Genome 540/541

Introduction to Computational Molecular Biology:

Genome and Protein Sequence Analysis

(Winter/Spring Quarter 2004)

• Synopsis: A two-quarter introduction to protein and DNA sequence analysis and molecular evolution, including a brief review of basic molecular biology (structure & evolution of genes, genomes and proteins), probabilistic models of sequences and of sequence evolution, computational gene identification, pairwise sequence comparison and alignment (algorithms and statistical issues), multiple sequence alignment and evolutionary tree construction, comparative genomics, and protein sequence/structure relationships. These are the central computational methods required to determine the "periodic table of biology", i.e. the list of proteins and their evolutionary relationships, which can be regarded as the first stage in the growth of molecular biology into a quantitative science. Moreover, the statistical and algorithmic methods used (which include maximum likelihood estimation, hidden Markov models, dynamic programming) have wide applicability in other areas of computational & mathematical biology.
• Prerequisites: Ability to write computer programs for data analysis is essential (homework assignments will require this.) Some prior exposure to probability and statistics, and molecular biology, is highly desirable but not essential.
• Lectures: TuTh 10:30-11:50 in J-280 (Health Sciences Complex)
  • Review/Homework discussion session: Mon 2-3 in J-280
• Instructors:
  • (Winter) Phil Green (K-343B; phg (at) u.washington.edu)
    • TA: Chris Saunders (ctsa (at) u.washington.edu)
  • (Spring) Joe Felsenstein (J-253; joe (at) gs.washington.edu)
• Office hours: by appointment with the instructor.
• Text:
  • (Required): Fundamental Concepts of Bioinformatics by Dan E. Krane, Michael L. Raymer; Benjamin Cummings 2002; ISBN: 0805346333. Paperback, ~$72 (available from UW Bookstore--South Campus Center Branch, or www.bn.com, www.amazon.com).
  • (Required): Statistical Methods in Bioinformatics : An Introduction by Warren J. Ewens, Gregory R. Grant; Springer, 2001. ISBN: 0387952292 . Hardbound, ~$85 (available from UW Bookstore--South Campus Center Branch, or www.bn.com, www.amazon.com).
  • (Recommended): Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids by Richard Durbin, S. Eddy, A. Krogh, G. Mitchison; Cambridge University Press, 1998. ISBN: 0521629713. Paperback, ~$35.
• To register for credit, contact Brian Giebel (bgiebel (at) u.washington.edu) to obtain an entry code. Enrollment is limited to 25 students. Auditing is allowed.
• See last year's site for approximate syllabus
• Grading & homework policies:
  • The entire course grade is based on the homework assignments, which are due weekly (more or less). No tests or exams.
  • The homework assignments involve writing programs for data analysis, and running them on a computer you have access to (we cannot provide computers). We don't require a specific language, since it is not practical for me to grade your code, just the output from running your programs. However it is important to use a language that allows you to write programs that will run fast on large datasets; ideally a compiled language such as C or C++. You may be able to get by with an interpreted language such as Perl or Java (some people have), however there is a definite risk that programs in these languages will take very long to run -- which means that (i) you will need access to a very fast computer to run them on, and/or (ii) you will need to get the program written early enough that you can allow 2 or 3 days for the actual run and still be able to turn in the assignment on time.
  • Homework is due before midnight on the indicated date. After that it will be accepted, but penalized.
  • It is OK to run your program on someone else's input datafile, and compare outputs to see if you get the same results. However it is not OK to share programs, or to get someone else to debug your program. A key part of the course is being able to write and debug your own programs for data analysis.

  Since important announcements (e.g. schedule changes) may be made by email in advance of lectures, all attendees (whether or not registered for the course) should send their email addresses to Phil Green at the above address.

• Message Board

HOMEWORK ASSIGNMENTS (Winter Quarter):

• Assignment 1, due Friday Jan. 16
• Assignment 2, due Sunday Jan. 25
• Assignment 3, due Sunday Feb. 1
• Assignment 4, due Sunday Feb. 8
• Assignment 5, due Sunday Feb. 15
• Assignment 6, due Sunday Feb. 22
• Assignment 7, due Sunday Feb. 29
• Assignment 8, due Sunday Mar. 7
• Assignment 9, due Sunday Mar. 14

SYLLABUS & LECTURE SLIDES FOR WINTER QUARTER:

Math Notation

Nature paper on Avida ( Avida web site )

