Hello Peers, Today we are going to share all week’s assessment and quizzes answers of the Algorithms for DNA Sequencing course launched by Coursera totally free of cost✅✅✅. This is a certification course for every interested student.
In case you didn’t find this course for free, then you can apply for financial ads to get this course for totally free.
Check out this article – “How to Apply for Financial Ads?”
About The Coursera
Coursera, India’s biggest learning platform launched millions of free courses for students daily. These courses are from various recognized universities, where industry experts and professors teach in a very well manner and in a more understandable way.
Here, you will find Algorithms for DNA Sequencing Exam Answers in Bold Color which are given below.
These answers are updated recently and are 100% correct✅ answers of all week, assessment, and final exam answers of Algorithms for DNA Sequencing from Coursera Free Certification Course.
Use “Ctrl+F” To Find Any Questions Answer. & For Mobile User, You Just Need To Click On Three dots In Your Browser & You Will Get A “Find” Option There. Use These Option to Get Any Random Questions Answer.
About Algorithms for DNA Sequencing Course
We will learn computational tools — algorithms and data structures — for evaluating DNA sequencing data. We will learn a little about DNA, genomics, and how DNA sequencing is employed. We will use Python to create important algorithms and data structures and to analyze real genomes, algorithms for dna sequencing github and DNA sequencing datasets.
SKILLS YOU WILL GAIN
- Bioinformatics Algorithms
- Algorithms
- Python Programming
- Algorithms On Strings
- selection algorithms
- dna sequence classification machine learning
Course Apply Link – Algorithms for DNA Sequencing
Algorithms for DNA Sequencing Quiz Answers
Week 1
Quiz 1: Module 1
Q1. Which of the following is not a suffix of CATATTAC?
- CAT
- TATTAC
- TAC
- C
Q2. What’s the longest prefix of CACACTGCACAC that is also a suffix?
- CACAC
- C
- CACACTG
- CAC
Q3. Which of the following is not a substring of GCTCAGCGGGGCA?
- GCC
- GCT
- GCA
- GCG
Q4. Starting around 2007, the cost of DNA sequencing started to decrease rapidly because more laboratories started to use:
- Sanger sequencing
- Double sequencing
- Second-generation sequencing
- DNA microarrays
Q5. Which of the following pieces of information is not included in a sequencing read in the FASTQ format:
- The sequence of base qualities corresponding to the bases
- A “name” for the read
- The sequence of bases that make up the read
- Which chromosome the read originated from
Q6. If read alignment is like “looking for a needle in a haystack,” then the “haystack” is the:
- Sequencing read
- Gene database
- Reference genome
- Sequencer
Q7. The Human Genome Project built the initial “draft” sequence of the human genome, starting from sequencing reads. The computational problem they had to solve was the:
- prime factorization problem
- de novo shutgun assembly problem
- gene finding problem
- read alignment problem
Q8. If the length of the pattern is x and the length of the text is y, the minimum possible number of character comparisons performed by the naive exact matching algorithm is:
- y – x + 1
- xy
- x + y
- x(y – x + 1)
Q9. If the length of the pattern is x and the length of the text is y, the maximum possible number of character comparisons performed by the naive exact matching algorithm is:
- x + y
- xy
- y – x + 1
- x(y – x + 1)
Q10. Say we have a function that generates a random DNA string, i.e. the kind of string we would get by rolling a 4-sided die (A/C/G/T) over and over. We use the function to generate a random pattern P of length 20 and a random text T of length 100. Now we run the naive exact matching algorithm to find matches of P within T. We expect the total number of character comparisons we perform to be closer to the…
- maximum possible
- minimum possible
Quiz 2: Programming Homework 1
Q1. How many times does \verb|AGGT|AGGT or its reverse complement (\verb|ACCT|ACCT) occur in the lambda virus genome? E.g. if \verb|AGGT|AGGT occurs 10 times and \verb|ACCT|ACCT occurs 12 times, you should report 22.
