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 informationy
		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

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

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

Representation, Learning and Inference in Models of Cellular Networks
	concepts to know
		Bayesian networks
		representing CPDs using tables, linear Gaussian models, trees
		module networks
		the module network learning procedure
		physical network models
		selecting experiments in the Robot Scientist
		flux balance analysis

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

Biomedical Text Mining
	concepts to know
		the named-entity recognition (NER) task
		the relation-extraction task
		the event-extraction task
		sources of evidence for NER and relation extraction
		the dictionary based approach to NER
		the rule-based approach to NER
		the conditional random field representation
		the rule based approach to relation extraction

Genotype Analysis
	concepts to know
		SNPs and CNVs
		genome-wide association study
		QTL mapping