Motif and cis-Regulatory Module (CRM) Modeling
	concepts to know
		the motif learning task
		position weight (profile) matrices
		EM algorithms
		Gibbs sampling
		the OOPs and ZOOPS models of MEME
		tying parameters
		the CRM learning task
		the structure search problem
		beam search
		sensitivity/recall, specificity, precision
		duration modeling
		semi-Markov models (generalized HMMs)
		dynamic programming with semi-Markov models
		entropy and mutual information
		using MI to identify interesting motifs (the FIRE approach)
	be able to do
		MEME algorithm
		Gibbs sampling for motif finding
		calculate sensitivity/recall, specificity, precision
		design HMMs with specified duration models

Gene Finding
	concepts to know
		the gene finding task
		interpolated Markov models
		the MDD representation
		TWINSCAN paired sequence representation
		pair HMMs
		the relationship between pair HMMs and sequence alignment
		generalized pair HMMs
	be able to do
		interpolated Markov models

RNA-Seq
    concepts to know
        how RNA-Seq data are generated
        the transcript quantification task given RNA-Seq data
        how the EM algorithm applies to the transcript quantification task
        alternative splicing
        probabilistic splice graphs
        identifiability of statistical models

RNA Analysis
    concepts to know
        RNA secondary structure
		the secondary structure prediction task
		using dynamic programming to predict RNA secondary structure
        how Nussinov can be generalized to do energy minimization
		pseudoknots and why they are a problem
        transformational grammars
        probabilistic grammars
        the Chomsky hierarchy
		ambiguity in a grammar
        why CFGs are appropriate for RNA modeling
        what the Inside, CYK and Inside/Outside algorithms do
		space and time complexity of Inside, CYK, Inside/Outside
		RIBOSUM matrices
		using a SCFG to find a sequence matching a given structure
    be able to do
        the Nussinov algorithm
        show parse trees for a sequence with a given grammar
        Inside algorithm
		Inside/Outside algorithm

Large-Scale and Whole-Genome Sequence Alignment
	concepts to know
		the large-scale alignment task
		the general strategy of large-scale aligners
		suffix trees
		tries
		threaded tries
		maximal unique matches (MUMs)
		multi-MUMs
		longest increasing subsequence problem
		constrained dynamic programming
		genome rearrangements
		progressive alignment
		recursive anchoring
		overview of MUMMER/LAGAN/MLAGAN/Mauve algorithms
		breakpoint graph in Mercator
		using undirected graphical models in Mercator to identify breakpoints
	be able to do
		show suffix trees for a given (set of) string(s)
		show trie/threaded trie for strings
		calculate MUMs and mult-MUMs

Comparative Network Analysis
	concepts to know
		classes of biological networks
		statistical properties of biological networks
		techniques for randomizing graphs
		the conserved module detection task
		Sharan et al. probabilistic model for conserved module detection
		Sharan et al. algorithm for conserved module detection


Protein Structure Prediction
	concepts to know
		the protein structure prediction problem
		levels of protein structure (primary, secondary, etc.)
		the homology modeling approach
		the threading approach
		how predictions are made
		how threadings are calculated
		branch-and-bound search
	be able to do
	   	branch-and-bound threading

Genotype Analysis
	concepts to know
		SNPs and CNVs
		genome-wide association study
		QTL mapping
		single-SNP statistical tests for association
		multiple testing correction
		bonferroni correction
		false discovery rate
		benjamini-hochberg FDR procedure
	be able to do
		bonferroni correction on a set of p-values
		benjamini-hochberg FDR calculation for a set of p-values