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Learning Objectives

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Learning Objectives

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Now · 1. HMM Concepts

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