Probabilistic Sequence Analysis
	concepts to know
		the motif learning task
		EM algorithms
		Gibbs sampling
		the various motif models of MEME (OOPS, ZOOPS, TCM)
		tying parameters
		the CRM learning task
		semi-Markov models (generalized HMMs)
		dynamic programming with semi-Markov models
		mutual information
		using MI to identify interesting motifs (the FIRE approach)
		variable-order Markov models
		the MDD representation
		TWINSCAN paired sequence representation
		pair HMMs
		the relationship between pair HMMs and sequence alignment
		generalized pair HMMs
		sensitivity/recall, specificity, precision
	be able to do
		MEME algorithm
		interpolated Markov models
		back-off models
		design HMMs with specified duration models
		calculate sensitivity/recall, specificity, precision

Sequence Alignment
	concepts to know
		the genome-alignment task
		genome rearrangements
		suffix trees
		tries
		threaded tries
		maximal unique matches (MUMs)
		multi-MUMs
		longest increasing subsequence problem
		constrained dynamic programming
		recursive anchoring
		overview of MUMMER/LAGAN/MLAGAN/Mauve algorithms
		breakpoint graph in Mercator
	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

RNA structure modeling
        concepts to know
                RNA secondary structure
		the secondary structure prediction task
                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
		using SCFGs for structure/sequence alignments
		using SCFGs to predict novel RNA genes
		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

Network Models
	concepts to know
		Bayesian networks
		representing CPDs using tables, linear Gaussian models, trees
		module networks
		using EM to learn module networks
		selecting experiments in the Robot Scientist
		flux balance analysis

Biomedical Text Processing
	concepts to know
		the vector space model
		the ARROWSMITH system
		using EM to find themes in literature
		the experiment annotation task
		the named-entity recognition task
		the relation-extraction task
		conditional random fields