BioE131/231

Introduction to Computational Biology.

• BioE131/231
  • Course info
    • Policies
    • Student wiki
    • Announcements
  • Notes and handouts
    • Slides
      • Computational virus design
      • Scripting compbio applications
      • DNA pattern recognition
      • Genome and pathway databases
      • Information content of DNA
  • Syllabus
    • • Accompanying notes
    • Lab practicals
    • Homework exercises
    • Textbooks
    • Other resources

Course info

• Code: Bioe131/231
• Title: Introduction to computational biology
• Instructor: Ian Holmes (office hours: 11am-noon Wednesdays, 374C Stanley Hall; or by appointment)
  • GSI: Allison Berke (office hours: ... )
• When:
  • Lectures: Mon, Wed & Fri, 3-4pm, 4 Evans Hall
  • Lab: Mon & Wed 4-5:30, 1171 Etcheverry Hall
• Lecture log

Policies

• Grading scheme
  • 50% homework assignments
    • Lowest homework grade will be discarded
  • 15% midterm exam
  • 15% final exam
  • 20% final project

• Extensions/Alternate Exam Dates
  • Requests for extensions on homework due dates must be submitted to Ian Holmes via email at least 2 days before homework due date, clearly stating reason(s) for request.
  • Please plan to attend the scheduled exam times. Requests for alternate exam dates must be submitted to Ian Holmes via email as soon as possible, clearly stating reason(s) for request. An alternate date request is only likely to be granted under extreme circumstances (family emergencies, major illness, etc.)

Student wiki

• Fall08 wiki for student registration

Announcements

Notes and handouts

Slides

Computational virus design

• Transposons, viruses & worms (PDF)
• Molecular biology databases (Powerpoint)
• Bioengineering RNA (Powerpoint)
• RNA folding algorithms (Powerpoint)

Scripting compbio applications

• Scripting in bioinformatics (Powerpoint)

DNA pattern recognition

• Alignment basics (Powerpoint)
• Homology (Powerpoint)
• Multiple alignment (Powerpoint)

Genome and pathway databases

• Biological ontologies (Powerpoint)
• Genome annotation & pathway mining (Powerpoint)
• GFF: Gene Finding Format (Powerpoint)

Information content of DNA

• Probability & information (Powerpoint) (available as PDF)
• Algorithmic complexity + incomplete review (Powerpoint)

Syllabus

Approximate sequence of lectures:

• Introductory case study
• Overview of syllabus; available means of assessment..
  • group & individual presentations; literature reviews; class participation; homework; exam(s); project
• Review of fundamental molecular biology
• Biophysical principles of RNA and protein folding
• Overview of biological databases
• Biophysics of synthetic biology: RNA folding kinetics & viral genome design
• Introduction to Unix
• Introduction to Perl programming: loops, variables, subroutines; file manipulation; data structures
• Assemblers, compilers, interpreters & virtual machines: machine code, C, Perl and Java
• Sequence alignment algorithms: Needleman-Wunsch, Smith-Waterman, Gotoh, BLAST
• Genome annotation; biological ontologies, pathway databases
• Probabilistic inference; Bayes' theorem; experimental error; expectation and variance
• Quick refresher in basic distributional analysis...
  • Basic combinatorics; binomials and multinomials
  • Geometric, exponential, Poisson distributions
  • Gaussian distribution; mixture distributions
• Quantitative measures of information; illustration via data compression
• Log-likelihood ratios and substitution matrices; coding & cryptography
• Probabilistic models for sequence motifs; "sequence logos"
• Algorithmic complexity & "big-O" notation: examples from compbio
• Finite state machines; multiple alignment; phylogenetic reconstruction
• Rate variation, evolutionary trace and phylogenetic profiling; applications to design
• Structural biology, protein structure prediction, protein design
• Clustering algorithms: K-means, K-medians; application to microarray data analysis
• Sequence assembly & metagenomics; examples (human microbiome; bioenergy)
• Guest lectures: computational biology at Berkeley

Lab practicals

1. Unix
2. Biological Databases
3. RNA folding
4. Perl Basics
5. Perl Hashes & Arrays
6. Perl Pattern Matching
7. Sequence Alignment
8. Information Content of DNA
9. Bacterial Gene Prediction
10. Primate Phylogeny
11. Pathway Mining
12. Protein Visualization
13. Nussinov Algorithm

Homework exercises

Homework exercises will be assigned in labs and posted on the individual lab pages. Programming assignments will be graded both for form (style) and function (correctness). Stylistic expectations will be outlined on the style guidelines page by the time the first assignment is given.

Textbooks

No textbook purchase is required to take the class.

References to the following textbook (which can be freely downloaded) appear occasionally as recommended reading:

• The MacKay Book: MacKay, DJC. Information Theory, Inference and Learning Algorithms. ISBN:0521642981
  • Can be downloaded from here (copyright grants permission to view but not print)

The following Perl books from O'Reilly may be a useful supplement to what's taught in class:

• The following are the two "classic" Perl tutorial and reference books:
  • Learning Perl (ISBN:9780596101053)
  • Programming Perl (ISBN:9780596000271)
• The "Perl for Bioinformatics" series have more bioinformatics-oriented examples:
  • Beginning Perl for Bioinformatics (ISBN:9780596000806)
  • Mastering Perl for Bioinformatics (ISBN:9780596003074)

Other resources

• DECF website and machine stats
• Frequently Asked Questions