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 take three classes per semester prior to the Candidacy Exam, in addition to their lab work. The advising committee helps design a course schedule for each student that matches his/her needs and interests.

Required Courses:

  1. Experimental Genome Science (GCB 534) (C. Brown and J. Murray)
    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. Advanced Computational Biology (GCB 537) (Y. Barash and L. Wang)
    GCB537 is a course geared towards GCB students, who are expected to have exposure to basic computational biology techniques and general familiarity with modern biology. This course is designed to (1) broaden the students’ knowledge in more advanced computational techniques applied to a wide variety of biological problems, and (2) provide a forum for the improvement of the students’ presentation skills. Papers are selected to cover a wide variety of areas not covered to the same degree in any of the other lecture-based courses.
  3. Genomics Seminar (GCB 752) (S. Diskin and H. Riethman)
    Recent papers from the primary genomics literature form the core material for the course. Each 3-hr seminar features a major topic in genomics, with student presentations centered on papers selected within the topic area. The “presenting” student will give a 10-15 minute introduction to the paper and will show powerpoint slides of the data in the paper.  All students in the class are expected to have read and to be prepared to discuss the papers presented. For example, following the introduction, non-presenting students will be called upon to explain a particular table or figure, or to discuss a point raised in the paper.

Strongly recommended courses:

  1. BIOM 600: Cell Biology
    BIOM 600 is an intermediate level graduate course designed to introduce students to the molecular components and physiological mechanisms that underlie the structure and function of cells.  The course is designed as an in-depth survey to cover general concepts central to the field of biochemistry and cell biology and to emphasize these concepts within the context of current scientific research questions and technical approaches.  Lectures will focus on recent discoveries in contemporary cell biology involving (i) basic cellular biochemistry; (ii) mechanisms of membrane transport and excitability; (iii) intracellular compartmentalization and protein/vesicle targeting, organelle biogenesis; (iv) cytoskeletal architecture, cell motility and adhesion; and (v) molecular mechanisms of signal transduction.  Efforts will be made to familiarize students with recent technical advances in molecular, biochemical, microscopic, spectroscopic, and electrophysiologic techniques.
  2. Either
    1. BIOM 555: Control of Gene Expression
      Regulation of gene expression including chromatin structure, transcription, DNA modification, RNA processing, translation, control of gene expression via microRNAs and post-translational processing.
    2. CAMB 550: Genetic Principles
      This is a combined lecture and discussion course that surveys major concepts and approaches used in experimental and human genetics. Discussions are problem-based and emphasize practical aspects of generating and interpreting genetic data.
  3. STAT 510: Probability and 512: Mathematical Statistics, or equivalent.  Students are expected to obtain graduate-level knowledge of probability and inference techniques.  Other statistics courses students often take include STAT 512: Mathematical Statistics, STAT 542: Bayesian Data Analysis.  Students with little statistical background may want to take BIOL 446: Statistics for Biologists, and BIOL 556: Advanced Statistics.
  4. At least one "core" computer science course (usually CIS 520: Machine Learning, CIS 550: Database and Information Systems, CIS 502: Analysis of Algorithms, or CIT 590 and 594: Programming Language and Tech).

Some example course choices are listed here.