Dates: 2 – 6 October 2023.
Location: St.Olavs Hospital/NTNU, Trondheim.
Organiser: Morten Beck Rye.
Evaluation: Report based with grade pass/fail.
Course code: MOL8008
Registration: COLSED. If you wish to earn credits, you will find information about this HERE.
Registration deadline: September 10th, 2023.
Credits: 7,5 ECTS.
Course page at NTNU: HERE
Lecturers:
- Morten Beck Rye (NTNU)
- Vidar Beisvåg (NTNU)
- Kjetil Klepper (NTNU)
Course description
The course will introduce bioinformatic approaches, tools and pipelines for computational analyses of Next Generation Sequencing (NGS) data. Focus will be on analysis methods for coding and non-coding RNA from RNA-seq, and transcription factors and epigenetic markers from ChIP-Seq. The course will cover strategies, methods and workflows used for analyses of such data, including mapping to reference genomes, feature extraction, and statistical analysis. In addition, the biological interpretation of output from such analyses will be presented as case studies from scientific journals.
Course program
The first part of the course will be one full week with intensive lectures (50%) and hands-on exercises (50%). This will take place in late September/early October in Trondheim. The students must attend this week to pass the course. The second part is a self-study and project work, which should be delivered in written form by a date set in early November. This work can be done at each students’ resident university. The exam will be on a pass/fail basis based on the evaluation of their project work.
Learning outcomes and competence
After completing the course the student should be able to: 1. Create and use an analysis pipeline for topics introduced 2. Understand the bioinformatics approaches used for different tasks in NGS 3. Have an overview of the type of biological questions that are typically assessed using NGS 4. Assist in the design and implementation of an experiment using NGS at their own facility
Prerequisites
Required: Masters degree in relevant programmes. Medical Doctors degree. Or medical students at The Student Research Programme. Candidates with other or lower degree will be assessed individually. Recommended: Familiarity with basic high-level programming of scripting languages like Python, R and Matlab. Some basic knowledge in applied bioinformatics.
Evaluation
The exam will be on a pass/fail basis based on the evaluation a project work using the tools used in the course on a relevant biological/methodological hypothesis suggested by the student.