Enter answer here
Q2. How many times does \verb|TTAA|TTAA or its reverse complement occur in the lambda virus genome?
Hint: \verb|TTAA|TTAA and its reverse complement are equal, so remember not to double count.
Enter answer here
Q3. What is the offset of the leftmost occurrence of \verb|ACTAAGT|ACTAAGT or its reverse complement in the Lambda virus genome? E.g. if the leftmost occurrence of \verb|ACTAAGT|ACTAAGT is at offset 40 (0-based) and the leftmost occurrence of its reverse complement \verb|ACTTAGT|ACTTAGT is at offset 29, then report 29.
Enter answer here
Q4. What is the offset of the leftmost occurrence of \verb|AGTCGA|AGTCGA or its reverse complement in the Lambda virus genome?
Enter answer here
Q5. As we will discuss, sometimes we would like to find approximate matches for P in T. That is, we want to find occurrences with one or more differences.
For Questions 5 and 6, make a new version of the \verb|naive|naive function called \verb|naive_2mm|naive_2mm that allows up to 2 mismatches per occurrence. Unlike for the previous questions, do not consider the reverse complement here. We’re looking for approximate matches for P itself, not its reverse complement.
For example, \verb|ACTTTA|ACTTTA occurs twice in \verb|ACTTACTTGATAAAGT|ACTTACTTGATAAAGT, once at offset 0 with 2 mismatches, and once at offset 4 with 1 mismatch. So \verb|naive_2mm(‘ACTTTA’, ‘ACTTACTTGATAAAGT’)|naive_2mm(’ACTTTA’, ’ACTTACTTGATAAAGT’) should return the list \verb|[0, 4]|[0, 4].
Hint: See this notebook for a few examples you can use to test your \verb|naive_2mm|naive_2mm function.
How many times does \verb|TTCAAGCC|TTCAAGCC occur in the Lambda virus genome when allowing up to 2 mismatches?
Enter answer here
Q6. What is the offset of the leftmost occurrence of \verb|AGGAGGTT|AGGAGGTT in the Lambda virus genome when allowing up to 2 mismatches?
Enter answer here
Q7. Finally, download and parse the provided FASTQ file containing real DNA sequencing reads derived from a human:
Note that the file has many reads in it and you should examine all of them together when answering this question. The reads are taken from this study:
Ajay, S. S., Parker, S. C., Abaan, H. O., Fajardo, K. V. F., & Margulies, E. H. (2011). Accurate
and comprehensive sequencing of personal genomes. Genome research, 21(9), 1498-1505.
This dataset has something wrong with it; one of the sequencing cycles is poor quality.
Report which sequencing cycle has the problem. Remember that a sequencing cycle corresponds to a particular offset in all the reads. For example, if the leftmost read position seems to have a problem consistently across reads, report 0. If the fourth position from the left has the problem, report 3. Do whatever analysis you think is needed to identify the bad cycle. It might help to review the “Analyzing reads by position” video.
Enter answer here
Week 2
Quiz 1: Module 2
Q1. Boyer-Moore: How many alignments are skipped by the bad character rule for this alignment?
Note: the number of skips is one less than the number of positions P shifts by. That is, if the pattern shifts by 2 positions, that’s 1 alignment skipped.
Also note: the question is asking only about the alignment shown. Do not consider any other alignments of P to T in your answer.
T: GGCTATAATGCGTAP: TAATAAA
Enter answer here
Q2. Boyer-Moore: How many alignments are skipped by the good suffix rule in this scenario?
T: GGCTATAATGCGTAP: TAATTAA
Enter answer here
Q3. Boyer-Moore, true or false: for given P and T, it’s possible that some characters from T will never be examined, i.e., won’t be involved in any character comparisons.
- False
- True
Q4. Consider a version of Boyer-Moore that uses only the bad character rule (no good suffix rule), and say our pattern P is a random string of 50% As and 50% Ts. In which scenario would you expect Boyer-Moore to skip the most alignments?
