Course manual 2022/2023

Course content

Genomics technologies, such as next-generation sequencing (NGS), are widely used in the life sciences. These technologies make it possible, for example, to study which genes are (dys)regulated after experimental treatments, or to determine which genes can play a role in diseases such as cancer, Alzheimer's or Huntington's disease. The expression level of all genes is then quantified genome-wide. In this type of transcriptome analysis (transcriptomics), the trajectory from the experimental design to the interpretation of the data is long. First, complex and sensitive molecular technologies are used in the wet lab. Subsequently, bioinformatics (dry-lab) plays an important role in interpreting the resulting large data sets. It is very important for students in the biomedical sciences to obtain experience with this type of work. During this course, students will be trained in drylab work, involving data (pre)processing, analysis and visualization. The teachers will show how omics data can be used to answer biological and biomedical research questions. A real-life experiment with a model organism or cell line will serve as a case study. The dry-lab work in this course, Practical Advanced Genomics II, builds on knowledge gained during the courses “Statistiek en onderzoeksmethoden 1 & 2”, “OMICS in de biomedische wetenschappen”, and complements Practical Advanced Genomics I.

Study materials

Literature

  • Available on Canvas and obtained using literature search.

Practical training material

  • Available on Canvas

Software

  • R or RStudio. All other software will be provided

Objectives

  • Explain basic principles of transcriptomics technologies.
  • Maintain Bioinformatics Labjournal
  • Advanced scripting skills for relevant programming languages; R and Linux bash.
  • Explain the (statistical) concepts and principles of some selected relevant bioinformatics methods that are required for analyzing and interpreting large scale omics data sets.
  • Is able to clearly structure complex omics data/display in clear figures and tables
  • Demonstrate that he/she can use selected relevant transcriptomics bioinformatics methods and tools in a correct way, and such that answers can be formulated to biomedical research questions.
  • Demonstrate that he/she can formulate relevant data analysis questions, generate results, and interpret the results such that these data analysis questions can be answered.
  • Demonstrate that he/she can experiment with large scale omics data sets in a data-driven research approach.

Teaching methods

  • Lecture
  • Computer lab session/practical training
  • Supervision/feedback meeting
  • Presentation/symposium
  • Self-study
  • Working independently on e.g. a project or thesis

Lecture: the lectures are given to provide scientific context and theoretical background.

Computer lab: Based on the preliminary discussions, lectures and the lab manual, the data analysis assignments are carried out, with assistance available. The results of the assignments must be recorded independently by the students in their lab journal. In this way, students are prepared for a larger project based on the data from Practicum Advanced Genomics 1. The students analyze data independently as much as possible, but with supervision, based on a research question. This forms the basis for a scientific report.

Presentation: The students will present the results and conclusions of their research project in a presentation session.

Self-study: The students will work on their research project, and learn for a formative test, in Self-study sessions, and supervision is often available.

Working independently: The students will work on their research project, but supervision is available.

Supervision/feedback meeting: Feedback and supervision will be provided in feedback sessions and ad-hoc in the practical room.

Learning activities

Activiteit

Uren

Practicals / Project / Lectures

142

Self-study

26

Total

168

(6 EC x 28 uur)

Academic skills

The skills (teaching and learning objectives) that are practiced and tested are listed in the Objectives section. There will be no direct link with the Academic Skills course, but the material will be used.

Assessment takes place according to the assessment models that are published on Canvas.

Attendance

Programme's requirements concerning attendance (OER-B):

  • Participation in all practical sessions, computer sessions, and seminars in the curriculum is obligatory.

Additional requirements for this course:

NL:

Mocht je wegens persoonlijke omstandigheden (denk hierbij aan ziekte of bijzondere familieomstandigheden) niet kunnen deelnemen aan een verplichte onderwijsbijeenkomst, neem dan direct per e-mail contact op met de vakcoördinator. De vakcoördinator bespreekt dan met je of er mogelijkheden zijn om het onderwijs op een andere wijze te volgen, en zo ja welke.

Ben je langdurig niet in staat om onderwijs te volgen (langer dan 1 week), neem dan ook contact op met de studieadviseur.

NB Covid-19: Houd je te allen tijde aan de RIVM richtlijnen, ook als dit betekent dat je daardoor één of meerdere verplichte onderwijsbijeenkomsten moet missen. Ook hiervoor geldt, neem direct contact op met de vakcoördinator zodat er samen gekeken kan worden naar een oplossing.

 

ENG:

If you are unable to participate in a compulsory educational meeting due to personal circumstances (e.g. illness or special family circumstances), please contact the course coordinator directly by e-mail. The course coordinator will then discuss with you whether there are possibilities to follow the education in a different way, and if so, which one.

If you are unable to follow education for a long time (longer than 1 week), please also contact the study advisor.

NB Covid-19: Adhere to the RIVM guidelines at all times, even if this means that you have to miss one or more compulsory educational meetings. In this case too, please contact the course coordinator directly so that we can look for a solution together.

 

Assessment

Item and weight Details

Final grade

0.45 (45%)

Report

0.3 (30%)

Presentation

0.25 (25%)

Labjournal

Assessment criteria:

Scientific Report (hourglass model)
- Research Question: Realistic, valid, creative; Note: introduction and biological context should be concise.
- Presentation Results: analysis questions, logical order, “storyline”.
- Figures and tables (see assessment matrix)
- Content & Discussion: Story line, some emphasis on methodology, creativity, complexity.
- Materials & Methods: can be relatively long, methodology is briefly correctly explained.

Presentation
(Introduction should be brief, the methodology can be discussed more in depth)
- The subject and methodology are correctly put into words and clearly explained
- Storyline with a certain emphasis on methodology; slides, figures and  are clear and logic.
- Discussion & answering questions

Labjournaal
- TOC, Readability and documentation code, Results and Conclusions.
- Scripting quality: meaningful names, good indentation, consistent design of script constructs, efficient.
- Choice of methodology: Correct, do we see reflection, is it "defendable", is the research question addressed.
- Data analysis approach: Do we see reflection, the results of executed code "snippets" are reported briefly and concisely, and conclusions are drawn.

 

Inspection of assessed work

Inspection sessions will be communicated during the course and via email

Assignments

Details about the major assignments can be found in Assessment section and the Teaching Methods section. It may happen that additional small "supporting assignments" will be provided. These will then be discussed during the seminars.

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Weeknummer Onderwerpen Studiestof
1 Practice RNAseq analysis en Scripting (case study) Canvas
2 Practice RNAseq analysis en Scripting (case study) Canvas
3 RNAseq Research Project Canvas
4 RNAseq Research Project Canvas

Timetable

The schedule for this course is published on DataNose.

Additional information

NOTE:

The teaching and learning goals have recently been changed. The coupling with the Learning Trajectory goals is not entirely correctly displayed.

Processed student feedback

The student feedback indicated that we should communicate more clearly the availability of the teachers.

We adjusted the description of the assignment to communicate better what is expected from the students.

Contact information

Coordinator

  • dr. M.J. Jonker