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Bioinformatics

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Introduction to Bioinformatics

Introduction to Bioinformatics

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  • Transcript

    • 1. Bioinformatics
      • Bioengineering Summer Camp
      • June 2001
      • Yaron Turpaz, Andrew Binkowski, Jie Liang
    • 2.
      • Introduction
        • Basic molecular biology.
      • What is Bioinformatics
      • UIC’s Bioinformatics group
        • Research projects
        • Summer camp research project.
      Outline http://gila.engr.uic.edu/bioinformatics/
    • 3. DNA Protein Nucleotides sequence Gene expression = Protein production
    • 4. Exons & Introns STOP F R L
    • 5. Biological Diversity Bacteria Fruit Fly Human Yeast
    • 6. The Genetic Code
      • From DNA sequence to Protein sequence.
      • 20 Building blocks of a protein = 20 Amino acids.
    • 7. From protein sequence to Structure
      • APRKFFVGGNWKMNGDKKSLGELIHTLNGAKLSADTEVVCGAPSIYLDFARQKLDAKIGVAAQNCYKVPKGAFTGEISPAMIKDIGAAWVILGHSERRHVFGESDELIGQKVAHALAEGLGVIACIGEKLDEREAGITEKVVFEQTKAIADNVKDWSKVVLAYEPVWAIGTGKTATPQQAQEVHEKLRGWLKSHVSDAVAQSTRIIYGGSVTGGNCKELASQHDVDGFLVGGASLKPEFVDIINAKH
      =
    • 8.
      • Computational analysis of high-throughput biological data
        • Whole genome sequencing.
        • Global genomic expression & profiling.
        • Functional genomics.
        • Structural genomics/proteomics
        • Comparative genomics.
      Bioinformatics http://gila.engr.uic.edu/bioinformatics/
    • 9.
      • Interdisciplinary approach
        • Computer science, Mathematics & Statistics.
        • Molecular biology, Biochemistry & Medicine.
      • Rapid growing impact area of BioE:
        • Boston U, UCBerkeley, UCSD, Rice, WashU, MIT,..
      Bioinformatics in BioE http://gila.engr.uic.edu/bioinformatics/
    • 10.
      • Structural genomics/proteomics
        • Structural basis of functional motifs in protein families.
        • The CAST server - http://cast.engr.uic.edu/
        • Drug discovery.
      • Functional genomics
        • Collaboration with TIGR - http://www.tigr.org/
          • Data mining of microbial DNA sequences for detection of foreign DNA.
          • Whole genome comparative studies.
      • Gene expression analysis
        • Collaboration with cancer biologist (Dr. Westbrook, School of Medicine)‏
          • Molecular based informatics method to facilitate diagnosis of cancer.
      Research
    • 11. Gene expression Computational analysis of cDNA microarray expression profiles ~26,000 genes in one experiment
    • 12. Functional correlation in clusters 1. Cell division 2. cell signaling/cell communication 3. cell structure/motility 4. cell/organism defense 5. gene/protein expression 6. metabolism 7. unclassified
      • Genes with similar expression patterns may participate in the same pathway or may be co-regulated.
      • Clustering of expression patterns may reveal such relationships.
    • 13. How Proteins Interfaces with Other Molecules
      • Analysis of protein topographic surfaces:
        • Identify protein function.
        • Predicting binding specificity and affinity.
        • Discovery of functional similarity.
      • Protein interaction with cosolvents
        • Stabilization of protein solution for longer shelf life.
        • Molecular mechanism and optimization.
      Research – structure & function
    • 14.
      • Atlas of Topographic Surfaces of All Known Protein Structures
        • Automatic identification of binding pockets.
        • Measurement size of surface binding pockets.
      • Drug Discovery
        • Quantifying ligand accessibility.
        • Constructing precise negative imprint or cast of binding site.
      Structural genomics/proteomics
    • 15. Voronoi Diagram and Delaunay Triangulation Geometry based approach for functional motifs Discrete Flow
    • 16. 3D alpha shapes (HIV-1 protease)‏
    • 17. What is an algorithm?
      • Precisely defined procedure for accomplishing a task.
        • driving directions,
        • furniture assembly instructions,
        • computer programs.
          • Built in hardware: fast
          • Built in software
    • 18. Are computers fast enough?
      • NP-complete problems:
          • eg. Traveling salesman problem :
          • 20 cities a few seconds
          • 30 cities a few hours
          • 60 cities a few decades
      • Computer speed does not increase exponentially.
    • 19. Pockets in Ribonuclease A
    • 20. A Server for Identification of Protein Pockets & Cavities CASTp http://cast.engr.uic.edu/ Users of CASTp: Columbia, Harvard, Mayo Clinic, Princeton, Stanford, U Penn, SUNY Stony Brook, Texas A&M, UCIrvine, UBC, Virginia Tech, Yale, Abbott Lab, Pfizer, SmithKline Beecham, ... Agouron, Emisphere, Vertex, ... Kyoto U, Cambridge, European Molecular Biology Lab, INRA (France), Pasteur-Lille, Uppsala, Weizman,... Brazil, Czech, Korea, Turkey, ...
    • 21. CASTp Results
      • Calculations:
      • Identifies all pockets and cavities. 
      • Measures the volume and area analytically. 
      • The number, area, and circumcircles of the mouth openings for each pocket. 
      • Files via email:
      • pocket and mouth information file,
      • Pocket and mouth atoms,
      • a script file for visualization using rasmol.  
      http://cast.engr.uic.edu/
    • 22. THANK YOU