CS 395 Algorithmic Techniques for Bioinformatics

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CS 395Algorithmic Techniques for Bioinformatics
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Ming-Yang Kao
Room 346 847-491-2272Email kao_at_cs.northwestern
.edu URL www.cs.northwestern.edu/kaowww.cs.n
orthwestern.edu/kao/cs395-bioinformatics
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What is bioinformatics?
Bio Biology Informatics Computer Science
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General Scope
computer science
biology
Use biology ideas to solve computer science
problems
Use computer science tools to solve biology
problems
our courses focus
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Use Biology to Solve CS Problems

• DNA Computing
• DNA Self-Assembly
• Genetic Algorithms
• Neural Networks
• Others

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Use CS to Solve Biology Problems

• Bioinformatics
• or
• Computational Biology
• Related fields
• computational neuroscience
• computational ecology
• medical informatics
• many more ...

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The State of Bioinformatics

• huge volume of biological data
• An enormous amount of biology data have been
  and will continue to be generated.
• E.g., human genome
• 3 109 base pairs
• 6 109 bits
• .75 GB
• useful but complex information in the data
• computational technologies for extracting
  information

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Key Types of Data and Information

• DNA encodes genetic information
• RNA copies and transports such information to
  produce proteins
• Protein performs various biological functions

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Some Main Areas of Bioinformatics

• A key goal of bioinformatics To study biological
  systems based on global knowledge of genomes,
  transcriptomes, and proteomes.
• Genome entire sets of materials in the
  chromosomes.
• Transcriptome entire sets of gene transcripts.
• Proteome entire sets of proteins.

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Example Research Areas of Bioinformatics

• DNA sequencing
• DNA microarray analysis
• DNA self-assembly for nano-structures
• DNA word design
• RNA secondary structure prediction
• Protein sequencing
• Proteomics
• Protein database search
• Protein sequence design
• Protein landscape analysis
• Phylogeny reconstruction
• Phylogeny comparison

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Career and Research Opportunities

• Still in its infancy
• There are lots of relatively doable open
  problems floating around.
• Simple algorithmic techniques can still
  extract useful information.
• Future trends
• More sophisticated techniques would be
  required to extract more complicated information.

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Learning Strategies for This Course (1)

• Changes in lab techniques for analyzing
  DNA/RNA/Protein

Changes in computational problems
For long lasting usefulness, we focus on
fundamental algorithmic techniques that are
applicable to multiple problems
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Goals of This Course

• Problem Formulation
  Learn how to formulate computational problems
  from biology problems.
• Problem Solving
  Learn how to apply and develop fundamental
  algorithmic techniques for solving such problems.

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Learning Strategies for This Course (2)

• If your major is CS (or allied fields),
• Learn as much biology as needed to start working
  on problems ASAP.
• While you are working on problems, continue to
  pick up biology knowledge.

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Learning Strategies for This Course (3)

• If your major is biology (or allied fields),
• Learn as much CS as needed to start working on
  problems ASAP.
• While you are working on problems, continue to
  pick up CS knowledge.

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Reading Assignment for Today

• Chapters 1 and 13 of Pevzner.

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The Remainder of Todays Class

• Go over the syllabus.
• If you would like to take this class, please put
  your name on the sign-up sheet.
• If you have any further questions, I will stay
  around after class to answer them.