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

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 (mRNA-seq) data, genomic DNA sequencing. Some post-analytical tools like gene ontology enrichment analysis and clustering/heat-maps of differentially expressed genes are introduced to students and included in the hands-on exercises.

This course is primarily aimed at PhD candidates admitted to the PhD programme in Health Sciences. General terms for admission to the course is a completed master’s degree in molecular biology or equivalent qualification (e.g. completed MABIO4410). Priority will be given to PhD candidates from OsloMet.

Note that all students must have a laptop not more than 2 years old (mac is easiest, but windows with linux also works). The laptop must be able to connect to a wireless network, and the student must have administrative rights to download and install software and bioinformatic packages.

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.

Admission to the course may also be offered to master students attending the Master’s Programme in Biomedicine / Master in Health and Technology, by prior approval from the thesis supervisor and depending on course capacity. These students must have completed the first 60 ECTS credits of the Master’s programme including MABIO4410, and write a master’s project where this course is considered relevant.

This course provides in-depth knowledge of quantitative design and statistical analysis that can be used in the students’ master’s theses, the possibilities and limitations of different quantitative designs, knowledge of different sources of data and the social science research tradition’s quality requirements for studies based on statistical analysis.

The course includes in-depth knowledge of cross-sectional design, time design, experiments and the survey method. Students will also acquire in-depth knowledge of the factors that can affect the validity and reliability of a study, as well as skills that are necessary to assess such factors. They will also learn and, not least, practise using univariate, bivariate and multivariate analyses.

The course will pay particular attention to regression analysis, and students will acquire in-depth knowledge of and skills in the use of regression-based analysis through lectures, online resources and seminars. The course aims to enable students who choose a quantitative design to use regression analysis in their master’s theses.

Language of instruction is Norwegian.

After completing the course, the student should have the following overall learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The student has

• in-depth knowledge of quantitative designs and which types of knowledge they can contribute
• advanced knowledge of different methods used to generate/obtain empirical material, such as surveys and register data
• in-depth knowledge of quality assessment in the quantitative tradition
• insight into the possibility of generalising the results of quantitative studies
• advanced knowledge of the dissemination of results
• in-depth knowledge of regression-based analysis/statistical analysis

Skills

The student can

• engage in methodological reflection on the relationship between research questions and design
• develop a research design for their master's thesis that is appropriate to their research question
• apply statistical analysis techniques and interpret the results they produce
• develop statistical analysis models based on theory and empirical data
• engage in methodological reflection on the relationship between research design and knowledge claims
• acquire in-depth knowledge of the methodological approach chosen for their master’s degree project
• engage in methodological and critical reflection on other research literature
• evaluate research ethics issues in connection with their master's degree project
• evaluate which questions can be empirically studied

General competence

The student

• is familiar with and able to reflect on different consequences of conducting research
• can apply ethical discretion in relation to their own role as researchers and how their participation in research can intervene in people's lives
• is familiar with and are able to comply with ethical guidelines such as correct and complete source information, informant anonymisation, confidentiality and researcher responsibility

Teaching takes place in the form of lectures on campus, statistics program exercises, e-lectures and assignments that the students work on independently and receive supervision on via e-based resources.

The following coursework requirements must have been approved for the student to take the exam:

• Coursework 1: individual project sketch. The project sketch must have a scope of up to 1 page.

The project sketch must be completed and approved by the given deadline for the student to take the exam. If the project sketch has not been approved, the student will be given one opportunity to submit an improved version by a given deadline. Coursework that is not approved on the second submission will disqualify the student from taking the exam.

School examination: No aids are allowed.

Semester assignment: All aids are permitted, as long as the rules for source referencing are complied with.

Grade scale A-F.