Bio Roboti c s Biospace Mesure s Cartesian Technologies Cellomics Ciphergen Clinical Micro Sensors CLONTECH CuraGen Display Systems Biotech Double Twist GeneData GeneFocus GeneMachines Genetic Micro Systems Genometrix Genomic Solutions GSI Lumonics Imaging Research Iris BioTechnologies Incyte Pharmaceutical s LION Ag Lumicyte Micronics Nanogen 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 Scanalyticsl Sigma - Genosys Silicon Genetics TeleChem International Universal 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. 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) The Shape of the Wave

43. 500,000 50,000 5,000 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... Year Projected Base Pairs

44. How much information is there in the World? Michael Lesk, Bellcore 1997 • 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

45. Company Revenues IT Budget Pct Chase-Manhattan 16,431,000,000 1,800,000,000 10.95 % AMR Corporation 17,753,000,000 1,368,000,000 7.71 % Nation’s Bank 17,509,000,000 1,130,000,000 6.45 % Sprint 14,235,000,000 873,000,000 6.13 % IBM 75,947,000,000 4,400,000,000 5.79 % MCI 18,500,000,000 1,000,000,000 5.41 % Microsoft 11,360,000,000 510,000,000 4.49 % United Parcel 22,400,000,000 1,000,000,000 4.46 % 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 % Wal-Mart 104,859,000,000 550,000,000 0.52 % K-Mart 31,437,000,000 130,000,000 0.41 % Business Comparisons