Reproducible Research in Bioinformatics and Computational Biology
Course description
Digitalization of agriculture requires upskilling and confidence-building for students coming from field or wet-lab work. In this course the students will be taught good working habits for data analyses in general and bioinformatics in particular. They will learn about data management and FAIR (Findable, Accessible, Interoperable, Reusable) practices, use a range of basic to advanced reproducible data analyses tools, and gain confidence in integrating computational approaches into their biological research. Tentative maximum enrollment of 30 students. The course is run jointly with the MedBioInfo graduate school (www.medbioinfo.se). VMAS students start with a 1-week introductory module. The second week is for an assignment – at-home for MedBioInfo, on location for VMAS. The third week is an advanced module, which is on-location for both cohorts.
Course facts
P000187, Reproducible Research in Bioinformatics and Computational Biology, 4.5 Hp
Print syllabus
Syllabus
Level
Third cycle
Subject
Bioinformatics
Grading Scale
One-grade scale
The grade requirements within the course grading system are set out in specific criteria. These criteria must be available by the course start at the latest.
Course language
English
Entry Requirements
Objectives
On completion of the course, the student will be able to:
- Understand the FAIR principles. Explain what reproducibility means in scientific research and why it is essential. Apply the principles to biological datasets. Evaluate their own analysis workflow in these terms.
- Identify suitable public databases for accessing and storing given types of biological data.
- Use tools in R to enhance reproducible research. Apply basic principles of reproducible analysis in R, including organizing code and results.
- Understand Unix philosophy. Be able to use navigate its file system, install and update software, log on to and use remote servers.
- Use VSCode or other development environments. Be able to write simple programs for scientific computing.
- Set up and maintain environments using tools like Pixi and Apptainer
- Understand and use Git to collaboratively develop, document, maintain, and distribute code.
- Understand and use common pipelining tools such as Nextflow and Nf-core. Design and run workflow pipelines.
- Apply the tools and principles to their own data
Content
In Module 1:
Students will learn the philosophy of R, as well as commonly used functions, how to structure code, and how to import libraries. R markdown, making R packages, functions.
Students will learn the FAIR principles in detail.
Students will learn the principle and practice of version control with Git, including git in R studio. They will learn to parallelize R projects.
Students will learn to use the Linux command line. They will be able to navigate the file system, log on to remote servers, and use basic commands. They will also learn to use NAISS High Performance Computing resources.
Students will be introduced to pipelining as a means of packaging their data and methods.
Module 2 will cover:
Version control tools. Students will learn the philosophy of version control with Git as an example. They will demonstrate understanding of basic concepts with a quiz. They will install Git on their own laptops. We will hold an interactive session in which they will collaborate on a single document which will be turned into a coherent whole.
They will create a Quarto blog, publish with github actions
Students will use Nextflow and nf-core to pipeline common bioinformatics tools as is done in bioinformatics publications to ensure reproducibility.
Students will learn about containers such as Docker and Singularity. They will learn hands-on how to use them to package their code and data for reuse under the FAIR principles.
Students will learn visualization using R and ggplot.
Final assignment is a Quarto blog explaining their Reproducible Research learning journey, version controlled using Git.
Students will review the FAIR principles in brief, and discuss how to organize computational projects.
Examination Formats and Requirements for Passing the Course
Examination will be by quizzes and written assignments. Evaluation will be based on theoretical understanding, technical<br> performance, and ability to interpret results.
Responsible Department/Equivalent
Department of Animal Biosciences
Supplementary information
Other Information
The course is proposed to run jointly with MedBioInfo. The course merges Reproducible Research course (geared towards VH PhD students) as Module 1, with the MedBioInfo Applied Bioinformatics course (geared towards Bioinformatics PhD students) as Module 2.
Module 1 (to be offered only through GS-VMAS) improves the students’ computational fluency, to the point that they will be able to complete the coursework of Module 2 alongside MedBioInfo students.
As students from all over Sweden will be in attendance, we intend to run a short excursion, e.g. to Lövsta or SciLifeLab. We also typically hold a course dinner for students to socialize.