Graduate Group in Genomics and Computational Biology

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Course Requirements

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In view of the highly varied academic backgrounds of students in GCB, members of the Advisory Committee meet with each student individually twice per semester (Year 1 and 2) and plan courses and rotations.  Under the GCB curriculum, students are required to complete a minimum of eight classes, as specified below. The advising committee helps design a course schedule for each student that matches his/her needs and interests.

Required Courses:

  1. Introduction to Genome Science (GCB 531) (John Hogenesch)
    This course serves as an introduction to the main laboratory and theoretical aspects of genomics and computational biology. The main topics discussed center around the analysis of sequences (annotation, alignment, homology, gene finding, variation between sequences, phylogeny reconstruction/estimation), and the functional analysis of genes (expression levels, proteomics, screens for mutants), together with a discussion of gene mapping, linkage disequilibrium, genetics of complex diseases, and integrative genomics. 
  2. Computational Biology (BIOL 536/CIS 535/GCB 536) (Junhyong Kim)
    This is a graduate level introductory computational biology course designed for both biology students and computer science/engineering students. The course will cover fundamentals of algorithms, statistics, and mathematics as applied to biological problems. After taking this course students will understand the algorithmic/mathematical principles of quantitative methods used in computational biology and gain appreciation of quantitative modeling in biomedical problems.
  3. Control of Gene Expression (BIOM 555)
  4. At least one GCB Seminar Course (GCB  513, 537, or 752)
  5. One course in statistical inference
    Possible choices include but are not limited to BIOL 446: Statistics for Biologists, BIOL 556: Advanced Statistics, ENM 503: Introduction to Probability and Statistics, or STAT 511: Statistical Inference.
  6. Four upper-level elective courses in biology, computer science, or statistics
    Of the four, at least one should be a "core" biology course (usually CAMB 550: Genetic Principles; BIOM 600: Cell Biology and Biochemistry; or BMB 508: Macromolecular Biophysics), one should be a "core" computer science course (usually CIS 520: Machine Learning; CIS 550: Database and Information Systems; or CIS 502: Analysis of Algorithms), and one should be a statistics course (STAT 510: Probability; STAT 542 Bayesian Data Analysis; BSTA 785: Statistical Methods for Genomic Data Analysis; or CIS 520: Machine Learning). Some other example course choices are listed below. Some example course choices are listed here.