- The text T consists of 40% As, 40% Ts, 10% Cs and 10%Gs
- The text T consists of 25% As, 25% Ts, 25% Cs and 25%Gs
- The text T consists of 10% As, 10% Ts, 40% Cs and 40%Gs
Q5. The naive exact matching algorithm preprocesses:
- The text T
- Neither
- Both
- The pattern P
Q6. The Boyer-Moore algorithm preprocesses:
- The pattern P
- Neither
- The text T
- Both
Q7. In which of the these scenarios is an offline matching algorithm not appropriate?
- A tool that evaluates a password by comparing it against a large database of bad (easy-to-guess) passwords
- Your web browser’s “find” function that allows you to find a particular word on the web page
- you are currently viewing
- A tool that searches for words in an archive of every speech made in the U.S. Congress
Q8. Say we have a k-mer index containing all 5-mers from T. We query the index using the first 5-mer from P and the index returns a single index hit. What can we say about whether P occurs in T? Assume T is longer than P and that P is at least 6 bases long.
- It definitely does
- It definitely does not
- We don’t know; not enough information
Q9. Say we have a k-mer index containing all k-mers from T and we query it with 3 different k-mers from the pattern P. The first query returns 0 hits, the second returns 1 hit, and the third returns 3 hits. What can we say about whether P occurs in T?
- It definitely does
- It definitely does not
- We don’t know; not enough information
Q10. Which of the following is not an “edit” allowed in edit distance:
- Transposition
- Deletion
- Substitution
- Insertion
Quiz 2: Programming Homework 2
Q1. How many alignments does the naive exact matching algorithm try when matching the string \verb|GGCGCGGTGGCTCACGCCTGTAATCCCAGCACTTTGGGAGGCCGAGG|GGCGCGGTGGCTCACGCCTGTAATCCCAGCACTTTGGGAGGCCGAGG (derived from human Alu sequences) to the excerpt of human chromosome 1? (Don’t consider reverse complements.)
Enter answer here
Q2. How many character comparisons does the naive exact matching algorithm try when matching the string \verb|GGCGCGGTGGCTCACGCCTGTAATCCCAGCACTTTGGGAGGCCGAGG|GGCGCGGTGGCTCACGCCTGTAATCCCAGCACTTTGGGAGGCCGAGG (derived from human Alu sequences) to the excerpt of human chromosome 1? (Don’t consider reverse complements.)
Enter answer here
Q3. How many alignments does Boyer-Moore try when matching the string \verb|GGCGCGGTGGCTCACGCCTGTAATCCCAGCACTTTGGGAGGCCGAGG|GGCGCGGTGGCTCACGCCTGTAATCCCAGCACTTTGGGAGGCCGAGG (derived from human Alu sequences) to the excerpt of human chromosome 1? (Don’t consider reverse complements.)
Enter answer here
Q4.Index-assisted approximate matching. In practicals, we built a Python class called \verb|Index|Index
implementing an ordered-list version of the k-mer index. The \verb|Index|Index class is copied below.
class Index(object):
def init(self, t, k):
”’ Create index from all substrings of size ‘length’ ”’
self.k = k # k-mer length (k)
self.index = []
for i in range(len(t) – k + 1): # for each k-mer
self.index.append((t[i:i+k], i)) # add (k-mer, offset) pair
self.index.sort() # alphabetize by k-mer
def query(self, p):
We also implemented the pigeonhole principle using Boyer-Moore as our exact matching algorithm.
Implement the pigeonhole principle using \verb|Index|Index to find exact matches for the partitions. Assume P always has length 24, and that we are looking for approximate matches with up to 2 mismatches (substitutions). We will use an 8-mer index.
Download the Python module for building a k-mer index.
Write a function that, given a length-24 pattern P and given an \verb|Index|Index object built on 8-mers, finds all approximate occurrences of P within T with up to 2 mismatches. Insertions and deletions are not allowed. Don’t consider any reverse complements.