Nature paper on human genome sequence

Nature paper on mouse genome sequence

• Biology review
  • Lecture 1: living organisms as imperfect replication machines; theory of evolution & tree of life; 'artificial life'; physical laws relevant to biological organisms; molecular structure
  • Lecture 2: entropy and 2d law of thermodynamics, implications for living organisms; DNA, RNA & protein structure; gene expression & "Central Dogma"; the genetic code; codon usage. Reading: Krane & Raymer pp. 1-19. site with molecular biology graphics used in this lecture.
  • Lecture 3: gene and genome structure in prokaryotes and eukaryotes; "global" genome organization. Reading: Krane & Raymer pp. 117-143.
  • Lecture 4: GC content variation in mammalian genomes. Mutations as molecular basis for evolutionary change. CpG islands. Large-scale mutational changes. Mutation fates. Neutral theory, mutation & substitution rates. Reading: Krane & Raymer pp. 57-65.
  • Lecture 5: Mutation & substitution rates (cont'd). Sources and characteristics of sequence data; Genbank and other sequence databases. Reading: Krane & Raymer pp. 19-27 ("Molecular Biology Tools").
• Basic algorithms
  • Lecture 6: overview of goals & experimental approaches of molecular biology; role of sequence analysis. Generalities on algorithms for biological data; finding exact matches in sequences; directed graphs; depth structure of directed acyclic graphs (DAGs); trees and linked lists.
  • Lecture 7: Dynamic programming on weighted DAGs. Maximal-scoring sequence segments; edit graphs & sequence alignment (local & global): Smith-Waterman algorithm, Needleman-Wunsch algorithm. Reading: Krane & Raymer pp. 34-47.
  • Lecture 8: Local vs global. Profiles; edge weight issues; linear space algorithms; hidden state DAGs; general & affine gap penalties; extending graphs to have "edge memory"; Smith-Waterman special cases, self-similarity.
  • Lecture 9: Speedups based on nucleating word matches: BLAST, FASTA, cross_match. Multiple sequence alignment: biological considerations; product DAGs;projection-reduced complexity Reading: Krane & Raymer pp. 48-53.
• Probability distributions on sets of sequences
  • Lecture 10: Carillo-Lipman, Hein, & progressive alignment algorithms. Probability models on sequences; review of basic probability theory: probability spaces, conditional probabilities, independence; failure of equal frequency assumption for DNA, proteins. Reading: Ewens & Grant sections 1.1, 1.2, 1.12
  • Lecture 11: Site models; examples: 3' splice sites, 5' splice sites, protein motifs; limitations of site models (variable spacing, non-independence) -- splice site illustrations; site probability models. Comparing alternative models, hypothesis tests, likelihood ratio tests. Neyman-Pearson Lemma, approximation to significance level for likelihood ratio test. Weight matrices for site models Reading: Ewens & Grant 3.1, 3.2, 3.4, 8.1, 8.3
  • Lecture 12: ; weight matrices for splice sites in C. elegans, score distributions. Hidden Markov Models: introduction; formal definition; probabilities of sequences. HMM examples: site models, 2-state models, 7-state prokaryote genome model. Reading: Ewens & Grant 5.2, 5.3.1, 5.3.2, 11.1
  • Lecture 13: Computing HMM probabilities via associated WDAG. Parameter estimation: Viterbi training, Baum-Welch (EM) algorithm. Reading: Ewens & Grant 11.2, 11.3
  • Lecture 14: Information theory: entropy, information inequality, Boltzmann distribution, coding theory/data compression, Kraft inequality, entropy & expected code length, uniquely decodable codes; information; relative entropy. Relative entropies of site models. Reading: Ewens & Grant 1.14, Appendix B.10.
  • Lecture 15: Sequence logos. Random variables; exact probability distribution for weight matrix scores. Non-independence in background & compositional models. Reading: Ewens & Grant 1.3.1, 1.3.2, 1.3.3, 1.4, 1.5, 2.10.1
  • Lecture 16: Order k Markov models; minimum description length principle. Gene identification in eukaryotes.
  • Lecture 17: Gene identification in eukaryotes (cont'd).
  • Lecture 18: Gene identification in eukaryotes (cont'd). 'Non-random' mutations; transcription-coupled asymmetry. paper describing this work.
  • Lecture 19: Mutation asymmetry (cont'd). Karlin-Altschul theory for high-scoring segments. Reading: Ewens & Grant chapter 7
  • Lecture 20: Karlin-Altschul theory (cont'd).

