At the end of the second year, students are required to take the Candidacy Exam (formerly the Prelim Exam), given by an ad hoc committee of three to five faculty members drawn from multiple disciplines within GCB. This exam determines whether a student has attained a satisfactory degree of scientific knowledge as well as a sufficient independence of thought to enter candidacy for the PhD degree. It is expected that the students will have knowledge equivalent to passing a graduate level class covering topics in the biological sciences, computer science, and statistics. The exam consists of a written exam that focuses on both the experimental and computational portions of the curriculum, as well as a written literature review on a specified topic. The literature review portion of the exam now takes place within GCB 752.
At the end of May, when all the scores are in, the faculty advisors of the students taking the exam meet with the Exam Committee to review the test scores, as well as class and rotation grades, and make the final recommendation for the student’s evaluation. The student may receive the following evaluations: “Pass,” “Qualified Pass,” or “Fail.” A “Qualified Pass” signifies that the student must remedy some deficiency in background. This may entail, for example, taking an additional course, or conducting an independent research project. Students who fail part or all of the exam by a small margin may be re-examined on the appropriate section(s). Students who do not pass the exam on the second occasion face dismissal from the program after review by the Prelim Committee.
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.
These are the general areas that students should be prepared to address in the written exam.
I. Computation and Statistics
A. Computer Science
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
a. Algorithms used in bioinformatics
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
a. Probability distributions for discrete and continuous random variables.
b. Means, general expectations and variances, conditional expectations, Bayes' rule.
c. Hypothesis testing and confidence intervals for means and proportions.
d. Multiple testing corrections.
e. Linear regression, logistic regression, ANOVA, chi-square goodness-of-fit tests.
f . Permutation tests and simple simulation strategies.
g. Bootstrap methods.
h. Simple clustering techniques (k-means, hierarchical, etc).
i. Introductory stochastic processes, in particular Markov chains and Poisson processes.
A. Molecular Genetics, Biochemistry and Cell Biology
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
a. Chromatin and chromosome orgranization
b. Meiosis and mitosis
c. Genetic pathway and analysis/epistasis
C. Genetic Variation and Mapping
a. Polymorphic markers
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
a. Sequence analysis and databases
b. Genome sequencing and assembly strategies
c. Experimental organisms and human
E. Functional Genomics
a. Genome-wide gene expression technology and analysis.
b. Basic methods and principles of proteomics
c. High-throughput screens
F. Molecular Evolution of Genomes
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