Masters course Bioinformatics Data Analysis and Tools

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Masters courseBioinformatics Data Analysis
and Tools

• Lecture 1 Introduction
• Centre for Integrative Bioinformatics
• FEW/FALW
• heringa_at_cs.vu.nl

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Course objectives

• There are two extremes in bioinformatics work
• Tool users (biologists) know how to press the
  buttons and the biology but have no clue what
  happens inside the program
• Tool shapers (informaticians) know the
  algorithms and how the tool works but have no
  clue about the biology
• Both extremes are dangerous, need a breed that
  can do both

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At the end of this course

• You will have seen a couple of algorithmic
  examples
• You will have got an overview about the methods
  used in the field
• You will have a firm basis of the physics and
  thermodynamics behind a lot of processes and
  methods
• You will have an idea of and some experience as
  to what it takes to shape a bioinformatics tool

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Bioinformatics
Studying informatic processes in biological
systems (Hogeweg)
Information technology applied to the management
and analysis of biological data (Attwood and
Parry-Smith)
Applying algorithms with mathematical formalisms
in biology (genomics) -- USA
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This course

• General theory of crucial algorithms (GA, NN,
  HMM, etc..)
• Method examples
• Research projects within own group
• Repeats
• Contact alignment
• Domain boundary prediction
• Physical basis of biological processes and tools

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Bioinformatics
Bioinformatics
Large - external (integrative) Science Human
Planetary Science Cultural Anthropology
Population Biology Sociology
Sociobiology Psychology Systems
Biology Biology Medicine
Molecular Biology
Chemistry Physics Small
internal (individual)
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Genomic Data Sources

• DNA/protein sequence
• Expression (microarray)
• Proteome (xray, NMR,
• mass spectrometry)
• Metabolome
• Physiome (spatial,
• temporal)

Integrative bioinformatics
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Protein structural data explosion
Protein Data Bank (PDB) 14500 Structures (6
March 2001) 10900 x-ray crystallography, 1810
NMR, 278 theoretical models, others...
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Algorithms in bioinformatics

• string algorithms
• dynamic programming
• machine learning (NN, k-NN, SVM, GA, ..)
• Markov chain models
• hidden Markov models
• Markov Chain Monte Carlo (MCMC) algorithms
• stochastic context free grammars
• EM algorithms
• Gibbs sampling
• clustering
• tree algorithms
• text analysis
• hybrid/combinatorial techniques and more

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Integrative bioinformatics _at_ VU

• Studying informational processes at biological
  system level
• From gene sequence to intercellular processes
• Computers necessary
• We have biology, statistics, computational
  intelligence (AI), HTC, ..
• VUMC microarray facility
• Enabling technology new glue to integrate
• New integrative algorithms
• Goals understanding cells in terms of genomes,
  fighting disease (VUMC)

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Bioinformatics _at_ VU

• Progression
• DNA gene prediction, predicting regulatory
  elements
• mRNA expression
• Proteins docking, domain prediction
• Metabolic pathways metabolic control
• Cell-cell communication

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Bioinformatics _at_ VU

• Qualitative challenges
• High quality alignments (alternative splicing)
• In-silico structural genomics
• In-silico functional genomics reliable
  annotation
• Protein-protein interactions.
• Metabolic pathways assign the edges in the
  networks
• Cell-cell communication find membrane associated
  components
• New algorithms

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Bioinformatics _at_ VU

• Quantitative challenges
• Understanding mRNA expression levels
• Understanding resulting protein activity
• Time dependencies
• Spatial constraints, compartmentalisation
• Are classical differential equation models
  adequate or do we need more individual modeling
  (e.g macromolecular crowding and activity at
  oligomolecular level)?
• Metabolic pathways calculate fluxes through time
  
• Cell-cell communication tissues, hormones,
  innervations

Need complete experimental data for good
biological model system to learn to integrate
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Bioinformatics _at_ VU

• VUMC
• Neuropeptide addiction
• Oncogenes disease patterns
• Reumatic diseases

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Bioinformatics _at_ VU

• Quantitative challenges
• How much protein produced from single gene?
• What time dependencies?
• What spatial constraints (compartmentalisation)?
• Metabolic pathways assign the edges in the
  networks
• Cell-cell communication find membrane associated
  components

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Integrative bioinformatics

• Integrate data sources
• Integrate methods
• Integrate data through method integration
  (biological model)

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Integrative bioinformaticsData integration
Algorithm
Data
tool
Biological Interpretation (model)
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Integrative bioinformaticsData integration
Data 1
Data 2
Data 3
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Integrative bioinformaticsData integration
Data 1
Data 2
Data 3
Algorithm 1
Algorithm 2
Algorithm 3
tool
Biological Interpretation (model) 1
Biological Interpretation (model) 2
Biological Interpretation (model) 3
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Bioinformatics

• Nothing in Biology makes sense except in the
  light of evolution (Theodosius Dobzhansky
  (1900-1975))
• Nothing in Bioinformatics makes sense except in
  the light of Biology