How many times does the string \verb|GGCGCGGTGGCTCACGCCTGTAAT|GGCGCGGTGGCTCACGCCTGTAAT, which is derived from a human Alu sequence, occur with up to 2 substitutions in the excerpt of human chromosome 1? (Don’t consider reverse complements here.)
Hint 1: Multiple index hits might direct you to the same match multiple times, but be careful not to count a match more than once.
Hint 2: You can check your work by comparing the output of your new function to that of the \verb|naive_2mm|naive_2mm function implemented in the previous module.
Enter answer here
Q5. Using the instructions given in Question 4, how many total index hits are there when searching for occurrences of \verb|GGCGCGGTGGCTCACGCCTGTAAT|GGCGCGGTGGCTCACGCCTGTAAT with up to 2 substitutions in the excerpt of human chromosome 1?
(Don’t consider reverse complements.)
Hint: You should be able to use the \verb|boyer_moore|boyer_moore function (or the slower \verb|naive|naive function) to double-check your answer.
Enter answer here
Q6. Let’s examine whether there is a benefit to using an index built using subsequences of T rather than substrings, as we discussed in the “Variations on k-mer indexes” video. We’ll consider subsequences involving every N characters. For example, if we split \verb|ATATAT|ATATAT into two substring partitions, we would get partitions \verb|ATA|ATA (the first half) and \verb|TAT|TAT (second half). But if we split \verb|ATATAT|ATATAT into two subsequences by taking every other character, we would get \verb|AAA|AAA (first, third and fifth characters) and \verb|TTT|TTT (second, fourth and sixth).
Another way to visualize this is using numbers to show how each character of P is allocated to a partition. Splitting a length-6 pattern into two substrings could be represented as \verb|111222|111222, and splitting into two subsequences of every other character could be represented as \verb|121212|121212
The following class \verb|SubseqIndex|SubseqIndex is a more general implementation of \verb|Index|Index that additionally handles subsequences. It only considers subsequences that take every Nth character:
import bisect
class SubseqIndex(object):
“”” Holds a subsequence index for a text T “””
def __init__(self, t, k, ival): """ Create index from all subsequences consisting of k characters spaced ival positions apart. E.g., SubseqIndex("ATAT", 2, 2) extracts ("AA", 0) and ("TT", 1). """ self.k = k # num characters per subsequence extracted
For example, if we do:
ind = SubseqIndex(‘ATATAT’, 3, 2)
print(ind.index)
we see:
[(‘AAA’, 0), (‘TTT’, 1)]
And if we query this index:
p = ‘TTATAT’
print(ind.query(p[0:]))
we see:
12
[]
because the subsequence \verb|TAA|TAA is not in the index. But if we query with the second subsequence:
print(ind.query(p[1:]))
we see:
1
[1]
because the second subsequence \verb|TTT|TTT is in the index.
Write a function that, given a length-24 pattern P and given a \verb|SubseqIndex|SubseqIndex object built with k = 8 and ival = 3, finds all approximate occurrences of P within T with up to 2 mismatches.
When using this function, how many total index hits are there when searching for \verb|GGCGCGGTGGCTCACGCCTGTAAT|GGCGCGGTGGCTCACGCCTGTAAT with up to 2 substitutions in the excerpt of human chromosome 1? (Again, don’t consider reverse complements.)
Hint: See this notebook for a few examples you can use to test your function.
Enter answer here
Week 3
Quiz 1: Module 3
Q1. The value in each edit-distance matrix element depends on its neighbors:
- Above, to the left, and to the right
- To the upper-left, to the left and to the lower-left
- To the left and to the lower-left
- Above, to the left, and to the upper-left
Q2. Say we have filled in the approximate matching matrix and identified the minimum value (say, 2) in the bottom row. Now we would like to know the shape of the corresponding 2-edit alignment, i.e. we would like to know where the insertions, deletions and substitutions are. We use a procedure called:
- Filling
- Binary search
- Pathing
- Traceback
Q3. Say the edit distance between DNA strings α and β is 407. What is the edit distance between α and β\verb|G|G (β concatenated with the base \verb|G|G)
- could be any of the other choices
- 406
- 407
- 408
Q4. Say we are using dynamic programming to find approximate occurrences of P in T. About how many dynamic programming matrix elements do we have to fill in?
