## All Weeks Finding Hidden Messages in DNA (Bioinformatics I)

This course begins a series of classes illustrating the power of computing in modern biology. Please join us on the frontier of bioinformatics to look for hidden messages in DNA without ever needing to put on a lab coat.

In the first half of the course, we investigate DNA replication, and ask the question, wherein the genome does DNA replication begin? We will see that we can answer this question for many bacteria using only some straightforward algorithms to look for hidden messages in the genome.

### 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 *Skew*_{i }(CATTCCAGTACTTCGATGATGGCGTGAAGA) attains a minimum value.

**Comment Answer below if you know the answers**

Q4. Compute *Count*_{2}(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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