PHVIT9560 Bioinformatics with emphasis on analysis of high throughput sequencing data Course description

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 at the forefront of knowledge in selected qualitative research designs and their theoretical basis, and related methodological considerations
• has in-depth knowledge and understanding of interviews and observation as methodological research tools in the phenomenological, hermeneutic, and discourse-analytic research traditions
• can evaluate the usefulness of different forms of analysis, interpretation, and documentation within the relevant traditions

Skills

The PhD candidate can:

• plan a health science research project with relevant qualitative designs and methods
• analyse, interpret, and disseminate the results of qualitative research
• address complex scientific issues and challenge established knowledge and practice in qualitative methodology

General competence

The PhD candidate can:

• argue in favour of particular qualitative approaches on the basis of scientific theory
• identify relevant ethical issues and conduct research based on qualitative methodology with professional integrity

participate in discussions on qualitative methodology

Work and teaching methods consist of lectures, seminars, self-study, practical exercises in pilot interviews, and analysis of authentic interviews and observation notes. The outcomes of the seminars are presented and discussed in plenary sessions.

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

Individual home examination based on specific questions. To be submitted no more than 2 weeks after the end of the course. Answer papers must consist of up to 3,500 words.

All

Pass / Fail

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

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.

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.