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Chapt “7:” Recombinant DNA and Genomics. Goals: What is recombinant DNA technology? What is genomics? ……. a nutshell, sort of. Structure of nucleotides. Base, nucleoside, nucleotide, D/RNA (polymer). Bases- diversity of life thru permutations of FOUR bases. RNA: U, C, G, A ---NTP
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Chapt “7:” Recombinant DNA and Genomics Goals: • What is recombinant DNA technology? • What is genomics? ……. a nutshell, sort of
Structure of nucleotides • Base, nucleoside, nucleotide, D/RNA (polymer)
Bases- diversity of life thru permutations of FOUR bases • RNA: U, C, G, A ---NTP • DNA: T, C, G, A ---dNTP
DNA: alpha helix, gouble strand complementary and antiparallel
DNA cloning vectors Natural biology-chemistry Bacterial processes Palindromes NNNACGTNN-> NNNA CGTNN NNNTGCANN NNNTGC ANN CGNNN NNNGC Enzymes Biotechnology
Biotechnology drives basic biology drives biotechnology….ABI series: 370, 373 and 377 • Non-radioactive. • Semi-automated and automate-able. • Large scale and high throughput operations- industrial strength! • Allows more questions. • Presents bioinformatics challenges or “opportunities.”
MAP alignment of variola and vaccinia consensus sequences
BLAST etc • Human neurofibromatosis NF-1 • Yeast Ira “GAP-type protein • both regulate Ras • Inappropriate cell division, tumor formation
Applications to biological systems • Immunology. • T-cell biology. • Molecular diagnostics. • Thyroid hormone resistance. • Pharmaceutical and Biotech. • Gene discovery and target validation. • HIV drug development. • Gene expression: Fluorescent differential display. • Comparative virology. • Adenoviruses. • Human adenoviruses. • Poxviruses.
1975 1980 1990 2000 Biology: Science in Transition Genetic Engineering DNA Sequencing Automated DNA Sequencing Human Genome Project H. Influenzae C. elegans • Biology is changing rapidly from a “data-poor” to a data-rich and information-rich science. • “New” Fields: • Computational Biology • Bioinformatics • Systems biology Human Genome PCR DNA Chips Growth of Genome Databases
Comparative genomics- Why? • Faster cheaper better technology and methodology. • Even more so for multiple similar additional genomes. • Scientific and funding interests- • Example: E. coli K12 vs O157:H7. • ca. 1Mb more DNA. • 1400 new genes. • 5 additional strains in progress. • Example: ca. 20 different Bacillus anthracis strains. • Example: Staphylococcus aureus- 5 strains. • Example: Chlamydia pneumoniae- 5 strains.
Conserved genes of poxviruses- need more tools Bacterial genomes: Need to build better “mouse trap.”
Genomics data mining: “BreakThrough” strains • Ad “BreakThrough 4/5” strain. • Isolated from a vaccinated recruit. • Serotyped as Ad4 by microneutralization. • Genome determination and analysis suggest it is Ad5. • BLAST; genome alignments; hexon and fiber trees. • Re-serotyped as Ad5. • BT4/5 hexon sequence is identical to Ad5.
Bioinformatics Mycoplasma genitalium ORFs
devGeneOrder3.0: Application to small bacterial genomes, ca. 2002 Mycoplasmasizedatepredicted coding regions M. genitalium 580kb 01/8/01 470 M. pneumoniae 816kb 04/2/01 688 M. pulmonis 964kb 10/2/01 782* • *Functions assigned to 486; 92 match hypothetical proteins; 204** without significant matches. • Gene annotation. • **204 potential targets for pharma and diagnostics. • How many unique targets within Mycoplasma group?
Microarray expt: yeast mRNAs • Cell growth ~ C source