Course description forPHVIT9560 Bioinformatics with emphasis on analysis of high throughput sequencing data

Introduction

The course will introduce students to basic bioinformatics including best practice when setting up and managing bioinformatics projects. The course covers introduction to high throughput sequencing technologies and will give students hands-on experience with the analysis of data from various sequencing platforms. Applications that are included in the practical part are processing of raw data reads, control of quantity and quality of data (FASTQC), expression analysis of small RNA sequencing data (miRNA) and transcriptome sequencing/microarray (mRNA-seq, cDNA) data, and detection of variation (e.g. SNPs) after resequencing (variant calling).

Learning outcomes

On completion of the course, the PhD candidate has achieved the following learning outcomes, defined in terms of knowledge, skills, and general competence:

 

Knowledge

The PhD candidate

  • is able to conduct bioinformatics analysis projects in agreement with best practice (transparency and reproducibility) in the field of bioinformatic science's philosophy

  • is in the forefront of knowledge about the current high throughput sequencing (HTS) technologies and understands the differences, benefits and drawbacks of these HTS technologies

  • can evaluate and make sound decisions on which platform and bioinformatic approach to use for different HTS projects.

  • Can contribute to development of new knowledge and interpret results from various HTS applications

 

Skills

The PhD candidate can

  • Plan a HTS research project and choose optimal sequencing platform

  • Carry out the relevant bioinformatic analyses both on the command-line (unix) and R-studio, and utilize web-based resources like Galaxy server and Genbank E-utilities.

  • Interpret the results of bioinformatics analysis of HTS (e.g. reliability, sensitivity and specificity) and judge their value for answering biological questions

  • Disseminate the results of HTS based research

 

General competence

The PhD candidate can

  • argue in favour of particular HTS technologies or bioinformatic approaches on the basis of current knowledge

  • argue in favour of the kind of materials and the number of samples to select/include in different kinds of HTS projects

  • can participate in discussions on HTS methodology

Teaching and learning methods

The course consists of three weeks consecutive work that includes the following teaching methods: self-study including exercises/questionaries related to background theory (one week), lectures and seminars (one week), and practical exercises in the use of different software programmes for analysis of HTS data (one week). The outcomes of the practical exercises in last week are discussed in plenary sessions.

Course Requirements

It is required that students complete all the obligatory practical exercises.

Assessment

All obligatory exercises must be completed to take the final exam. The final exam is a written examination with invigilation, 4 hours. One internal and one external examiner will assess the answer papers submitted by all candidates.

Grading scale

Grades are awarded on the basis of pass or fail.

Examiners

One internal and one external examiner will assess the answer papers submitted by all candidates.

Admission requirements

This course is primarily aimed at PhD candidates admitted to the PhD programme in Health Sciences and PhD students from Memorial University, Newfoundland. General terms for admission to the course is a completed master's degree in molecular biology or equivalent qualification (e.g. completed MABIO4400). Priority will be given to PhD candidates from HIOA and Memorial University, Newfoundland.

Note that all students must have a laptop not more than 2 years old (windows 7 or more recent or mac with OS X). The laptop must be able to connect to wireless network.

The course can also be offered to students who have been admitted to the "Health Science Research Programme, 60 ECTS", by prior approval from the supervisor and based on given guidelines for the research programme.

Course information

Course name in Norwegian
Bioinformatikk med fordypning i analyse av nestegenerasjons sekvenseringsdata
Study programme
Fall: Doktorgradsstudium i helsevitenskap
Weight
5 ECTS
Year of study
2020
Curriculum
FALL 2020
Schedule
FALL 2020
Programme description
Fall 2020: Doktorgradsstudium i helsevitenskap
Subject History