MF9395 – In silico Study of Genome Regulation – Omics Data Analysis
Course description
Schedule, syllabus and examination date
Course content
There are three main goals in the course "In silico study of genome regulation":
1) Overview of genome regulation in biomedical research (e.g., molecular biology of protein-DNA interactions, histone modification, DNA methylation, DNA mutations and 3- D genome regulation via chromatin-chromatin interactions), the effect of genome regulation in general and their association with disease, and the basic data analysis skills such as the application of Unix, R, and Python in high throughput sequencing.
2) Applications of statistical methods and data mining tools in big data analysis based on various experiments such as from the aforementioned topics in transcriptomics, epigenomics, and 3D genome regulation et al. For example, the application of various high throughput sequencing (e.g., RNA-seq, ChIP-seq, ATAC-seq, Hi-C, WGS, and WGBS etc) in molecular biology research and the relevant data analysis and result interpretation, and the understanding of data mining methods (e.g., regression, clustering, PCA, network prediction etc.) utilized in in silico genome regulation.
3) Integrated data analysis based on the above-mentioned multiple-omics datasets. This will be illustrated through advanced research topics from the earlier publications, where omics-datasets were used to tackle a specific biological problem such as network inference, tumor classification, regulatory mutation prediction, and differential methylation analysis et al.
Learning outcome
From the proposed course, students will acquire basic knowledge in in silico study of genome regulation such as the analysis of various experimental datasets (e.g., transcriptomics, epigenetics, 3D chromatin organization, protein-DNA interaction, and DNA mutations etc) and the application of multiple-omics datasets in biomedical research.
In the course, students will not only learn the theory of methods and tools in computational genome regulation, but also practice the computational analysis by using diverse genomic datasets. For example, analysis of gene expression, histone modifications/TF-binding, nucleosome density, DNA methylation profiles, DNA mutation, and 3D chromatin organization in RNA-seq, ChIP-seq, ATAC-seq, WGBS, WGS, and Hi-C experiments, respectively. Especially, real research examples of in silico genome regulation studies will be illustrated based on earlier publications.
After the course, students will understand the difference between the low-level analysis (e.g., quality assessment, alignment, normalization) and the high-level analysis (e.g., peak calling, visualization, differential analysis, genome annotation) in high throughput sequencing. For example, the former one is a routing work and time consuming but with standard pipelines, which are often done by an engineer in the core facility. The latter one?requires a person with both domain specific knowledge and advanced bioinformatics skills such as a researcher. Finally, students will be able to design/plan/perform in silico study of genome regulation by using multiple-omics datasets, and will be capable of searching for correct external help (e.g., collaborators) in future.
Overall, the proposed course will be an excellent learning source for the next generation biologists or biomedical researchers for interested students locally at Campus Ahus. Nowadays, biology becomes more and more quantitative, there are far more datasets than qualified personnel can handle. It is essential for the future biologists or biomedical researchers to have sufficient knowledge in both molecular biology and quantitative data analysis. In a metaphor, people do not need to build a car but they have to learn to drive a car to their desired destination. That is all this course is designed for, the PhD/master students, who are devoted to molecular biology research.
Admission to the course
Applicants admitted to a PhD programme at UiO sign up for classes and exam 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 sign up for classes and exam 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 upon registration receive an immediate reply in StudentWeb?as to whether a seat at this course is granted or not.
Formal prerequisite knowledge
Students should have basic knowledge in statistics (e.g., MF9130, MF9130E), applied mathematics, molecular biology, biochemistry, or related subjects in advance of this course.
Recommended previous knowledge
It is important to have general programming skills (e.g., R, Python, or Matlab et al) and Unix shell script experience before taking the course. However, students are encouraged to participate in the course, if they have a strong desire and motivation to learn the aforementioned informatics skills.
Overlapping courses
- 5 credits overlap with MEDFL5395 – In silico Study of Genome Regulation – Omics Data Analysis.
- 2 credits overlap with MF9155 – Introduction to statistics and bioinformatics for the analysis of large-scale biological data.
- 2 credits overlap with MEDFL5155 – Introduction to statistics and bioinformatics for the analysis of large-scale biological data.
- 2 credits overlap with MF9255 – Multi-omic data analysis and integration for precision medicine.
- 2 credits overlap with MEDFL5255 – Multi-omic data analysis and integration for precision medicine.
Teaching
The course is organized as full day teaching over six days.
The course includes lectures, computational data analysis, group study of scientific papers and projects, and take-home materials.
The course materials include lecture notes, selected chapters from textbooks, scientific papers, and computational demos with real application examples. These will be released before lectures (e.g., one week earlier) in ample time. Students are expected to read them before and after the lectures. Some of course materials will be used for group discussion and projects during the courses.
Furthermore, students will be given the opportunity to discuss with the course leader their selected topics from the course materials (e.g., scientific publications, textbook, or computer demos) before the end of course.
You have to participate in at least 80 % of the teaching to be allowed to take the exam. Attendance will be registered
Examination
In the exam, students will deliver a written report for how they carry out a pseudo research project that involves multi-omics data analysis in high throughput sequencing (e.g., gene expression, histone modifications, transcription factor binding, 3D chromatin interactions et al.). In the report, students shall show the ability to perform necessary data analysis and be familiar with the tools that can be used in the data analysis. Overall, students shall deliver expected results (e.g., tables and figures) based on their planned work in the written report.
The deadline for submitting the exam is four weeks after the course.
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.