Final Project: general information
Introduction
This page has general information about your “Final Project” for this course, which is an individual project on a topic of your choice. In it, you’re expected to apply many of the things you have learned during the course.
Across four checkpoints, your Final Project is worth 50 points (out of a total of 100 for the course). More information about each checkpoint will be shared as the course progresses, but here is a brief overview:
| Checkpoint | Due / when | Points |
|---|---|---|
| FP1: Proposal | Sun, Nov 15 | 10 |
| FP2: Progress report | Sun, Nov 29 | 10 |
| FP3: Oral presentation | In class, Dec 7 & 9 | 15 |
| FP4: Final submission | Sun, Dec 13 | 15 |
In the course intro slide deck, we said the Oral presentation and Final submission would be 10 and 20 points, respectively — but the numbers in the table above are the final ones.
Picking a topic
The topic and scope of the project are completely up to you. We recommend that you take advantage of this flexibility, so your Final Project will be as useful as possible for your own research and/or personal development. For example:
You may want to work with your own data or a publicly available dataset that is similar to data you expect to work with later.
You are also allowed to start working on a larger research project in your Final Project, even if you may not get far enough during the course to draw biological conclusions.
If you’re not sure what kind of data you’ll be working with in your research, you can simply pick something that seems of interest to you, and/or we could help you find something (see below).
Alternatively, you may prefer to focus on practicing the process that you’ve learned during this course (project structure, reproducibility, Slurm batch jobs, etc.). In that case, a small and simple dataset that isn’t necessarily relevant to your research is a perfectly fine choice.
We are happy to help you find a suitable topic and/or dataset. More generally, we highly recommend that you discuss your Final Project ideas with us before you settle on a topic — ideally by early November, well before the proposal is due on Nov 15. You can do so during office hours or by email.
Example projects
To give you an idea of the scope we have in mind, here are a few examples of suitable Final Projects:
- RNA-seq: check read quality and quantify gene expression with CLI tools, and test for differential expression in a Quarto document.
- Bacterial genomes: assemble the genomes of a handful of isolates, assess the quality of the assemblies, and summarize and plot the results in R.
- Amplicon sequencing (metabarcoding): process 16S or ITS reads with the nf-core/ampliseq pipeline and analyze community composition in R.
- Automating a lab or field data workflow (not omics): take the many raw output files from, for example, a plate reader, qPCR machine, or field sensors, use shell scripts to organize, check, and combine them, and summarize and plot the results in a Quarto report.
As a rough guide, a project with a few shell scripts (some of which you run as Slurm batch jobs) and one or two Quarto documents that take raw data all the way to a few summary tables and figures is a good size.
Your grade will mostly depend on the process you follow, not on the conclusions you draw from your data or on whether you used the “best” tools for the job — see the next section for more details.
Graded aspects
Graded aspects of your Final Project revolve around appropriate usage of the tools and principles we covered during the course. To receive a high grade, your project should:
Be well-organized: contained in a single parent directory with a clear and sensible structure of subdirectories, descriptive file and directory names, no files floating around with unclear purpose or source, and so on.
Be well-documented, with at least one README in Markdown format in the root directory of your project.
Be version-controlled with Git throughout: with regular, meaningful commits. Use a
.gitignorefile to keep large files like raw data and most results out of your repository. You will need to push to GitHub at least for the proposal, progress report, and final submission checkpoints.Contain both shell scripts and R scripts or Quarto documents that do data processing and/or analysis (we will cover R and Quarto in weeks 12–14). Avoid editing data or results files by hand (for example, fixing column names in Excel), since such steps aren’t recorded and can’t be rerun.
Be performed entirely at OSC, and include the running of one or more scripts as Slurm batch jobs.
Run external software (such as bioinformatics tools) using containers (preferred) or OSC modules.
Be easily re-runnable using a “protocol” in one of your Markdown files (we will cover such protocols in week 10). After your final submission, we will try to rerun your project as part of the grading process!
Academic integrity
You are allowed to use generative AI for your Final Project (we will cover genAI for coding in weeks 8 and 9), as long as you can explain what your code does. For example, if genAI comes up with commands or constructs we did not cover in the course, look into these until you understand what they do — or ask it to stick to things we did cover. You may be asked to orally explain your code at several points, similar to the oral examinations for the graded assignments.
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Use of generative AI Tools is permitted. |
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Open-book research for the assignment is permitted. |
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Getting help on the assignment is not permitted, except from the instructors. |
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Collaborating, or completing the assignment with others, is not permitted. |
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Copying or reusing previous work is not permitted, except as described below. |
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APA Citations and/or formatting for this assignment are not required. |
Using your own research data is encouraged. Discussing your project’s research questions, data, and analysis choices with your advisor or lab members is fine — but the code and documentation must be your own work. If you build on code that you (or your lab) wrote before this course, clearly indicate which files or parts were pre-existing: only the work you did for this course will be graded.