HOMEWORK ASSIGNMENTS (Spring Quarter):

• Assignment 1, due Friday April 9. Tree used in this homework.
• Assignment 2, due Friday April 16.
• Assignment 3, due Friday April 23.
• Assignment 4, due Friday April 30.
• Assignment 5, due Friday May 7.
• Assignment 6, due Friday May 14.
• Assignment 7, due Friday May 21.

SYLLABUS & LECTURE SLIDES FOR SPRING QUARTER:

• Lecture 21: Parsimony phylogeny methods. Fitch and Sankoff algorithms for counting steps on a given tree. Compatibility method and pairwise compatibility theorem. Reading: Krane & Raymer 78-86, 97-105; Ewens & Grant 385-387, 398-400; Durbin et al. 160-163, 173-176, 188-189
• Lecture 22: Searching tree space. Counting trees. Heuristic rearrangement, sequential addition, star decomposition, disk-covering, and quartet puzzling methods. Branch and bound for finding all most parsimonious trees. Reading: Krane & Raymer 81-83, 105-108; Ewens & Grant 398-400; Durbin et al. 163-165, 176-179
• Lecture 23: Distance methods. Least squares fitting of trees to matrices of distances. Statistical behavior. Neighbor-joining and UPGMA methods. Reading: Krane & Raymer 65-66, 85-93; Ewens & Grant 387-398; Durbin et al. 165-173, 189-191
• Lecture 24: Models of DNA change. Markov chain models for evolutionary change of DNA sequences along lineages of a phylogeny. Jukes-Cantor, Kimura K2P, F84, HKY, Tamura-Nei, General Time-Reversible, General 12-parameter models. Reading: Krane & Raymer 67-68; Ewens & Grant 365-382; Durbin et al. 193-197
• Lecture 25: Likelihood on phylogenies. Likelihoods for DNA models, algorithm for computing likelihoods on a tree. HMM rate variation again. Tree space with branch lengths. Likelihood ratio tests of molecular clocks, models of change. Reading: Krane & Raymer 93, 70-74; Ewens & Grant 400-408; Durbin et al. 197-209
• Lecture 26: Modelling variation of rates of evolution from site to site. Gamma distribution of rates, Hidden Markov Model for rates and Forward-Backward algorithm to evaluate likelihood for it. Hasegawa 14-species 232-site primate mitochondrial sequences Reading: Krane & Raymer 70-74 (again); Ewens & Grant 415-416; Durbin et al. 215-217
• Lecture 27: Bootstraps and jackknifes for resampling among sites to get confidence intervals on the tree. What they do and don't mean. Reading: Krane & Raymer 108-11; Ewens & Grant 408-415; Durbin et al. 179-180, 212-215
• Lecture 28: Coalescents: Trees of genes within species and what happens to molecular evolution as one gets down near the species level. Reading: Krane & Raymer 114; Durbin et al. 211-212
• Lecture 29: Markov Chain Monte Carlo methods. Using them to compute likelihoods summing over all coalescent genealogies. Griffiths/Tavare' and Kuhner et. al. approaches. Reading: Ewens & Grant 310-314; Durbin et al. 206-207, 211-212
• Lecture 30: Tree alignment. Why you ought not do multiple sequence alignment without thinking about phylogenies at the same time. Sankoff's strategy, Clustal and progressive alignment as approximations. Probabilistic models of insertion and deletion and prospects for ever getting anywhere using them. Reading: Krane & Raymer 93-94; Durbin et al. 180-188
• Lectures 31-32 (Martin Tompa)
• Lectures 33-34 (including homework assignment) and Related papers (Hong Qian)
• Lectures 35-38 (Bill Noble)
• Lectures 39-40 (David Baker)

OTHER RELEVANT COURSES AT UW:

• CSE 590cb (Reading and Research in Computational Biology)
• CSE 527 (Computational Biology)
• Pathobiology 536 (Bioinformatics and Gene Sequence Analysis)
• Genetics 562 (Spring 2001): Population Genetics
• Genetics 570 (Spring 2002): Phylogenetic Inference
• Genetics 590 (Autumn 2002): Evolution and Population Genetics Seminar

COMPUTATIONAL BIOLOGY COURSES AT OTHER SITES:

• Computational Molecular Biology (Sean Eddy, Washington University)
• Algorithms for Molecular Biology (Ron Shamir, Tel Aviv University)
• Computational Molecular Biology (Doug Brutlag & Lee Kozar, Stanford)
• Representations and Algorithms for Computational Molecular Biology (Russ Altman, Stanford)
• Introduction to Computational Molecular Biology (Bonnie Berger and Manolis Kamvysselis, MIT)