Figure from the original question paper

(A) Option figure from the original question paper

(B) Option figure from the original question paper

(C) Option figure from the original question paper

(D) Option figure from the original question paper

Given below are three algorithms for the motif finding problem. Identify which figure belongs to which algorithm. ████████ randomly select k-mers Motifs = (Motif_1, ..., Motif_t) in each string from Dna BestMotifs ← Motifs for j ← 1 to N i ← RANDOM(t) Profile ← profile matrix formed from all strings in Motifs except for Motif_i Motif_i ← Profile-randomly generated k-mer in the i-th sequence if SCORE(Motifs) < SCORE(BestMotifs) BestMotifs ← Motifs return BestMotifs (a) Figure I ████████ BestMotifs ← motif matrix formed by first k-mers in each string from Dna for each k-mer Motif in the first string from Dna Motif_1 ← Motif for i = 2 to t form Profile from motifs Motif_1, ..., Motif_{i-1} Motif_i ← Profile-most probable k-mer in the i-th string in Dna Motifs ← (Motif_1, ..., Motif_t) if SCORE(Motifs) < SCORE(BestMotifs) BestMotifs ← Motifs return BestMotifs (b) Figure II ████████ randomly select k-mers Motifs = (Motif_1, ..., Motif_t) in each string from Dna BestMotifs ← Motifs while forever Profile ← PROFILE(Motifs) Motifs ← MOTIFS(Profile, Dna) if SCORE(Motifs) < SCORE(BestMotifs) BestMotifs ← Motifs else return BestMotifs (c) Figure III

(A) Figure I: Gibbs Sampling; Figure II: Randomized Motif Search; Figure III: Greedy Motif Search

(B) Figure I: Gibbs Sampling; Figure II: Greedy Motif Search; Figure III: Randomized Motif Search

(C) Figure I: Greedy Motif Search; Figure II: Randomized Motif Search; Figure III: Gibbs Sampling

(D) Figure I: Randomized Motif Search; Figure II: Gibbs Sampling; Figure III: Greedy Motif Search

Algorithmic Thinking in Bioinformatics Quiz 2: 16 March 2025 (January 2025 term)

Question 1

+2 marksOne correct option

Consider a univariate Gaussian Mixture Model (GMM) with 3 components. Suppose for component 2, you are given the following data points and corresponding responsibilities. Which of the following options correctly represents the updated mean $\mu_2$ ?

$x_i$	$r_{i,2}$
3	0.1
7	0.7
10	0.6
5	0.4
8	0.8

Question 2

+3 marksOne correct option

Question 3

+5 marksOne correct option

Given below are three algorithms for the motif finding problem. Identify which figure belongs to which algorithm.

text

1████████
2randomly select k-mers Motifs = (Motif_1, ..., Motif_t) in each string from Dna
3BestMotifs ← Motifs
4for j ← 1 to N
5  i ← RANDOM(t)
6  Profile ← profile matrix formed from all strings in Motifs except for Motif_i
7  Motif_i ← Profile-randomly generated k-mer in the i-th sequence
8  if SCORE(Motifs) < SCORE(BestMotifs)
9    BestMotifs ← Motifs
10return BestMotifs

(a) Figure I

text

1████████
2BestMotifs ← motif matrix formed by first k-mers in each string from Dna
3for each k-mer Motif in the first string from Dna
4  Motif_1 ← Motif
5  for i = 2 to t
6    form Profile from motifs Motif_1, ..., Motif_{i-1}
7    Motif_i ← Profile-most probable k-mer in the i-th string in Dna
8  Motifs ← (Motif_1, ..., Motif_t)
9  if SCORE(Motifs) < SCORE(BestMotifs)
10    BestMotifs ← Motifs
11return BestMotifs

(b) Figure II

text

1████████
2randomly select k-mers Motifs = (Motif_1, ..., Motif_t) in each string from Dna
3BestMotifs ← Motifs
4while forever
5  Profile ← PROFILE(Motifs)
6  Motifs ← MOTIFS(Profile, Dna)
7  if SCORE(Motifs) < SCORE(BestMotifs)
8    BestMotifs ← Motifs
9  else
10    return BestMotifs

A
Figure I: Gibbs Sampling; Figure II: Randomized Motif Search; Figure III: Greedy Motif Search
B
Figure I: Gibbs Sampling; Figure II: Greedy Motif Search; Figure III: Randomized Motif Search
C
Figure I: Greedy Motif Search; Figure II: Randomized Motif Search; Figure III: Gibbs Sampling
D
Figure I: Randomized Motif Search; Figure II: Gibbs Sampling; Figure III: Greedy Motif Search

Also asked in Quiz 2 3 Aug 2025

14 more questions in this paper

Sign in with Google — it is free — to see every question with its answer and explanation, practise it in learning mode, or take it as a timed mock test.

More on the Algorithmic Thinking in Bioinformatics Quiz 2 16 Mar 2025 paper

The IIT Madras BS Algorithmic Thinking in Bioinformatics (Algorithmic Thinking in Bioinformatics) Quiz 2 paper sat on 16 Mar 2025, in the January 2025 term: 17 questions for 50 marks in 120 minutes. The first 3 questions are below. Sign in with Google — it is free — to see the whole paper with its answers and explanations, in learning mode or as a timed mock test.

Feature	Algorithmic Thinking in Bioinformatics Quiz 2 16 Mar 2025 at a glance
Term	January 2025 term
Subject	Algorithmic Thinking in Bioinformatics
Course code	BSBT4001
Questions	17
Marks	50
Duration	120 min
MCQ	8
Numerical	5
MSQ	4
Official paper	IIT M DEGREE AN EXAM QDB2 16 Mar 2025
Negative marking	No negative marking.
Updated	25 Sept 2026

Algorithmic Thinking in Bioinformatics Quiz 2: 16 March 2025 (January 2025 term)

Question 1

Question 2

Question 3

14 more questions in this paper

More on the Algorithmic Thinking in Bioinformatics Quiz 2 16 Mar 2025 paper

Same Quiz 2, other subjects

More Algorithmic Thinking in Bioinformatics