- |P| |T|
- |P| + |T|
- |T|^2 (squared)
- |P|^2 (squared)
Q5. Local alignment is different from global alignment because:
- It finds similarities between substrings rather than between entire strings
- There is no dynamic programming algorithm for solving it
- It compares three strings instead of two
- Insertions and deletions incur no penalty
Q6. The first law of assembly says that if a prefix of read A is similar to a suffix of read B, then…
- A and B might overlap in the genome
- A and B must be from different genomes
- Read B might have a sequencing error at the end
- A and B should not be joined in the final assembly
Q7. The second law of assembly says that more coverage leads to…
- less accurate results
- more and longer overlaps between reads
- more sequencing errors
Q8. In an overlap graph, the nodes of the graph correspond to
- Bases
- Genomes
- Overlaps
- Reads
Q9. The overlap graph is a useful structure because:
- It makes it faster to compare reads
- A reconstruction of the genome corresponds to a path through the graph
- It helps to ignore long overlaps
Q10. Which of the following is not a reason why an overlap might contain sequence differences (i.e. might not be an exact match):
- Insufficient coverage
- Polyploidy
- Sequencing error
Quiz 2: Programming Homework 3
Q1. What is the edit distance of the best match between pattern GCTGATCGATCGTACG|GCTGATCGATCGTACG and the excerpt of human chromosome 1? (Don’t consider reverse complements.)
Enter answer here
Q2. What is the edit distance of the best match between pattern GATTTACCAGATTGAG|GATTTACCAGATTGAG and the excerpt of human chromosome 1? (Don’t consider reverse complements.)
Enter answer here
Q3. In a practical, we saw a function for finding the longest exact overlap (suffix/prefix match) between two strings. The function is copied below.
def overlap(a, b, min_length=3):
“”” Return length of longest suffix of ‘a’ matching
a prefix of ‘b’ that is at least ‘min_length’
characters long. If no such overlap exists,
return 0. “””
start = 0 # start all the way at the left
while True:
start = a.find(b[:min_length], start) # look for b’s prefix in a
if start == -1: # no more occurrences to right
return 0
Say we are concerned only with overlaps that (a) are exact matches (no differences allowed), and (b) are at least \verb|k|k bases long. To make an overlap graph, we could call \verb|overlap(a, b, min_length=k)|overlap(a, b, min_length=k) on every possible pair of reads from the dataset. Unfortunately, that will be very slow!
Consider this: Say we are using k=6, and we have a read \verb|a|a whose length-6 suffix is \verb|GTCCTA|GTCCTA. Say \verb|GTCCTA|GTCCTA does not occur in any other read in the dataset. In other words, the 6-mer \verb|GTCCTA|GTCCTA occurs at the end of read \verb|a|a and nowhere else. It follows that \verb|a|a’s suffix cannot possibly overlap the prefix of any other read by 6 or more characters.
Put another way, if we want to find the overlaps involving a suffix of read \verb|a|a and a prefix of some other read, we can ignore any reads that don’t contain the length-k suffix of \verb|a|a. This is good news because it can save us a lot of work!
