MEDFL5255 – Multi-omic data analysis and integration for precision medicine
Course description
Schedule, syllabus and examination date
Course content
The aim of this course is to teach students new approaches of multi-omic data analysis to study gene expression regulation in healthy and disease tissues.
The course will present different computational methods to analyze multi-omic datasets in healthy and disease settings. A specific focus will be given to the analysis and integration of datasets dedicated to the study of transcriptional gene regulation, systems biology, and cancer.
The students will get acquainted with good practices and hands-on experience to process, quality-control, visualize, summarize, and analyze large-scale multi-omics data sets. During the course, the students will be exposed to machine learning and computational approaches for managing, analysing, and interpreting next-generation sequencing data (e.g, ChIP-sequencing, mRNA sequencing, ATAC sequencing).
The first three days will be organized with lectures followed by practical sessions, while the last two days will contain lectures by national and international speakers followed by presentations of scientific manuscripts by the students.
Learning outcome
The students will learn how to computationally process, handle, and analyze multi-omics datasets to study transcriptional gene regulation in healthy tissues and diseases. Specifically, students will get familiar with:
- Quality control, basic alignment and pre-processing of Illumina sequencing data
- Analysis and quantification of gene expression data
- Processing and analysis of ChIP-sequencing data, IDR analysis and peak calling
- Processing and analysis of ATAC-seq data for chromatin accessibility, TF motif foot printing.
- Computational modeling of transcription factor (TF)-DNA interactions
- Quality-control for TF ChIP-seq data analyses
- Prediction of TF binding sites
- Computational prediction of transcriptional regulators acting upon gene expression regulation from omics data
- Prediction of cancer driver non-coding somatic mutations
- Pre-processing of data for network inference
- Integration of multi-modal data using network approaches
- Modelling gene regulatory networks for individual patients
- Comparative analysis of large-scale gene regulatory networks
- Discovery of somatic driver genes in cancer
- Prioritization of somatic driver genes based on the integration of cancer genomes and transcriptomes
- Discuss scientific papers describing multi-omics data analysis
Admission
MEDFL5255 is restricted to students at the Medical Student Research Programme at the Faculty of Medicine and the Faculty of Dentistry, UiO.
Maximum number of participants is 15.
Course registration:
- Students apply in StudentWeb
- This course has registration type Application.
- Applicants must wait for a reply to the course application. A reply will be given in StudentWeb and sent by e-mail within 1 week after the application deadline has passed.
Prerequisites
Formal prerequisite knowledge
- Basic knowledge working with Python and/or R
- Knowledge of working in a Shell environment
- Knowledge of Molecular Biology
Recommended previous knowledge
The course is mainly intended for biology students but is also open for students with a computer science or related background wishing to extend their knowledge of analytical approaches used in the biological domain.
Overlapping courses
- 5 credits overlap with BIOS-IN9410 – Bioinformatics for Molecular Biology
- 5 credits overlap with BIOS-IN5410 – Bioinformatics for Molecular Biology
- 5 credits overlap with IN-BIOS9000 – Genome Sequencing Technologies, Assembly, Variant Calling and Statistical Genomics
- 2 credits overlap with IMB9270 – Quantitative biology, or mathematics is biology’s next microscope
- 2 credits overlap with MEDFL5270 – Quantitative biology, or mathematics is biology’s next microscope
- 5 credits overlap with MF9255 – Multi-omic data analysis and integration for precision medicine
Teaching
This is a 5-day course, with classes running from 9.00-16.00.
The course is organized through lectures, practical computer lab exercises, group projects, and presentations of papers.
Literature will be scientific publications, shared two weeks prior to the start of the course. These will be used for the group presentations during the 5-day course.
Students will further have a 30 min individual discussion about three scientific papers with a course leader (via Zoom) in week 2.
You have to participate in at least 80 % of the teaching to be allowed to take the exam. Attendance at lectures will be registered.
Examination
The student will write a summary of lectures at the end of the course.
The exam paper and oral presentation should be in English.
Submit assignments in Inspera
You submit your assignment in the digital examination system Inspera. Read about how to submit your assignment.
Use of sources and citation
You should familiarize yourself with the rules that apply to the use of sources and citations. If you violate the rules, you may be suspected of cheating/attempted cheating.
Language of examination
The examination text is given in English, and you submit your response in English.
Grading scale
Grades are awarded on a pass/fail scale. Read more about the grading system.
Explanations and appeals
Resit an examination
Withdrawal from an examination
It is possible to take the exam up to 3 times. If you withdraw from the exam after the deadline or during the exam, this will be counted as an examination attempt.
Special examination arrangements
Application form, deadline and requirements for special examination arrangements.
Evaluation
The course is subject to continuous evaluation. At regular intervals we also ask students to participate in a more comprehensive evaluation.