For catalog copy and pre-requisites, see the main page for BME100.

Who, When, and Where:

Lectures: MWF 3:30-4:40 Social Science II, Room 137

One lab section a week is required:
F 11:30am Baskin Engineering 105

You must register for the lab and the course together---neither can be taken without the other. WARNING: we may not get the times and locations in the Registrar's time schedule---labs tend to get moved in the first week to accomodate demands on Baskin Egnineering 105 and to avoid TA schedule conflicts.

Although you must register for the lab, attendance at lab sections is optional---it will be a time when I'll be in the lab to help out with Perl questions, with bioinformatics tools on the web, with debugging, and with general help with the homework assignments.

Texts

Programming Perl
Larry Wall, Tom Christiansen & Jon Orwant
latest edition
O'Reilly and Associates

Considered the best single book on PERL---this is the main reference work on the language, and every PERL programmer should have a copy of it handy. You may use other PERL tutorials or references, but I expect you to have easy access to this one. We will be covering just the basics of PERL, not open-source packages like BioPerl, which you may wish to learn on your own.

Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids from Cambridge University Press by R. Durbin, S. Eddy, A. Krogh, and G. Mitchison.

This book is a tutorial introduction to the use of hidden Markov models and other probabilistic models for sequence analysis problems in computational molecular biology, but is aimed more at a gradauate-student audience. We've been using it for years in the both graudate bioinformatics courses, but Fall 2002 marks the first year for using it in the undergraduate course. This is a grad text and reference book that every bioinformatics programmer should have.

(Be sure to look at the errata page.)

Darling models

I am planning to add some assignments this year to build physical models of peptides (and maybe DNA base pairs) using the Darling model kits. These kits are available over the web at http://www.darlingmodels.com/ I recommend getting the "NMSU Biochemistry" kit, though the cheaper "protein alpha helix--pleated sheet" kit may suffice. I have found these kits to give me a much better insight into protein flexibility and rigidity than the standard ball-and-stick models used in organic chemistry classes, and they are fun to play with. To reduce costs, it is quite reasonable for students to share a kit.

Some initial instructions for building a protein backbone with this model kit are now available.

Evaluation

Based on the first running of the course in Fall 2001, there will be no exams. It turns out to be very difficult to make up small enough problems for examination---almost all the homework exercises are much larger problems than could reasonably be given on a timed exam.

The assignments will be distributed on the web (see http://www.soe.ucsc.edu/~karplus/bme100/f02/homework.html).

The relative weights of the different types of assignment in the evaluation has not been determined yet---it should be roughly proportional to how much time the different assignments take to do well. We will try to assign points to each assignment as it is given, but the total number of points won't be known until we've created all the assignments.

Academic Integrity

Collaboration without explicit written acknowledgement will be considered cheating. Collaboration on lab assignments with explicit written acknowledgement is encouraged---guidelines for the extent of reasonable collaboration will be given in class.

Rough list of topics we'll probably cover (not necessarily in order)

1. Quick review of the fundamental dogma of biology: DNA->RNA->protein, bases, codons, amino acids
   (3.5 lectures)
2. Stochastic models, Bayes Rule, 0-order Markov chain
   (0.5 lecture)
3. First-order Markov model, pseudocounts. (1 lecture)
4. Converting abitrary scores to stochastic models: P-value and E-value. Brief discussion of Z-scores (Gaussian dist.) and fat tails of extreme-value (Gumbel dist.) (1 lecture)
5. Interpreting classification results: true/false positives, specificity, sensitivity, ROC curves (1 lecture)
6. Entropy, relative entropy, sequence logos. (1 lecture)
7. Mutual information (1 lecture)
8. Substitution matrices and sequence alignment scores. (1 lecture)
9. Aligning sequences to sequences, dynamic programming We'll do the the simple, but inefficient algorithm (for aribtrary gap costs) first. (1 lecture: the alignment problem and global dynamic programming with arbitrary gap costs) (1 lecture: global dynamic programming with linear gap costs, traceback) (1 lecture: affine gap costs. Global and local dynamic programming)
10. Introduction to Hidden Markov models (1 lecture on HMMs) (1.5 lectures on profile HMMs giving Viterbi algorithm)
    (Could have been based on Rachel Karchin's lecture (powerpoint slides), but weren't.)
11. Training HMMs (one lecture) (forward-backward algorithm)
12. Multiple alignment techniques Overview and progressive alignment (1 lecture) T-Coffee (1 lecture) paper on T-coffee:
    T-Coffee: A novel method for fast and accurate multiple sequence alignment.
    Notredame C, Higgins DG, Heringa J.
    J Mol Biol 2000 Sep 8;302(1):205-17
13. Library databases (training session by library staff). New access method for PUBMED/MEDLINE, also covering BIOSIS and "Web of Science" (Science Citation Index). (1 lecture)
14. Protein secondary structure (DSSP and STRIDE), mutual information, and entropy, in order to explain second track of 2-track HMM. Discuss secondary structure prediction using neural nets (2 lectures).
15. Phylogeny: brief mention of maximum-likelihood and parsimony. Additivity assumption. UPGMA algorithm presented, ultrametric assumption and molecular clocks, intro to neighbor-joining (no proofs) (1 lecture)
16. RNA structure and Stochastic Context-Free Grammars
17. Revisiting traceback in sequence=sequence alignment (everyone had it wrong in the homework assignment, so I must have done a poor job of explaining it the first time).
18. Combining secondary structure, fold-recognition, and new-fold methods for protein structure prediction. Using the transparencies to be given at Schloss Dagstuhl I could have handed out book chapter on SAM-T2K, but didn't.
19. Guest lectures. (I will be gone for the last 5 lectures of the quarter, but I have arranged some top-notch guest lectures to cover for me.)
    • Bill Scott: X-ray crystallography (2 lectures)
    • David Haussler: topic to be determined.
    • Melissa Cline
    • Hui Wang: alternative splicing

Rough list of topics we didn't have enough time to do more than briefly mention:

• Contact order and folding rate. Last year, handed out paper on contact order:
  Contact order, transition state placement and the refolding rates of single domain proteins.
  Plaxco KW, Simons KT, Baker D.
  J Mol Biol 1998 Apr 10;277(4):985-94
• Phylogenetic analysis
• DNA microarrays and expression data
• Gene finding
• Proteomics
• RNA structure
• DNA assembly
• Fast methods for searching (BLAST and BLAT).
• RNA genes, DNA microarrays, computational and functional genomics in the Lowe lab The lecture notes from last year are on-line at http://www.soe.ucsc.edu/~lowe/lectures/.

Other resources on the web

Handouts for Rune Lygsoe's summer 2002 course on bioinformatics

User's Guide to the Human Genome (in nature genetics).

SoE home

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