Graded Assignment 2:
Unix shell data tools and version control

Week 4 graded assignment – due Sunday Sep 21 at 11:59 pm

Author
Affiliation

Jelmer Poelstra

CFAES Bioinformatics Core, Ohio State University

Published

September 12, 2025

Overview

This graded assignment is worth 10 points and is due on Sunday Sep 21st. You will practice with Unix shell commands, especially the “data tools” commands, and with Git commands.

Directions and grading

Submission expectations

  • Deadline: Sunday September 21st at 11:59 pm.
  • Submission: You don’t need to submit anything or notify the instructors: we will simply look for these files in the appropriate location at OSC. (We will also be able to see when the files were last modified.)
  • When to start: We recommend that you only get started with this assignment “for real” after Thursday’s class. Many of the questions revolve around commands you’ll have learned by Tuesday, so you could do a partial practice run after that lecture.

Academic integrity

Use of generative AI Tools (e.g. ChatGPT, Microsoft Copilot, Google Gemini) is not permitted.
Getting help on the assignment is not permitted
Collaborating, or completing the assignment with others, is not permitted
Copying or reusing previous work is not permitted
Open-book research for the assignment is permitted
APA Citations and/or formatting for this assignment are not required

Rubric

You can earn a maximum of 0.5 points for each of the numbered 20 questions/items below. There is a bonus 21st question that can earn you 1 point if you dropped points elsewhere.

Detailed steps

General

  • Work in VS Code at OSC as per usual.

  • Use Unix shell and Git commands to answer all questions unless explicitly instructed not to do so.

  • In the Markdown file with your answers (see below), copy each of the numbered instructions/questions into your document, and insert your answers in between. The code parts of your answers should be entered in Markdown code blocks, and that code’s output should also be part of your answer. For example:

    1. List the files in your current working dir

   I used the following command:

   ```bash
   ls
   ```

   The output was:

   ```
   file1.txt   file2.txt   annot.gtf
   ```

Setting up

  • Create a new dir for this assignment, /fs/ess/PAS2880/users/$USER/GA2, and navigate there. This will be your working dir for the entire assignment. Inside it, also create a dir data and a dir results.

  • In your working dir, create a README.md file and open it in the VS Code editor. Use this file throughout the assigment to add your answers.

Part A: Starting a Git repository

  1. Add a header to your README file to indicate that this file (and dir) is for your assignment.

  2. Initialize a Git repository inside your new directory.

  3. Add & stage and then commit the README file with an appropriate commit message.

Part B: Exploring the GTF file with Unix data tools

  1. Copy the genome annotation GTF file GCF_016801865.2.gtf.gz that is in your copy of the Garrigós et al. (2025) data to a file data/annot.gtf.gz (i.e., into the data dir you just created).

  2. Check the file size of annot.gtf.gz. Then, decompress the GTF file with the gunzip command below. Finally, check the size of the resulting annot.gtf file. How large is the size difference between the compressed and uncompressed files?

    gunzip data/annot.gtf.gz

  3. The total number of “genomic features” (genes, exons, etc.) listed in a GTF file is simply the number of lines in its main/table section.

    • Count the total number of lines in annot.gtf.
    • Take a look at the contents of the file and count the number of header lines by eye.
    • Based on your knowledge of what the GTF header lines look like, combine grep -v and wc -l to count the number of genomic features in the file.

  4. Does the difference between the two line counts you just performed correspond to the number of header lines you counted visually? If not, can you figure out which other line(s) than the header lines contain the pattern you used with grep above?

    Click here for a hint

    Review the “Additional grep tips” box in the section on grep to find an option that might help you make sense of that.

  5. How many distinct “sequences” (chromosome/scaffolds/contigs) are represented in the annotation file? Store a list of these distinct sequences in a file results/scaffolds.txt.

    Click here for a hint

    In this and all following questions, you’ll only want to consider the table part of the file, so you should start your commands with grep -v as above.

  6. Print a “count table” (like we did towards the end of this section) of feature types in the annotation: this should have a count of the number of times each feature type occurs. (Such a table will for example show how many genes have been annotated in this genome – quite useful!)

  7. Check the status of your Git repository. You should see that your README has changed but also that Git has detected the files in the data and results dirs. You don’t want to commit any files in the data and results dirs, so create a .gitignore file that makes Git ignore them. Check the status of your repository again – is your .gitignore file working as intended?

  8. Stage and commit the .gitignore file.

  9. Stage and commit the README.md file again.

Part C: Exploring the FASTQ files with Unix data tools

  1. Copy the FASTQ file ERR10802863_R1.fastq.gz of the Garrigós et al. (2025) data to a file of the same name inside the same data dir you copied the GTF file into.

  2. Check the status of your Git repository. Does the FASTQ file show up? Why/why not?

  3. How many reads does the FASTQ file contain?

    Click here for hints

    Recall from last week’s exercises that a direct linecount of the compressed file will not give you the correct answer.

    Also, don’t forget to convert the number of lines to the number of reads.

  4. How many reads in the FASTQ file contain the sequence ACGT? (You can assume this string does not occur outside of the lines with sequences.)

  5. How many reads contain at least 10 consecutive Ns (uncalled bases)? (You can assume that stretches of N’s do not occur in the sequence quality lines.)

  6. Using the read length information in the FASTQ read header lines, print a count table of read lengths.

    Click here for hints

    • First, select only the read header lines. When doing so, you can assume that @s do not occur in other lines.

    • Next, extract the read length numbers in these header lines, which are in the format length=74. One way to do this is with a fun trick: by using = as the delimiter to cut (-d =)!

  7. Compare the number you got in the previous question with the number of 35-bp reads from the question before that. What does this seem to tell you? To confirm, can you check what a sample of 35-bp reads look like?

    Click here for hints

    To see the sequences in the 35-bp reads, use a grep option that allows you to see the line after each match along with the matching line. Also, pipe grep’s output to less or head to avoid having thousands of lines printed to screen.

  8. Stage and commit the README.md file again.

Bonus

  1. Name at least two concepts or commands that we have gone over in the course so far, but which you feel you do not (fully) understand. It helps to be specific about what you struggle with.
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References

Garrigós, Marta, Guillem Ylla, Josué Martínez-de la Puente, Jordi Figuerola, and María José Ruiz-López. 2025. “Two Avian Plasmodium Species Trigger Different Transcriptional Responses on Their Vector Culex pipiens.” Molecular Ecology 34 (15): e17240. https://doi.org/10.1111/mec.17240.