All Weeks Finding Hidden Messages in DNA (Bioinformatics I)
Finding Hidden Messages in DNA (Bioinformatics I) Week 01 Quiz Answers
Q1. True or False: The Finding oriC Problem is a well-defined computational problem.
- True
- False
Q2. Compute Count(ACTGTACGATGATGTGTGTCAAAG, TGT).
- Comment Answer below if you know the answers
Q3. What is the most frequent 3-mer of CGCCTAAATAGCCTCGCGGAGCCTTATGTCATACTCGTCCT?
- Comment Answer below if you know the answers
Q4. What is the reverse complement of TTGTGTC?
- Comment Answer below if you know the answers
Q5. Solve the Pattern Matching Problem with Text = ATGACTTCGCTGTTACGCGC and Pattern = CGC to find all starting positions of Pattern in Text. Return the starting positions in increasing order (make sure to use 0-based indexing!)
Enter your answer as a collection of space-separated integers. (e.g., 4 7 14)
- Comment Answer below if you know the answers
Finding Hidden Messages in DNA (Bioinformatics I) Week 02 Quiz Answers
Q1. True or False: Deamination is more likely to occur when DNA is single-stranded than when it is double-stranded.
- True
- False
Q2. Compute the Hamming distance between CTACAGCAATACGATCATATGCGGATCCGCAGTGGCCGGTAGACACACGT and
CTACCCCGCTGCTCAATGACCGGGACTAAAGAGGCGAAGATTATGGTGTG.
- Comment Answer below if you know the answers
Q3. Identify the value of i for which Skewi (CATTCCAGTACTTCGATGATGGCGTGAAGA) attains a minimum value.
- Comment Answer below if you know the answers
Q4. Compute Count2(CATGCCATTCGCATTGTCCCAGTGA, CCC).
- Comment Answer below if you know the answers
Q5. The d-neighborhood of the k-mer Pattern is the collection of all k-mers that are at most Hamming distance d from Pattern.
How many 10-mers are in the 1-neighborhood of Pattern = CCAGTCAATG?
Note that the d-neighborhood of Pattern includes Pattern.
- Comment Answer below if you know the answers
Finding Hidden Messages in DNA (Bioinformatics I) Week 03 Quiz Answers
Q1. Which type of algorithm selects the most attractive choice at each step?
- Dynamic programming algorithm
- Greedy algorithm
- Randomized algorithm
- Brute force search
- Combinatorial algorithm
- Machine learning algorithm
Q2. True or false: a motif of lowest score with respect to a collection of strings does not need to appear as a substring of one of the strings.
- True
- False
Q3. Order the following probability distributions from lowest to highest entropy:
A: (0.5, 0, 0, 0.5)
B: (0.25, 0.25, 0.25, 0.25)
C: (0, 0, 0, 1)
D: (0.25, 0, 0.5, 0.25)
- C, A, D, B
- D, C, A, B
- B, C, A, D
- B, A, D, C
- C, A, B, D
Q4. Consider the following profile matrix:
- A: 0.4 0.3 0.0 0.1 0.0 0.9
- C: 0.2 0.3 0.0 0.4 0.0 0.1
- G: 0.1 0.3 1.0 0.1 0.5 0.0
- T: 0.3 0.1 0.0 0.4 0.5 0.0
Q4. Which of the following strings is a consensus string for this profile matrix? (Select all that apply.)
- TCGCGA
- ACGCGA
- ACGTTA
- AGGTCA
- AAGCTA
- AAGAGA
Q5. Consider the following motif matrix:
CTCGATGAGTAGGAAAGTAGTTTCACTGGGCGAACCACCCCGGCGCTAATCCTAGTGCCC
GCAATCCTACCCGAGGCCACATATCAGTAGGAACTAGAACCACCACGGGTGGCTAGTTTC
GGTGTTGAACCACGGGGTTAGTTTCATCTATTGTAGGAATCGGCTTCAAATCCTACACAG
Which of the following 7-mers is a median string for this motif matrix? (Select all that apply.)
- AACGCTG
- ATAACGG
- GAACCAC
- GGTTACT
- AATCCTA
- CGTGTAA
Q6. Consider the following profile matrix Profile:
- A: 0.4 0.3 0.0 0.1 0.0 0.9
- C: 0.2 0.3 0.0 0.4 0.0 0.1
- G: 0.1 0.3 1.0 0.1 0.5 0.0
- T: 0.3 0.1 0.0 0.4 0.5 0.0
Compute Pr(TCGGTA|Profile). (Express your answer as a decimal and do not round your answer.)
Finding Hidden Messages in DNA (Bioinformatics I) Week 04 Quiz Answers
Q1. True or False: RandomizedMotifSearch performs well when given a uniform profile matrix.
- True
- False
Q2. True or False: RandomizedMotifSearch and GibbsSampler are usually run on many choices of initial k-mers.
- True
- False
Q3. True or False: it is possible for GibbsSampler to move from a collection of motifs with lower score to a collection of motifs with higher score.
- True
- False
Q4. Which of the following motif-finding algorithms is guaranteed to find an optimum solution? In other words, which of the following are not heuristics? (Select all that apply.)
- RandomizedMotifSearch
- GreedyMotifSearch (without pseudocounts)
- GreedyMotifSearch (with pseudocounts)
- MedianString
Q5. Assume we are given the following strings Dna:
TGACGTTC
TAAGAGTT
GGACGAAA
CTGTTCGC
Then, assume that RandomizedMotifSearch begins by randomly choosing the following 3-mers Motifs of Dna:
- TGA
- GTT
- GAA
- TGT
What are the 3-mers after one iteration of RandomizedMotifSearch? In other words, what are the 3-mers Motifs(Profile(Motifs), Dna)? Please enter your answer as four space-separated strings.
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