MF9255 – Multi-omic data analysis and integration for precision medicine
Course description
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).
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 to the course
Maximum number of participants is 15.
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.
Applicants admitted to a PhD programme at UiO apply to this course in StudentWeb.
Applicants who are not admitted to a PhD programme at UiO must apply for a right to study before they can apply to this course. See information here: ?How to apply for a right to study and admission to elective PhD courses in medicine and health sciences.
Applicants will receive a reply to the course application in?StudentWeb?at the latest one week after the application deadline.
Formal prerequisite knowledge
- Basic knowledge working with Python and/or R
- Knowledge of working in a Shell environment
- Knowledge of Molecular Biology
Overlapping courses
- 5 credits overlap with MEDFL5255 – Multi-omic data analysis and integration for precision medicine.
- 2 credits overlap with MF9395.
- 2 credits overlap with MEDFL5395.
Teaching
This is a five day course, with classes running from 09.00 - 16.00.
The course is organized through lectures, practical computer lab exercises, group projects, and presentations of papers.
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.
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 five day course.
Students will further have a 30 minute individual discussion about three scientific papers with a course leader (via Zoom) in week 2. The oral presentation should be in English.
You have to participate in at least 80 % of the teaching to be allowed to take the exam. Attendance will be registered.
Examination
Home exam where the student should write a summary of lectures.
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.
More about examinations at UiO
- Use of sources and citations
- Special exam arrangements due to individual needs
- Withdrawal from an exam
- Illness at exams / postponed exams
- Explanation of grades and appeals
- Resitting an exam
- Cheating/attempted cheating
You will find further guides and resources at the web page on examinations at UiO.