Applied bioinformatics and data analysis in medical research

Ph.D. -course

Course description

Course content

The main objective of this course is to provide the candidates with the knowledge central for successful application of bioinformatics and data analysis in clinical research on human tissue.

The course will focus on practical aspects and methodological considerations that needed to be taken into account when dealing with human derived data, such as data sensitivity, limited sample sizes, sample misclassification, choice of appropriate statistical models, and covariates, and tissue heterogeneity. The course will be composed of seminars, lectures, and hands-on tutorials given by clinicians and researchers based on real-life scenarios. The course will provide an inspiring environment, promoting networking between trainees and researchers, promoting career development, and encouraging future collaborations.

The course will be highly beneficial for all participants with an interest in bioinformatics, biology, medicine, or clinical research in general.

Learning outcomes

Upon completing the course, the candidate will have gained knowledge of the following:

    • Understanding of basic statistical models, benefits and limitations of covariate inclusion.
    • Legal and practical implications of working with sensitive data.
    • General understanding of possibilities and limitations of Next Generation Sequencing (NGS) data, with an emphasis on RNA and whole genome/exome sequencing.
    • Biobanks both in term of access and sample collections.
    • Understanding of the limitations and constraints of studies involving human tissue.

Upon completing the course, the candidate will gain the following skills:

    • Big data analysis and dimensionality reduction.
    • Data quality control: removal of low quality samples, detection of sample outliers and data filtering, identification of mislabeled samples.
    • Basic data wrangling with R.

Upon completing the course, the candidate will gain the following competences:

    • Designing experiments under constrains of scarse sample availibility.
    • Descision making based on metadata and other existing information (including power estimation based on preliminary data).

Study period

8.-10. November 2023

Credits (ECTS)


Course location

Campus UiB / Haukeland University Hospital
Language of instruction
Course registration and deadlines

Registration in Studentweb for internal students.

Deadline: September 1st

Having been admitted to a Master Programme or equivalent, or students in the Medical Student Research Programme. Students are required to have a basic knowledge of statistics. Familiarity with next-generation sequencing and a basic understanding of molecular biology and/or genetics are desirable.
Recommended Previous Knowledge
Familiarity with bioinformatics, transcriptomics/genetic analyses, and basic programming/scripting in R and/or python.
Part of training component

Recommended as part of the training component for PhD candidates affiliated with the Research School of Neuro-SysMed.

The course is highly recommended for Master students towards the end of their degree, and PhD candidates in their first year. However, PhD candidates in more progressed stages of their research, and postdocs, will also greatly benefit from this course.

Form of assessment

Pass/ Fail

To pass the course the candidate must have:

1. Prepared for the course by reading scientific papers.

2. Participated in the course days and actively work on course tasks and made an oral presentation of own research.

3. Written a methodological reflection note (2-3 pages).

Course overlap
The course will be focusing on real-life applications of the material, which is not covered by any of the existing courses.
Who may participate
The course is open to students (PhD, master, students in the Medical Student Research Program), postdocs and researchers with clinical/biology/bioinformatics background. The course will be open nationally and internationally.

All tasks are obligatory in order to merited ECTS:

1. Prior to the beginning of the course, the students will receive a list of scientific papers related to the covered topics. Students are expected to critically read and familiarize themselves with the papers prior to the lectures (20 hours).

2. Each course day will begin with a seminar from a clinician or a researcher who will present their research, serving as a real case scenario for topics covered during that day. The seminar will be followed by a set of lectures, and group discussions. After a one-hour lunch break, with food provided, there will be practical hands-on tutorials. These will use real-life data, covering the daily topics, and the students will be guided and assisted by expert teachers (21 hours).

3. On the last day, the student will prepare a flash talk (5 min) describing their own project. The same project can be used for the written assignment (5 hours).

4. For the final assessment, the students will be required to write a methodological description of a project on a research topic of their choice. The project can be based on their ongoing research or any other topic of interest. The description should cover all relevant considerations and topics covered throughout the course and is to be submitted two weeks after the course (30 hours).

Total work: 76 hours

Academic responsible

Main responsibles:

Gonzalo S. Nido
Fiona Dick


Eirik Tveit Solheim


Dr. Gonzalo S. Nido

Dr. Fiona Dick


Invited speakers

Reading list