Genome 540

Introduction to Computational Molecular Biology:

Genome and Protein Sequence Analysis

(Winter Quarter 2021)

• Synopsis: Together with Genome 541, a two-quarter introduction to protein and DNA sequence analysis and molecular evolution, including 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 in a general purpose programming language (R and MATLAB are not suitable). Some prior exposure to probability and statistics, and molecular biology, is desirable but not essential.
• Lectures: will be available as Zoom recordings one day prior to the corresponding discussion.
• Discussions: TuTh 10:30-11:50 via Zoom.
• (Zoom links will be emailed to registered attendees and auditors by the instructors.).
• To register for credit, contact the instructor (Phil Green, phg [ a t ] uw.edu) for permission, and Brian Giebel (bgiebel (at) uw.edu) to obtain an entry code. Enrollment is limited to 25 students. Auditing is allowed.
• Instructors:
  • Phil Green (phg [ a t ] uw.edu)
  • TA: Dani Faivre (dfaivre [ a t ] uw.edu)
• Office hours:
  • Dani: by appointment.
  • Phil: by appointment.
• Grading & homework policies:
  • The entire course grade is based on the homework assignments, which are due weekly. No tests or exams.
  • The homework assignments involve writing programs for data analysis (using a general-purpose programming language -- R and MATLAB are not suitable for these assignments!), 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 us 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 Python (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 by 11:59 pm on the indicated date. After that it will be accepted, but penalized. Specifically, each assignment is worth 10 points, from which 1 point will be deducted for each day (or fraction thereof) that you turn it in late. The maximum deduction for being late is 3 points (even if you are more than 3 days late). If you get less than 7 points on an assignment, you are allowed to redo it and take the new score (which will be 7, i.e. 10 - 3, if there are no mistakes).
  • 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.
• Recommended texts:
  • 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. www.amazon.com.
  • Statistical Methods in Bioinformatics : An Introduction (Statistics for Biology and Health) by Warren J. Ewens, Gregory R. Grant; Springer, 2005. ISBN: 0387400826. Note that this is the 2d edition!! www.amazon.com.
• See last year's site for approximate syllabus

HOMEWORK ASSIGNMENTS: