2009 Exam Dates:
Written: 5/21/09 and 5/22/09
Review paper due: 5/29/09
The goal of the GCB Candidacy Exam is to ensure that students engaged in dissertation research have the appropriate fundamental knowledge covering the basic areas relevant to Genomics and Computational Biology and to assess the students’ skills in critical reading and synthesis of primary literature. The Exam is administered by the GCB Prelim Committee whose composition is adjusted annually. The committee will determine the test material and the content of the test with input from the GCB faculty. The exams will consist of two parts, scheduled within three weeks of the finals week in the Spring semester. The detailed format of the tests and their timing will be determined by the prelim committee and communicated to the students prior to the preparation period.
The first part will consist of a written exam covering the basic knowledge set. A list of topics to be emphasized is provided below. This list is not considered complete, but it is expected that the questions will concentrate on these topics and related material. It is expected that the students will have knowledge equivalent to passing a graduate level class covering these topics. The second part of the exam will consist of reading a set of primary literature specified by the prelim committee and submitting a short paper that synthesizes and critiques the material.
Sample Topics
These are the general areas that students should be prepared to address in the written exam. Courses in parentheses indicate the level at which you should know the material.
I. Computation and Statistics (primarily CIT 592/594 and BIO 446)
A. Computer Science (CIT 592/594; some from BIO 536)
a. Basic data structures (lists, stacks, queues, trees, hash tables)
b. Basic complexity analysis (growth function, NP and NP-complete)
c. Basic database queries and propositional logic
d. Recursion and mathematical induction
e. Basic Algorithms (sorting, Minimum Spanning Trees, Shortest Paths, Graph Traversal, Numerical Optimization)
f. Algorithm Design Principles (Divide and Conquer, Greedy, Approximation and Heuristics, Dynamic Programming)
B. Computational Biology (Bio/GCB 536; GCB 537; GCB 535)
a. Algorithms used in bioinformatics (BIO 536):
b. Basic string matching
c. Sequence alignment
d. Probabilistic String Generative Models (HMMs, stochastic context free grammar)
e. BLAST and other DB search algorithms
f. Phylogeny construction
g. Machine Learning for bioinformatics (Clustering, Support Vector Machines, Neural Nets)
h. Markov Chains
C. Statistics (BIO 446; some from BIO 556)
a. Basic probability (random variables, expectation, variance, correlation/covariance)
b. Conditional probability
c. Commonly used distributions (normal, exponential, gamma, beta, binomial, multinomial, poisson)
d. Frameworks of statistical inference (Maximum Likelihood Estimation, Bayesian Estimation)
e. Data Analysis
i. Confidence intervals
ii. Hypothesis testing
iii. Linear and logistic Models
iv. Multivariate Analysis (PCA, Multi-dimensional scaling, etc)
v. Classification
f. Computational Methods
i. maximization algorithms (grid search, Newton-Raphson, EM)
ii. simulation algorithms (Grid sampling, Gibbs sampling, Metropolis- Hastings algorithm)
g. Matrices and matrix operations (BIO 556), basic multivariate calculus
II. Genetics/Genomics (primarily GCB 531 and CAMB 550)
A. Molecular Genetics, Biochemistry and Cell Biology (BIOL 527/582; some from BIO 480/CELL 600)
a. Nucleic acids: structure and function
b. Proteins: structure, domain, reactions
c. Molecular basis of gene expression, translation, and regulation
d. Subcellular organelles: structure and functions
e. Signal transduction principles
f. Biochemical pathways
B. The Structure and Transmission of Genetic Information (CAMB 550)
a. Chromatin and chromosome orgranization
b. Meiosis and mitosis
c. Genetic pathway and analysis/epistasis
C. Genetic Variation and Mapping (GCB 531 or CAMB 550)
a. Polymorphic markers
b. Heterozygosity
c. Meiosis, crossing over, recombination, and genetic maps
d. Quantitative Trait Loci
e. "Forward" and "reverse" genetics
f. Linkage in pedigrees, LOD score, Linkage disequilibrium (LD)
g. Haplotype blocks
h. Mapping by LD
D. DNA Sequencing, Genome Projects and Comparative Sequence Analysis (GCB 531)
a. Sequence analysis and databases
b. Genome sequencing and assembly strategies
c. Experimental organisms and human
E. Functional Genomics (GCB 531)
a. Genome-wide gene expression technology and analysis.
b. Basic methods and principles of proteomics
c. High-throughput screens
F. Molecular Evolution of Genomes (GCB 531 or BIO 536/CIS 535)
a. Evolutionary processes (mutation, drift, natural selection)
b. Neutral theory of evolution
c. Comparative genomics (multiple-species sequence comparison, functional inference, gene family evolution)
d. Phylogeny reconstruction