MLED5900 Masteroppgave Emneplan

Fysiske samlinger på campus i Oslo på 2 dager hver, i tillegg til 2 digitale samlinger med organisert undervisning der det også vil bli gjort opptak som legges ut til deltakerne i læringsplattformen, og som vil være tilgjengelig i hele undervisningsperioden.

Samlingene er en blanding av forelesninger (inkludert eksperter fra næringslivet), og veiledet arbeid i grupper.

Innlevering av 3 skriftlige arbeidskrav (700-1400 ord)

Innlevering av skriftlig prosjektoppgave, enten individuell (1750-2100 ord i tillegg til evt appendiks) eller i gruppe på 2 personer (3500-4200 ord i tillegg til evt appendiks).

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

• knows how 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 Geneontology.org 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

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

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

All obligatory exercises must be completed to take the final exam. The final exam is an individual home examination, 6 hours.

Ingen

One internal examiner.