bioinformatics a biologist’s perspective rob rutherford
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Bioinformatics A Biologist’s perspective Rob Rutherford. 1. The Biologist’s perspective 2. A survey of tools 3. Training students for the future. - PowerPoint PPT PresentationTRANSCRIPT
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Bioinformatics
A Biologist’s perspective
Rob Rutherford
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1. The Biologist’s perspective
2. A survey of tools
3. Training students for the future
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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)
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Figure 1.18 Careful observation and measurement provide the raw data for science
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PubMed had 400,000 new research articles entered in 2002.
NCBI-NLM, 2003
Productive Tinkerers
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NIH-NLM 2003
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NIH-NLM 2003
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(Cockerill 2003))
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“If your experiment needs statistics, you ought to have done a better experiment.”
-Rutherford (the other one)
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RA Fisher 1956, University of Adeliade Archives
“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.”
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2 Wings of Bioinformatics
Housekeeping BioinformaticsRepresentation, storage, and distribution of data
Analytical BioinformaticsNew tools for the discovery of knowledge in data
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Part 2
A Survey of Problems/Opportunities
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“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
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A Survey of Problems
Finding Genes and Understanding GenesProtein Structure and FunctionGene ExpressionNetworks
Other areas
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Finding and Understanding Genes
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Receptors-GPCR (767)
Receptors-NHR (56)
Integrins (33)
Ion Channels (313)
Kinases (713)
Phosphatases (274)
Phosphodiesterases (58)
Neurotrans. transporters (34)
P450s (59)
Proteases (527)
Secreted (3621)
Other (53076)
Estimated Gene Number~(59538)Human Genes
Rutherford
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10 20 30 40 ....*....|....*....|....*....|....*....| consen 1 SPKNTPVVLIPKKGPGKYRPISlvDYKILNKATKKrFSpp 40 1MML 83 SPWNTPLLPVKKPGTNDYRPVQ--DLREVNKRVED-IH-- 117 1HNI_B 54 NPYNTPVFAIKKKDSTKWRKLV--DFRELNKRTQD-FWev 90 1MU2_B 49 NPYNTPTFAIKKKDKNKWRMLI--DFRELNKVTQD-FTei 85 1D1U_A 69 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
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Finding Conserved Regions/Domains
HIV protein
Comparing your sequence versus models derived from curated known protein families
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Thanks to Porterfield
Phylogenetics and Evolution
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Protein Structure
Imaging Experimental X-ray diffraction data
Predicting structure in silico from sequence
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Experimental structures in the Protein Data Bank
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HIV reverse tanscriptase
Goodsell, PDB
DNA (human genome)
RNA (HIV virus)
Protein
Structure is Function
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Goodsell, PDB
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Figure 17.0 Ribosome
Structural Predictions just from raw protein sequence?
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Figure 17.0 Ribosome 1 ggcacgaggc acggctgtgc aggcacgcat gcaggccagc ….
1 atctgcacgt ggttatgctg ccggagtttg ggccgccact….
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CASPCommunity Wide Assessment of techniques
for Protein Structure Prediction
Every two years, contest to test protein structure prediction from primary sequence
An example:
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Gene Expression
Sequencing RNA (ESTs)Sequencing bits of ESTs (SAGE)
Automation of In situDNA microarray technology
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One spot for each gene
MicroArray
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Microarray Expression Analysis
Reference Mixture Specific Organ
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H2O2 SDS Diamide Iron NO NOSigE SigH IdeR NrpR Experimental Conditions
400
0 G
enes
Gene turned onGene turned off
Low O2
DormancyGenes
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Figure 1.3 Some properties of life
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Figure 1.23x1 Biotechnology laboratory
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Metabolic Pathway Map
Building Transcriptional Network Map
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Networks
Biochemical PathwaysSignaling Networks
Transcriptional Networks Computational Neuroscience
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Scientific American 2001
Microarrays uncover networks of interactions…
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Other Opportunities
Organismal Physiology Populations
Communities Ecosystems
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Same issues in “Macro” Biology
Long history of mathematical modeling
Huge datasets from •GPS/GIS•Remote sensing
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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)
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Where is all this leading to?
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Part 3How do we prepare our students for
this future?
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Dr. Peter Munson
Head of the Mathematical and Statistical Computing Laboratory Division of Computational Biosciences National Institutes of Health
Ole’ pre 1976
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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
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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(!)
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