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# Learning Objectives - Understand HMM concepts for sequence analysis - Apply HMMs to profile hidden patterns - Use Viterbi and Forward algorithms ## 1. HMM Concepts A statistical model where the system is a Markov process with unobserved (hidden) states.
Learning Objectives
- Understand HMM concepts for sequence analysis
- Apply HMMs to profile hidden patterns
- Use Viterbi and Forward algorithms
1. HMM Concepts
A statistical model where the system is a Markov process with unobserved (hidden) states. Each state emits observations with certain probabilities.
Components: States, Transition probabilities, Emission probabilities, Initial probabilities.
2. Profile HMMs for Protein Families
Represent a family of related sequences with:
- Match states: Conserved positions in alignment
- Insert states: Regions where sequences can insert extra residues
- Delete states: Silent states for gaps Used for: protein family classification (Pfam), gene prediction, multiple sequence alignment.
3. Key HMM Algorithms
Forward Algorithm: P(sequence | model) - sum over all possible paths. Used for scoring sequences against models.
Viterbi Algorithm: Most likely path (state sequence) through HMM. Used for predicting domain boundaries, gene structure.
Baum-Welch Algorithm (EM): Learn HMM parameters from training sequences. Used for building profile HMMs from unaligned data.
Q1: What is the difference between a Markov chain and an HMM?Markov chain: states are observed. HMM: states are hidden, only emissions are observed. In sequence analysis: the state (e.g., coding/non-coding) is hidden, the DNA sequence (emission) is observed. Q2: What is the Viterbi algorithm used for in bioinformatics?Find the most likely sequence of hidden states (e.g., gene structure: exon, intron, intergenic) given the observed DNA sequence. Also used for protein domain detection. Q3: How do profile HMMs represent insertions and deletions?Match states (M): conserved aligned positions with emission probabilities. Insert states (I): emit extra residues. Delete states (D): emit nothing (silent) for gaps. This captures sequence variation better than consensus. Q4: What is the Forward algorithm used for?Calculates total probability of observed sequence given model (summing over all possible paths). Used for classifying sequences: does this sequence belong to this protein family? Q5: What is the Baum-Welch algorithm?Expectation-Maximization (EM) algorithm for learning HMM parameters from unlabeled training sequences. Iteratively estimates transition and emission probabilities from data. Join Discord PreviousBLAST & Database SearchingNextPhylogenetics