Here is a suggestion for how to implement this idea. You don’t have to do it this way, but this might help you. Let every k-mer in the dataset have an associated Python \verb|set|set object, which starts out empty. We use a Python dictionary to associate each k-mer with its corresponding \verb|set|set. (1) For every k-mer in a read, we add the read to the \verb|set|set object corresponding to that k-mer. If our read is \verb|GATTA|GATTA and k=3, we would add \verb|GATTA|GATTA to the \verb|set|set objects for \verb|GAT|GAT, \verb|ATT|ATT and \verb|TTA|TTA. We do this for every read so that, at the end, each \verb|set|set contains all reads containing the corresponding k-mer. (2) Now, for each read \verb|a|a, we find all overlaps involving a suffix of \verb|a|a. To do this, we take \verb|a|a’s length-k suffix, find all reads containing that k-mer (obtained from the corresponding \verb|set|set) and call \verb|overlap(a, b, min_length=k)|overlap(a, b, min_length=k) for each.
The most important point is that we do not call \verb|overlap(a, b, min_length=k)|overlap(a, b, min_length=k) if \verb|b|b does not contain the length-k suffix of \verb|a|a.
Download and parse the read sequences from the provided Phi-X FASTQ file. We’ll just use their base sequences, so you can ignore read names and base qualities. Also, no two reads in the FASTQ have the same sequence of bases. This makes things simpler.
Next, find all pairs of reads with an exact suffix/prefix match of length at least 30. Don’t overlap a read with itself; if a read has a suffix/prefix match to itself, ignore that match. Ignore reverse complements.
Hint 1: Your function should not take much more than 15 seconds to run on this 10,000-read dataset, and maybe much less than that. (Our solution takes about 3 seconds.) If your function is much slower, there is a problem somewhere.
Hint 2: Remember not to overlap a read with itself. If you do, your answers will be too high.
Hint 3: You can test your implementation by making up small examples, then checking that (a) your implementation runs quickly, and (b) you get the same answer as if you had simply called \verb|overlap(a, b, min_length=k)|overlap(a, b, min_length=k) on every pair of reads. We also have provided a couple examples you can check against.
Picture the overlap graph corresponding to the overlaps just calculated. How many edges are in the graph? In other words, how many distinct pairs of reads overlap?
Enter answer here
Q4. Picture the overlap graph corresponding to the overlaps computed for the previous question. How many nodes in this graph have at least one outgoing edge? (In other words, how many reads have a suffix involved in an overlap?)
Enter answer here
Week 4
Quiz 1: Module 4
Q1. The slow (sometimes called “brute force”) algorithm for finding the shortest common superstring of the strings in set S involves:
- Iteratively removing strings from S that don’t belong in the superstring
- Trying all orderings of the strings in S
- Concatenating the strings in of S
- Finding the longest common substring of the strings in S
Q2. Which of the following is not a true statement about the slow (brute force) shortest common superstring algorithm.
- It might collapse repetitive portions of the genome
- The superstring returned might be longer than the shortest possible one
- The amount of time it takes grows with the factorial of the number of input strings
Q3. Which of the following is not a true statement about the greedy shortest common superstring formulation of the assembly problem?
- The amount of time it takes grows with the factorial of the number of input strings
- It might collapse repetitive portions of the genome
- The superstring returned might be longer than the shortest possible one
Q4. True or false: an Eulerian walk is a way of moving through a graph such that each node is visited exactly once
- False
- True
Q5. If the genome is repetitive and we try to use the De Bruijn Graph/Eulerian Path method for assembling it, we might find that:
- There is more than one Eulerian path
- The genome “spelled out” along the Eulerian path is not a superstring of the reads
- The De Bruijn graph breaks into pieces
Q6. In a De Bruijn assembly graph for given k, there is one edge per
- read
- k-mer
- k-1-mer
- genome
Q7. Which of the following does not help with the problem of assembling repetitive genomes:
- Paired-end reads
- Longer reads
- Increasing minimum required overlap length for the overlap graph
Quiz 2: Programming Homework 4
Q1. In a practical, we saw the \verb|scs|scs function (copied below along with \verb|overlap|overlap) for finding the shortest common superstring of a set of strings.
