Programming

For this assignment, you will fill in the body of one function and call it. You may add as many variables as you need to solve the problems, but you do not need to write additional functions for this assignment. For all of our assignments you should NOT use parts of Python not yet discussed in class or the course readings.

Formatting¶

By the end of the assignment, dna_analysis.py must produce output of the exact form:

GC-content: ___
AT-content: ___
G count: ___
C count: ___
A count: ___
T count: ___
Sum of G+C+A+T counts: ___
Total count: ___
Length of nucleotides: ___
AT/GC Ratio: ___
GC Classification: ___
Is suitable for nanotech: ___

Problem 1: Remove Some Lines¶

Run dna_analysis.py in the terminal on test-small.fastq like you did for Problem 0. Be sure to take note of what output appears in the terminal. Then comment out the line gc_count = 0 by putting a # at the start of the line. Save the file and then run it again in the terminal.

In answers.txt, explain what happened, and why it happened.

Now, restore the line to its original state by removing the # that you added. What would happen if you commented out this line instead? nucleotides = filename_to_string(file_name)

Explain what happens and why in answers.txt.

Problem 2: Compute AT Content¶

Modify your program so that, in addition to computing and printing the GC ratio, it also computes and prints the AT content. The AT content is the percentage of nucleotides that are A or T.

There are two ways to compute the AT content:

• Copy the existing loop that examines each nucleotide and modify it. You will now have two loops, one of which computes the GC count and one of which computes the AT count.
• Add more statements into the existing loop, so that one loop computes both the GC count and the AT count.

You may use whichever approach you prefer. Check your work by manually computing the AT content for test-small.fastq, then comparing it to the output of running your program on test-small.fastq.

Run your program on sample_1.fastq. Copy and paste the relevant line of output into answers.txt.

Problem 3: Count Nucleotides¶

Modify your program so that it also computes and prints the number of A nucleotides, the number of T nucleotides, the number of G nucleotides, and the number of C nucleotides.

When doing this, add at most one extra loop to your program. You can solve this part without adding any new loops at all, by reusing an existing loop.

Check your work by manually computing the results for file test-small.fastq, then comparing them to the output of running your program on test-small.fastq.

Run your program on sample_1.fastq. Copy and paste the relevant lines of output into answers.txt (the lines that indicate the G count, C count, A count, and T count).

Problem 4: Sanity-Check the Data¶

Modify dna_analysis.py so that it will calculate and print the following variables:

• sum_counts: the sum of the A count, the C count, the G count, and the T count
• total_count: the total number of nucleotides
• len_nuc: the length of the nucleotides string variable using len(nucleotides).

Then run dna_analysis.py on each of the 11 .fastq files provided. As you run these files, you’ll notice that at least one of these quantities will be different from the other two for at least one .fastq file.

In answers.txt, state which .fastq file(s) and which quantities produce different results. Also write a short paragraph that explains why these differ.

If all the three quantities you measured are the same, then it would not matter which one you used in the denominator when computing the GC content. However, you saw that the three quantities are not all the same. In answers.txt, state which of these quantities should be used in the denominator and which should not, and why.

If your program incorrectly computed the GC content, which should be equal to

$$\frac{G+C}{A+C+G+T}$$

then state that fact in answers.txt. Go back and correct your program, **and also update any incorrect answers elsewhere in answers.txt. It is fine to change the code we provided you if needed.

Problem 5: Compute the AT/GC ratio¶

Sometimes biologists use the AT/GC ratio, defined as,

$$\frac{A+T}{G+C}$$

rather than the GC-content which is defined as,

$$\frac{G+C}{A+C+G+T}$$

Modify your program so that it also computes the AT/GC ratio. This should be calculated as: (A count + T count) / (G count + C count).

Check your work by manually computing the results for file test-small.fastq. Compare them to the output of running your program on test-small.fastq.

Run your program on sample_1.fastq. Copy and paste the relevant lines of output into answers.txt on the line that indicates the AT/GC ratio.

Problem 6: Categorize Organisms¶

The GC content can be used to categorize microorganisms. Fill in the body of the classify function to return the classification of the organism (“high”, “moderate” or “low”) described in the data file given using these classifications:

• If the GC content is above 58%, the organism is considered “high GC content”.
• If the GC content is below 35%, the organism is considered “low GC content”.
• Otherwise, (the GC content is between 35% and 58% inclusively), the organism is considered “moderate GC content”.

Biologists can use GC content for classifying species, for determining the melting temperature of the DNA (useful for both ecology and experimentation, for example PCR is more difficult on organisms with high GC content), identifying its suitability for DNA nanotechnology, and for other purposes. Here are some examples:

• The GC content of Streptomyces coelicolor A3(2) is 72%.
• The GC content of Yeast (Saccharomyces cerevisiae) is 38%.
• The GC content of Thale Cress (Arabidopsis thaliana) is 35%.
• The GC content of Plasmodium falciparum is 20%.

Again, test that your program works on some data files with known outputs. The test-small.fastq file has low GC content. We have provided four other test files, whose names explain their GC content: test-moderate-gc-1.fastq, test-moderate-gc-2.fastq, test-high-gc-1.fastq, test-high-gc-2.fastq.

You will find starter code for the classify() function near the top of dna_analysis.py, just before where the main program begins. There is an assignment statement inside the function body that is there only as a placeholder.

# Function to return GC Classification
def classify(gc_content):
    '''
    gc_content - a number representing the GC content
    Returns a string representing GC Classification. Must return one of
    these: "low", "moderate", or "high" based on the cutoffs in the spec
    '''

    # This statement is a placeholder. For Problem 8, replace it with your
    # code (will be more than one line) that sets classification to the
    # correct value based on gc_content. (And then delete this comment.)
    classification = "high"

    return classification

The function classify takes the gc_content as an input parameter and returns an appropriate string indicating the GC Classification. Once you have filled in the body of the classify function you should call the function from your main program in the appropriate place and use the string it returns to print out a message that matches what is expected. For example, be sure that your output for test-moderate-gc-1.fastq matches test-moderate-gc-1-expected.txt exactly using Diff Checker.

After your program works for all the test files, run it on sample_1.fastq. Copy and paste just the relevant line of output from your program into answers.txt (the line that indicates the GC classification).

Problem 6a: Nanotech¶

Additionally, modify the nano_suitable function to return whether or not the DNA strand is suitable for use in DNA technology. The decision should be made as follows:

• If the GC content is greater than 50% and less than 60%, nano_suitable should return True.
• Otherwise, nano_suitable should return false.

The starter code for the nano_suitable function looks like this:

def nano_suitable(gc_content):
    '''
    gc_content - a number representing the GC content
    Returns a boolean representing if the given GC Content is suitable for
    DNA nanotechnology
    '''
    return True

Like with the classify, the expected output is shown in the expected output files.

Quality¶

Your assignment should pass two checks: flake8 and our code quality guidelines. The code quality guidelines are very thorough. For this assignment, the most relevant rules can be found in these sections:

• Variable Names
• Whitespace
• Line Length
• Documentation
• Efficiency and Redundancy
  • Factoring

Collaboration¶

At the bottom of your answers.txt file, in the “Collaboration” part, state which students or other people (besides the course staff) helped you with the assignment, or that no one did. Please refer to the course syllabus for more on the course collaboration policy.

Submission¶

Submit dna_analysis.py and answers.txt on Gradescope under the assignment Homework 2.