Question

Given below are two statements: Assertion (A): Hidden Markov Model outperforms simple motif searches for gene prediction. Reason (R): Hidden Markov Model incorporates state transitions and probabilistic modelling.

• A. Both (A) and (R) are correct and (R) is the correct explanation of (A)
• B. Both (A) and (R) are correct but (R) is not the correct explanation of (A)
• C. (A) is correct but (R) is not correct
• D. (A) is not correct but (R) is correct

Hint:

HMMs are powerful for gene prediction because they model biological sequences as probabilistic transitions between hidden states.

Answer 1

The Correct Option is A

Concept:
Hidden Markov Models are probabilistic models used for sequence analysis. In bioinformatics, they are useful for gene prediction, protein family detection, and sequence annotation.

Step 1: Check Assertion (A).
Simple motif searches look only for fixed patterns. Gene prediction requires modelling coding regions, non-coding regions, start sites, splice sites, and transitions among states. HMMs can handle these complex patterns better. \[ A \text{ is correct} \]

Step 2: Check Reason (R).
HMMs use hidden states, transition probabilities, and emission probabilities. This allows them to model biological sequences statistically. \[ R \text{ is correct} \]

Step 3: Check explanation.
Because HMMs include state transitions and probabilistic modelling, they can outperform simple motif searches in gene prediction. \[ \therefore \text{Correct Answer is (A)} \]