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Bioinformatics A Biologist’s perspective Rob Rutherford

Bioinformatics A Biologist’s perspective Rob Rutherford. 1. The Biologist’s perspective 2. A survey of tools 3. Training students for the future.

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Bioinformatics A Biologist’s perspective Rob Rutherford

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  1. BioinformaticsA Biologist’s perspectiveRob Rutherford

  2. 1. The Biologist’s perspective2. A survey of tools3. Training students for the future

  3. If the biota, in the course of eons, has built something …..who but a fool would discard seemingly useless parts? To keep every cog and wheel is the first precaution of intelligent tinkering. -Aldo Leopold (1887 - 1948)

  4. Figure 1.18 Careful observation and measurement provide the raw data for science

  5. PubMed had 400,000 new research articles entered in 2002.NCBI-NLM, 2003 Productive Tinkerers

  6. NIH-NLM 2003

  7. NIH-NLM 2003

  8. (Cockerill 2003))

  9. “If your experiment needs statistics, you ought to have done a better experiment.”-Rutherford (the other one)

  10. “To consult the statistician after an experiment is finished is … to ask him to conduct a post mortem examination. He can perhaps say what the experiment died of.” RA Fisher 1956, University of Adeliade Archives

  11. 2 Wings of Bioinformatics Housekeeping Bioinformatics Representation, storage, and distribution of data Analytical Bioinformatics New tools for the discovery of knowledge in data

  12. Part 2A Survey of Problems/Opportunities

  13. “The Central Dogma” DNA Information Warehouse (4 nucleic acid letters atgc) RNA Temporary copy of a gene Protein Working Cellular Machine (20 amino acid letters) RNA polymerase PDB

  14. A Survey of Problems Finding Genes and Understanding Genes Protein Structure and Function Gene Expression Networks Other areas

  15. Finding and Understanding Genes

  16. Human Genes Rutherford

  17. 10 20 30 40 ....*....|....*....|....*....|....*....| consen 1 SPKNTPVVLIPKKGPGKYRPISlvDYKILNKATKKrFSpp40 1MML 83 SPWNTPLLPVKKPGTNDYRPVQ--DLREVNKRVED-IH--1171HNI_B54 NPYNTPVFAIKKKDSTKWRKLV--DFRELNKRTQD-FWev901MU2_B49 NPYNTPTFAIKKKDKNKWRMLI--DFRELNKVTQD-FTei851D1U_A69 SPWNTPLLPVKKPGTNDYRPVQ--DLREVNKRVED-IH–103 50 60 70 80 ....*....|....*....|....*....|....*....| Consen 41 qPGFRPGRSLLNKLKGS-KWFLKLDLKKAFDSIPHDPLLR 79 1MML 118 -PTVPNPYNLLSGLPPShQWYTVLDLKDAFFCLRLHPTSQ 156 1HNI_B 91 qLGIPHPAGL-----KKKKSVTVLDVGDAYFSVPLDEDFR 125 1MU2_B 86 qLGIPHPAGL—AKK -RRITVLDVGDAYFSIPLHEDFR 120 1D1U_A 104-PTVPNPYNLLSGLPPShQWYTVLDLKDAFFCLRLHPTSQ 142 CnD3 HIV

  18. Finding Conserved Regions/Domains HIV protein Comparing your sequence versus models derived from curated known protein families

  19. Phylogenetics and Evolution Thanks to Porterfield

  20. Protein Structure Imaging Experimental X-ray diffraction data Predicting structure in silico from sequence

  21. Experimental structures in the Protein Data Bank

  22. Structure is Function HIVreverse tanscriptase DNA (human genome) RNA (HIV virus) Protein Goodsell, PDB

  23. Goodsell, PDB

  24. Figure 17.0 Ribosome Structural Predictions just from raw protein sequence?

  25. 1 ggcacgaggc acggctgtgc aggcacgcat gcaggccagc …. Figure 17.0 Ribosome 1 atctgcacgt ggttatgctg ccggagtttg ggccgccact….

  26. An example: CASPCommunity Wide Assessment of techniques for Protein Structure Prediction Every two years, contest to test protein structure prediction from primary sequence

  27. Gene ExpressionSequencing RNA (ESTs)Sequencing bits of ESTs (SAGE)Automation of In situDNA microarray technology

  28. MicroArray One spot for each gene

  29. Microarray Expression Analysis Reference Mixture Specific Organ

  30. S i g E S i g H I d e R N r p R H 2 O 2 I r o n N O N O S D S D i a m i d e Low O2 Dormancy Genes Experimental Conditions 4000 Genes Gene turned on Gene turned off

  31. Figure 1.3 Some properties of life

  32. Figure 1.23x1 Biotechnology laboratory

  33. Metabolic Pathway Map Building Transcriptional Network Map

  34. NetworksBiochemical PathwaysSignaling NetworksTranscriptional Networks Computational Neuroscience

  35. Microarrays uncover networks of interactions… Scientific American 2001

  36. Other Opportunities Organismal Physiology Populations Communities Ecosystems

  37. Same issues in “Macro” Biology • Long history of mathematical • modeling • Huge datasets from • GPS/GIS • Remote sensing

  38. If the biota, in the course of eons, has built something …..who but a fool would discard seemingly useless parts? To keep every cog and wheel is the first precaution of intelligenttinkering. -Aldo Leopold (1887 - 1948)

  39. Where is all this leading to?

  40. Part 3How do we prepare our students for this future?

  41. Dr. Peter MunsonHead of the Mathematical and Statistical Computing Laboratory Division of Computational Biosciences National Institutes of Health Ole’ pre 1976

  42. The Tool Builders • Excellent mathematical skills (algorithms, linear algebra, data structures) • Be comfortable in a Linux/Unix environment, and know Perl and C/C++. • A deep background in 2+ advanced area of biology with chemistry prerequisites. • Graduate training

  43. The systems biologist. Biologist who is an intelligent and skeptical consumer of large data sets • Probability and Statistics • SQL and database basics • Equilibrium and rates of change (Calculus) • Exposure to system level data • And who knows how and when to collaborate(!)

  44. end

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