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Genomics

Genomics. Advances in 1990 ’ s Gene Expressed sequence tag (EST) Sequence database Information Public accessible Browser-based, user-friendly bioinformatics tools Oligonucleotide microarray (DNA chip) PCR Hybridization of oligonucleotides to complementary sequences. Proteomics.

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Genomics

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  1. Genomics • Advances in 1990’s • Gene • Expressed sequence tag (EST) • Sequence database • Information • Public accessible • Browser-based, user-friendly bioinformatics tools • Oligonucleotide microarray (DNA chip) • PCR • Hybridization of oligonucleotides to complementary sequences

  2. Proteomics • An analytical challenge !! • One genome  many proteomes • Stability of mRNA • Posttranslational modification • Turnover rate • Regulation • No protein equivalent PCR • Protein does not replicate • Proteins do not hybridize to complementary a.a. sequence • Ab-Ag

  3. Questions to ask • What it is ? • Molecular function • Knockout • Why is this being done ? • Biological process • Y2H • Where is this ? • Cellular compartment • Immunofluorescence • What it is ? • Molecular function • Knockout • Why is this being done ? • Biological process • Y2H • Where is this ? • Cellular compartment • Immunofluorescence

  4. New high-throughput strategies • What it is ? • Random transposon tagging (yeast) • Michael Snyder at Yale • Bar code (yeast) • Ron Davis at Standford • RNAi (C. elegans)

  5. Transposon • Mobile pieces of DNA that can hop from one location in the genome to another. • Jumping gene • Tn3 in Saccharomyces cerevisiae • A modified minitransposon (mTn3) • Review: life cycle of yeast

  6. mTn3 • Fig 6.1 • Why URA3 gene in mTn3? • Why a lacZ without a promoter and start codon ? • Why lox P ? • Significance of homologous recombination ? Ross-Macdonald et al., 1999 Nature 402, 413

  7. The mTn insertion project (1) • To create mutations: • A yeast genomic plasmid library in E. coli was randomly mutagenized by mTn insertion

  8. The mTn insertion project (2) • Isolate mutated plasmids • Cut with Not1 • Transform yeast • Homologous recombination • Replace mTn-mutated gene with wt gene • In URA3-lacking strain • Results: • 11,232 strains turned blue

  9. mTn approach to yeast genome • 11,232 strains turned blue • 6,358 strains sequenced • 1,917 different annoted ORFs • 328 nonannoted ORFs • “gene” = ORFs > 100 codons • What’s next ?

  10. Phenotype macroarrays • 96 strains x 6 = 576 strains

  11. Array analysis • Metabolic pathway • Oxidative phosphorylation => red • Alkaline phosphatase + BCIP => blue

  12. 1,2,3,4,…….……,8,9,10 Now what ? • 576 strains, 20 growth conditions • Data, data, data…. • Look for extremes ? • Cluster • Group together similar patterns • Mathematical description • Co-expression • Standard correlation coefficient • Graphical representation • Original experimental observation • Color, dark and light • Visualize and understand the relationships intuitively

  13. http://bioinfo.mbb.yale.edu/genome/phenotypes/ Data analysis Growth conditions • Transformants clustering • Graphical representation Transformants

  14. Data clusters • 20 Growth conditions

  15. Double cluster • Horizontal cluster: transformants • Vertical cluster: growth conditions • Identify assays for functionally related proteins

  16. Cell wall biogenesis and maintenance DNA metabolism Growth condition cluster • Clustering growth conditions that result in similar phenotypes • More effective screening functionally related proteins

  17. Discovery Questions • What advantage is there to clustering the phenotypes in this manner? • Some of the genes identified in this analysis had no known function. How can clustering these data help us predict possible functions?

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