def overlap(a, b, min_length=3): """ Return length of longest suffix of 'a' matching a prefix of 'b' that is at least 'min_length' characters long. If no such overlap exists, return 0. """ start = 0 # start all the way at the left while True: start = a.find(b[:min_length], start) # look for b's suffx in a if start == -1: # no more occurrences to right return 0 # found occurrence; check for full suffix/prefix match if b.startswith(a[start:]): return len(a)-start start += 1 # move just past previous match import itertools def scs(ss): """ Returns shortest common superstring of given strings, which must be the same length """ shortest_sup = None for ssperm in itertools.permutations(ss): sup = ssperm[0] # superstring starts as first string for i in range(len(ss)-1): # overlap adjacent strings A and B in the permutation olen = overlap(ssperm[i], ssperm[i+1], min_length=1) # add non-overlapping portion of B to superstring sup += ssperm[i+1][olen:] if shortest_sup is None or len(sup) < len(shortest_sup): shortest_sup = sup # found shorter superstring return shortest_sup # return shortest
It’s possible for there to be multiple different shortest common superstrings for the same set of input strings. Consider the input strings \verb|ABC|ABC, \verb|BCA|BCA, \verb|CAB|CAB. One shortest common superstring is \verb|ABCAB|ABCAB but another is \verb|BCABC|BCABC and another is \verb|CABCA|CABCA.
What is the length of the shortest common superstring of the following strings?
CCT, CTT, TGC, TGG, GAT, |ATT
Enter answer here
Q2. How many different shortest common superstrings are there for the input strings given in the previous question?
Hint 1: You can modify the \verb|scs|scs function to keep track of this.
Hint 2: You can look at these examples to double-check that your modified \verb|scs|scs is working as expected.
Enter answer here
Q3. Download this FASTQ file containing synthetic sequencing reads from a mystery virus:
All the reads are the same length (100 bases) and are exact copies of substrings from the forward strand of the virus genome. You don’t have to worry about sequencing errors, ploidy, or reads coming from the reverse strand.
Assemble these reads using one of the approaches discussed, such as greedy shortest common superstring. Since there are many reads, you might consider ways to make the algorithm faster, such as the one discussed in the programming assignment in the previous module.
How many As are there in the full, assembled genome?
Hint: the virus genome you are assembling is exactly 15,894 bases long
Enter answer here
Q4. How many Ts are there in the full, assembled genome from the previous question?
Enter answer here
Conclusion
Hopefully, this article will be useful for you to find all the Week, final assessment, and Peer Graded Assessment Answers of Algorithms for DNA Sequencing Quiz of Coursera and grab some premium knowledge with less effort. If this article really helped you in any way then make sure to share it with your friends on social media and let them also know about this amazing training. You can also check out our other course Answers. So, be with us guys we will share a lot more free courses and their exam/quiz solutions also, and follow our Techno-RJ Blog for more updates.
Great ?V I should certainly pronounce, impressed with your site. I had no trouble navigating through all tabs and related information ended up being truly simple to do to access. I recently found what I hoped for before you know it in the least. Reasonably unusual. Is likely to appreciate it for those who add forums or something, web site theme . a tones way for your client to communicate. Nice task..
You could definitely see your expertise in the work you write. The arena hopes for even more passionate writers like you who are not afraid to mention how they believe. At all times follow your heart.
Way cool, some valid points! I appreciate you making this article available, the rest of the site is also high quality. Have a fun.
It is appropriate time to make a few plans for the future and it’s time to be happy. I have read this put up and if I may I want to suggest you some interesting things or tips. Perhaps you could write next articles relating to this article. I want to read even more issues about it!
I truly appreciate this post. I’ve been looking all over for this! Thank goodness I found it on Bing. You’ve made my day! Thank you again!
Merely wanna remark on few general things, The website design is perfect, the content is really wonderful : D.
Magnificent website. A lot of useful information here. I am sending it to some pals ans also sharing in delicious. And certainly, thanks for your sweat!
Hi my friend! I want to say that this post is amazing, great written and come with approximately all significant infos. I?¦d like to peer extra posts like this .
