Introduction to Computational Biology.
- Code: Bioe131/231
- Title: Introduction to computational biology
- Instructor: Ian Holmes (follow the link to my page for my office hours)
Calendar & announcements
Lecture slides will be posted on the BioE131 lecture notes
page after each group of lectures has concluded and corrections have been applied.
The following self-directed exercises may be used to assess knowledge of examinable material.
These exercises are not for credit, except where otherwise stated.
Except for sample examination questions, each of the following should take roughly 2 hours.
For midterms 1 & 2
- SampleMidterm questions (covering both midterms, and including links to past papers)
For midterm 2 only
See also the BioE131 fact sheet
- Grading scheme
- 40% homework assignments
- 30% midterm exams
- 30% final project
- Extensions/Alternate Exam Dates
- Requests for extensions on homework due dates must be submitted to IanHolmes via email at least 2 days before homework due date, clearly stating reason(s) for request.
- Please plan to attend the scheduled exam/presentation times. Requests for alternate exam/presentation dates must be submitted to IanHolmes 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.)
- Conflicting commitments on other courses will not usually be considered adequate reason to moving exams or homework deadlines unless the conflict is experienced by a majority of the class.
- Every year several requests failing to meet the above criteria are declined -- please do not invite mutual disillusion by asking for an exception.
- We want to encourage you to work together, so for regular homework assignments, you may submit jointly with up to one other student as long as you identify who it was you worked with. However, we also want to encourage mixing, so you can contribute no more than three homework assignments with any one partner. After that you will have to rotate with someone else.
sequence of lectures (see also BioE131 weekly schedule
- 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
- Introduction to Unix
- Introduction to Python programming: strings, methods, lists; file manipulation; data structures
- Biophysics of synthetic biology: RNA folding kinetics & viral genome design
- 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
- Extreme-value, hypergeometric 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, RNA & protein structure prediction, RNA & protein design
- Clustering algorithms: K-means, K-medians; application to transcriptomic data analysis
- Sequence assembly & metagenomics; examples (human microbiome; bioenergy)
- Computational biology at Berkeley
- Biological Databases
- Python Lists & Dictionaries
- RNA folding
- Sequence Alignment
- Protein Visualization
- Catch-up lab; project work
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.
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)
is also a very useful guide to probabilistic biological sequence analysis.
The following Python books may be a useful supplement to what's taught in class:
- For a book with more bioinformatics-oriented examples:
- 22 Nov - Thanksgiving
- 23 Nov - Thanksgiving
- 28 Nov - Last lecture before RRR week
- 07 Sep - UsingWikiHomework due
- 21 Sep - Python Basics Homework due
- 29 Sep - Python sequence simulator h/w due
- 12 Oct - RNA logic homework due
- 27 Oct - Nussinov algorithm homework due
- 02 Nov - Broken Telephone Tree homework due (optional)
- 09 Nov - Pairwise alignment homework due
- 17 Nov - Information content homework due
- 17 Nov - Bayesian inference homework due
- 28 Nov - Phylogeny homework due
- 06 Dec - DiffusionLimitedAggregationHomework due (optional)
- 15 Oct - Midterm #1
- 26 Nov - Midterm #2
- 7 Nov - Final project announcement
- 26 Nov - Final project team names, member lists & provisional themes due
- 13 Dec - Final project exam presentations (8-11am)
- 14 Dec - Final project peer reviews due (12noon)
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