Spring 2018 graduate course, CS 581: Algorithmic Computational Genomics

Instructor: Tandy Warnow, Founder Professor of Computer Science

Tandy-cropped

Course meets Tuesdays and Thursdays 2:00-3:15 Tuesday/Thursday in 1304 SC.

Office hours: Tu 3:15-4:15 PM in Siebel Center 3235

Teaching Assistant: To Be Determined

Course description: This is a course on applied algorithms, focusing on the use of discrete mathematics, graph theory, probability theory, statistics, machine learning, and simulations, to design and analyze algorithms for phylogeny (evolutionary tree) estimation, multiple sequence alignment, genome-scale phylogenetics, genome assembly and annotation, and metagenomics. Each of these biological problems is important and unsolved, so that new methods are needed. Hence, this course will provide opportunities for computer scientists, mathematicians,and statisticians, to do original and important research that can have an impact on biology. Every year, two or more students from this course have done final projects that were subsequently published in major scientific journals; you can be one of them! For examples of these papers, see Mirarab et al., Bioinformatics 2014, Zimmermann et al., BMC Genomics 2014, Davidson et al., BMC Genomics 2015, Chou et al., BMC Genomics 2015, Vachaspati and Warnow, BMC Genomics 2015, and Nute and Warnow, BMC Genomics 2016.

Who should take this class: The course is designed for graduate students in computer science, computer engineering, bioengineering, mathematics, and statistics, and does not depend on any prior background in biology.

Biology graduate students: Every year, biology graduate students have taken the course for credit and done well. Therefore, if you are a biology graduate student where these questions are relevant to your research (especially if using phylogeny estimation or multiple sequence alignments), you are very welcome in the class! However, please meet with me to discuss my expectations regarding homework and exams for biology students.

Pre-requisites: CS 374 and CS 361/STAT 361, or consent of the instructor; no biology background is required. If you did not take these pre-requisites at UIUC but have equivalent coursework in algorithms and probability/statistics, you will probably do fine. If you are a biologist without this background but you are working on problems where phylogeny estimation or multiple sequence alignment are important, you may be able to take the course as well with some extra work. Please see me if you have any questions about whether the course is suitable for you!

Course Textbook: Computational Phylogenetics: An introduction to designing methods for phylogeny estimation, published by Cambridge University Press (and available for purchase at Amazon). The image of the Monterey Cypress is there because of the NSF-funded CIPRES project, whose purpose was to develop the methods and computational infrastructure to improve large-scale phylogeny estimation.


Other course materials: Approximately the first half of the course is based on phylogenomics and multiple sequence alignment, and is based on the textbook. The second half of the course will cover genome assembly and annotation, comparative genomics, and metagenomics, and will be based on the scientific literature. You are expected to do all assigned reading (whether from the textbook or of published papers) in advance of coming to class.

Grading:

Homeworks: Homeworks need to be submitted to MOODLE in PDF format; these are due at 1 PM on the due date, which will generally be Tuesdays. Except as indicated, homeworks can be submitted up to 48 hours past the deadline for reduced credit (80% if within 24 hours and 60\% if within 48 hours); homeworks due after April 4 must be submitted by the deadline for credit. The single worst homework grade will be dropped.

Final Project: The course requires a final project of each student, and is due in class on the last day the class meets. Please provide hardcopy to me directly - in class or in my office hours. You are strongly encouraged to do a research project, but you can also do a survey paper on some topic relevant to the course material. In both cases, your project should be a paper (of about 15 pages) in a format and style appropriate for submission to a journal. Research projects can involve two students, but survey papers must be done by yourself. Grades on the final project depend upon the kind of project you do. For a research paper, your grade will be 30% writing, 40% scientific/algorithmic rigor, and 30% impact. If you do a survey paper, the grade will be 30% writing, 30% summary of the literature you discuss, and 40% commentary (i.e., insight, critical and thoughtful discussion of the issues that come up). See the chapter on Projects from the textbook for possible research projects. You might also want to look at this page for a list of possible final projects provided for this course in a previous year.

Class Presentation: All students will present research papers from the recent scientific literature. The presentation of scientific papers is a major part of the course, and all students are expected to participate actively in discussing these papers.

Course Participation: Your course participation will be evaluated in terms of how you participate in the in-class discussions of the scientific literature we are reading, and also of the presentations of scientific papers given by the other students.

Academic integrity: You are expected to abide by the university academic integrity standards, which means (among other things) that you should never copy anyone else's homework nor let anyone copy your homework. This is particularly important for your final project, especially if you refer to the scientific literature in your project. You must also never plagiarize, which means (among other things) that any text that you copy from another document must be properly attributed (with quotation marks around the copied material, and citation to the document from which you have copied the material). Even paraphrasing can count as plagiarism. All violations of academic integrity standards will be reported to the appropriate university offices. Serious violations will result in a failing grade for the course. Please see this page for a brief discussion of this issue, and the real academic integrity page. The academic integrity code is applied to the homework assignments, as follows. You are encouraged to work with other students on the homework, but if you do this, this is what you should do. First, indicate on the homework who you worked with. Second, do not look at the other homework solutions when you write your own solutions; this includes not looking at someone else's write-up of a critique of some literature. Third, and more generally, you must write your homework solutions entirely on your own, using your own language. Please do not under any circumstances copy homework solutions from anyone else, or let anyone copy from you. Similarly, the academic integrity code is applied to your final project by the expectation that you will not copy text from any paper, and you will give appropriate credit to all material that you use from prior publications, websites, etc. It is particularly important to think about ethics in the context of your research. I have written up some scenarios challenging research integrity, but also see the interesting article in WIRED on The Young Billionaire Behind the War on Bad Science and the Retraction Watch website.

Emergency response recommendations: Please see this webpage.

Additional reading:

Please see 581 2017 course webpage, which has substantial overlap with this year's course.