Tools in Molecular Data Analysis

Course manual 2022/2023

Course content

This course aims to develop practical skills in data handling, analysis and visualization for large “omics” data commonly found in contemporary plant research.

Two domain-specific projects will be addressed: i) RNA-seq analysis, and ii) Microbiome analysis. For each project, after introductory lectures on the topic, students will be trained to handle these specific datasets. Afterwards, they will have to process a dataset by themselves and build a result report to describe their biological conclusions from the experiment. They will receive feedback on their approach, including their R coding practices and the biological interpretation of their results.

During this course, importance of both experimental design and parameter choice will be discussed. The students will have to perform a data analysis in the most reproducible manner and visualize data to convey a biological answer to a research question.

Study materials

Literature

• Microbiome data analysis: https://www.nature.com/articles/nature11237

• Microbiome data analysis: https://www.nature.com/articles/s41579-018-0029-9

• Gene expression data analysis (RNA-seq): https://www.nature.com/articles/s41576-019-0150-2

Syllabus

• All documents will be made available in Canvas

Practical training material

• All documents will be made available in Canvas

Software

• Windows users need to install Git for Windows: https://gitforwindows.org

Objectives

• Students can describe and explain the experimental set-up of OMICS experiments such as transcriptomics, metagenomics analysis
• Students can implement practical skills in data handling, analysis and visualization for large OMICS data
• Students are able to examine RNA-seq and microbiome data analyses
• Students are able to link data analyses outcomes to biological interpretation

Teaching methods

• Lecture
• Computer lab session/practical training
• Self-study
• Presentation/symposium
• Working independently on e.g. a project or thesis

First, lectures will explain the general concepts and give an overview of the omics data analysis process. Then, practicals will help the students to learn in detail how omics data are analysed and then apply what they just learnt through small exercises. During these practical sessions, students will be able to check if they can apply what they have learnt and discuss with the others students and the teacher(s). Finally, students will work in groups of two on projects where they apply what they have learnt and analyse a new omics data set, describe and discuss their results.

Learning activities

Attendance

Requirements of the programme concerning attendance (OER-B):

1. Attendance during practical components exercises is mandatory.

Assessment

- two assignments: final grade is the weighted average of the two assignments. Both grades need to be 5.5 or higher.

- students need to be present for both assignments

- for the assignements students can work together, but assignments are submitted individually

- each student uploads an individual assignment on Canvas

- if an individual assessments  is graded below a 5.5 that assignment has to be redone (retake). 

Inspection of assessed work

Students hand in their individual reports on Canvas, where the assignment will be graded and feedback provided. If more feedback is required students can request a feedback session.

Within 30 days after the grade is provided, students can request a feedback moment with a teacher.

Assignments

The two assignments will be done in groups of two students. For each assignment, students need to change group partner. It will not be allowed to do two assignments with the same group partner. The two assignments will be graded. Feedback will be provided via Canvas.

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Timetable

The schedule for this course is published on DataNose.

Additional information

We will arrange virtual machines for the students.

Contact information

Coordinator

• dr. P.M. Bleeker

Teachers

• dr. Marc Galland
• Tijs Bliek ing.
• Fred White, Msc
• dr. Anna Heinz-Busschart