Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a strain of coronavirus that causes COVID-19. Throughout these homework assignments, we will analyze this virus and try to understand its origins and inner workings. We will implement the necessary bioinformatics tools and apply them to further our understanding of this pesky little virus.
First, we will analyze the main component of every organism - its genetic material. Our focus will be on the genes, parts of the genetic material that code for proteins. Proteins are the main macromolecular actors in every organism.
Genes dictate the behavior of an organism, such as replication, viral assembly, and even innate immune evasion. If we compare the genes from this new virus with genes from other known viruses, we can get a good idea of how this virus works and maybe even how to stop it. When these genes are translated into proteins, they start acting out their function. Some proteins can attach to human cells and allow viruses to enter them. If we can figure out which genes these are, they will make good candidates for drug targets.
We can find potential genes in a genome by looking for common patterns shared across all genes. However, validating that these potential candidates are, in fact, real genes requires experimental confirmation.
Open homework-1.ipynb to get started.
We are conducting a study on the use of large language models (LLMs) to help grade homework assignments. Your homework will either be graded by an LLM or a human TA, chosen at random. You will not know which method was used. You may request a human review of any section of your graded homework after receiving your grades. Participation is purely voluntary, and opting out will not affect your grade in any way. For additional information, please contact the TAs via e-mail: pavlin.policar@fri.uni-lj.si and martin.spendl@fri.uni-lj.si.
If you do not wish to participate in this study, please uncheck the box below (remove the x inside the square brackets):
- I consent to participate in the LLM grading study.
Thank you for your contribution to this research!
To complete the homework, you need to commit and push your work, including all the code, notebook, and images to your GitHub repository. You do not need to submit anything else. We will open assignments on Ucilnica, but these are there only for your convenince, so the due dates are visible in your Ucilnica calendar.
For grading, we automatically generate a report for every student, so we never actually go through your code and notebooks. Our report generator automatically goes through your submission and pulls out the relevant bits.
There are three types of exercises/answers within each homework:
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Coding exercises require you to implement one of the algorithms you learned about in lectures. We will always include Python stubs for the functions you need to implement in a separate file (most often
helper_functions.py). Please read the function docstrings for expected parameter and return types. Coding problems will be automatically graded with unit tests.For some problem sets, we may provide unit tests, which you will be able to find in
test_helper_functions.py. These unit tests are meant only to serve as a guideline and are not exhaustive. It is good practice to write your own tests to convince yourself that your implementation works as intended. Your code will be graded on a different, more exhaustive set of unit tests. -
Image answers require you to generate an image and save the plot to a corresponding file in
<img>.png. Please ensure that your images have the exact same name as specified in the instructions and are stored in the root folder of the repository (the same folder as the README.md file). -
Variable answers require you to write down your answers into variables. Please be careful that the variables have the exact same name as in the instructions. We will always provide a stub in the notebook. We don't actually run your entire notebooks, we evaluate only the variable we are grading. This means that your variables should be set explicitly. For instance
x = 5 / 3oranswer_var = xwill not work and will receive zero points. All answer variables should be set explicitly e.g.answer_var = 1.6666in for this simple example. Please ensure there are no syntax errors in your notebook. We've added GitHub Actions that test syntax errors when you push your code to GitHub. If there is a green checkmark at the top of your repository, your code has no syntax errors. If there is a red X at the top, your notebook did not compile correctly, so make sure you eliminate any errors.
We will automatically fetch your solutions at the deadline and these submissions will be graded by default. If you submit after the deadline -- using your late days -- please notify Pavlin or Martin via email, and we will re-evaluate your submission.
You will need Python 3.10 or higher. You will need to install biopython for accessing NCBI and matplotlib for plotting. You will also need jupyterlab to open and run the notebook. You can also use numpy, pandas, and seaborn. You can install everything necessary by running
pip install biopython matplotlib jupyterlab
Please do not use any other libraries, because they will not be installed in the automatic grader environment and will fail, resulting in zero points. If you think some other library absolutely needs to be included, please reach out on Slack and we will discuss it there.
You can start the notebook by running
jupyter lab
and a browser window should pop open, or you can manually navigate to http://localhost:8888/ in your browser.
