Previous year's CSCI2820 Class Syllabus (from 2012)

Syllabus [PDF]

Course Topics [PDF]

CSCI 2820 (formerly 2950L) Advanced Algorithms in Computational Biology and Medical Bioinformatics

Instructor: Sorin Istrail (Office hours: Tuesday 4-6pm or by appointment)
Email: sorin at cs dot brown dot edu
Phone: (401) 863-6196

Graduate TA: Pinar Demetci (Office hours: Thursday 10:30am-12pm
Email: pinar underscore demetci at brown dot edu
Time and Place: Tuesdays and Thursdays 2:30-3:50 Zoom Meeting ID: 926 8418 6590
Class Page:
Course Description: This course is devoted to computational problems and methods in the emerging field of Medical Bioinformatics where genomics, computational biology and bioinformatics impact medical research. There is no prerequisite for this course and individual accommodations will be made for students of different backgrounds; we will tailor assignments specifically for Life Sciences students (Biology, Chemistry, Medical) or Computational students (Applied Math, Computer Science, Engineering).
  1. Linkage Disequilibrium(LD), LD Measures (D', r2, directed informativeness), and the Unification of LD Measures Problem
  2. Spectral Graph Theory and Spectral Clustering
  3. Population Genetics
    1. SNPs, Tagging SNPs (the minimum informative subset of SNPs problem) and Haplotypes
    2. Population genetics fundamentals: models, linkage disequilibrium, identity by descent (IBD), pedigrees, trios
    3. The Coalescent Theory and the Miniciello-Durbin ancestral recombination graph reconstruction
    4. Population substructure
    5. Polya urn game
    6. Ewens sampling lemma
    7. Haplotype Phasing: Short-range phasing (Clark-consistency graphs, Clark methods, maximum-likelihood, parsimony, PHASE) and long-range phasing (deCODE method)
  4. Genome-wide Association Studies (GWAS)
    1. The Next Generation Sequencing in GWAS
    2. Tests of associations, hypothesis testing, and multiple comparisons corrections
    3. Disease models: common-disease common-variant, Zollner-Pritchard, McClellan- King genetic heterogeneity in human disease
    4. The missing heritability problem: Common variants vs. rare variants
    5. Genome-wide graph theory algorithms
  5. GWAS case studies: Autism, Multiple Sclerosis, Type 2 Diabetes, Schizophrenia

Structure of the Course


Homeworks will be assigned every 2-3 weekS. Towards the second half of the courses, homeworks will be assigned less frequently for students to focus on their final projects. Homework problems will consist of a mix of general problems, programming assignments, and critical readings of research articles. Homeworks must be turned in on time and late submissions may be subject to penalties. Programming may be done in Python, R, Java, or Matlab. We encourage using Python and R.


The list of suggested projects will become available on the projects page. There will be two presentations for the class projects, one during the middle of the term and one at the end of the term.


  1. Final project – 45%
  2. Homeworks – 40%
  3. Presentation – 15%

Extra credit will be given for original contributions to research projects.

Course Resources

Web Site

Nearly everything you will need will be made available through the course web site and Piazza, including TA notes, slides, homework assignments, etc. Please check Piazza announcements regularly.


(recommended – not required) Principles of Population Genetics (Fourth Edition, 2007) Daniel L. Hartl and Andrew G. Clark


The course is designed for graduate students and upper-level undergraduates. It is also open to Computer Science and Math students, as well as biological and medical students. Since the class will be comprised of students with a diverse background, homework and tests will involve general questions for all students as well as more in-depth questions, which you will be able to choose from in accordance with your particular background. While there are no formal prerequisites for the courses, you should have a strong background in at least one of these two areas. Please contact the professor if you are unclear as to whether you have the necessary prerequisites for the course.

Collaboration Policy

You may discuss the homework problems with other students or use other resources such as textbooks or the Internet. However, you must not obtain answers directly from anyone. All homeworks will be submitted individually and should be written up by yourself. We take plagiarism seriously.