What¦s Going down i am new to this, I stumbled upon this I’ve discovered It absolutely helpful and it has helped me out loads. I hope to contribute & assist other customers like its helped me. Great job.
You are a very intelligent person!
I went over this web site and I conceive you have a lot of excellent info, saved to favorites (:.
I like what you guys are up also. Such smart work and reporting! Carry on the excellent works guys I have incorporated you guys to my blogroll. I think it will improve the value of my website :).
You are a very smart person!
Howdy very cool web site!! Guy .. Excellent .. Amazing .. I will bookmark your blog and take the feeds additionally…I’m glad to seek out numerous useful info here within the submit, we need work out more techniques in this regard, thanks for sharing. . . . . .
Thank you for sharing excellent informations. Your web site is so cool. I’m impressed by the details that you have on this blog. It reveals how nicely you perceive this subject. Bookmarked this web page, will come back for more articles. You, my friend, ROCK! I found simply the info I already searched everywhere and simply could not come across. What a great web site.
I’ve recently started a web site, the information you provide on this site has helped me greatly. Thanks for all of your time & work. “My dear and old country, here we are once again together faced with a heavy trial.” by Charles De Gaulle.
Hi there, I found your blog via Google while looking for a related topic, your website came up, it looks great. I have bookmarked it in my google bookmarks.
You could certainly see your skills in the work you write. The world hopes for even more passionate writers like you who are not afraid to say how they believe. Always follow your heart.
I do agree with all the ideas you’ve presented in your post. They are very convincing and will certainly work. Still, the posts are very short for novices. Could you please extend them a bit from next time? Thanks for the post.
cialis 5mg sale otc cialis pills for erection
Hiya very nice website!! Man .. Excellent .. Superb .. I’ll bookmark your web site and take the feeds also…I’m happy to seek out so many helpful information here in the publish, we need develop more techniques in this regard, thanks for sharing. . . . . .
duricef 250mg ca order duricef generic finasteride over the counter
order diflucan pill fluconazole 100mg generic cipro 500mg drug
order flagyl 400mg online cheap cephalexin 125mg us buy keflex pills
order cleocin 300mg online cheap generic fildena sildenafil over the counter
buy tamoxifen 10mg sale buy tamoxifen pill buy cefuroxime 250mg sale
careprost allergy nasal spray buy careprost pills trazodone order
suhagra over the counter sildenafil 200mg for sale buy cheap generic sildalis
I am a website designer. Recently, I am designing a website template about gate.io. The boss’s requirements are very strange, which makes me very difficult. I have consulted many websites, and later I discovered your blog, which is the style I hope to need. thank you very much. Would you allow me to use your blog style as a reference? thank you!
buy azipro 250mg generic purchase neurontin for sale buy gabapentin 100mg pill
brand furosemide buy ventolin for sale buy cheap ventolin
order levitra without prescription order vardenafil 10mg cheap hydroxychloroquine
You really make it appear so easy together with your presentation however I find this topic to be really one thing that I think I would never understand. It sort of feels too complicated and extremely broad for me. I’m looking forward on your subsequent put up, I’ll try to get the hang of it!
buy levitra medication order tizanidine 2mg pill plaquenil online order
order asacol 800mg without prescription cost astelin 10ml buy irbesartan 150mg online
buy olmesartan 10mg pill buy verapamil 120mg without prescription buy divalproex 250mg pill
order carvedilol 25mg without prescription order generic coreg 25mg chloroquine ca
oral acetazolamide 250mg order acetazolamide 250 mg online cheap azathioprine online order
Thank you for sharing with us, I conceive this website truly stands out : D.
You have noted very interesting details ! ps nice web site.
order digoxin 250 mg telmisartan 80mg us molnunat 200 mg brand
naproxen 250mg pills order cefdinir 300mg pill prevacid pills
buy baricitinib for sale order olumiant generic purchase lipitor online
Some genuinely great info , Glad I observed this.
buy albuterol 100mcg online buy albuterol 100mcg order phenazopyridine 200mg