DS832: Introduction to Genomic Data Science

The Study Board for Science

Teaching language: English
EKA: N340106102
Assessment: Second examiner: None
Grading: Pass/Fail
Offered in: Odense
Offered in: Spring
Level: Master

STADS ID (UVA): N340106101
ECTS value: 5

Date of Approval: 30-10-2023


Duration: 1 semester

Version: Approved - active

Comment

This course introduces genomic data science in medicine. The targeted students are those who are interested to work in the field of genetic and omics researches in basic and clinical medicine.  

Genomic medicine is a rapidly developing and hot area of research and translational applications. Expertise in genomic data science is highly needed in biomedical research.

Entry requirements

None

Academic preconditions

Basic knowledge in biology, statistics and computer programming.

Course introduction

The aim of the course is to introduce the concept and molecular mechanisms in genomics, high-throughput technologies for genomic analysis from array to next-generation sequencing, different types of genomic data on DNA sequence variation, molecular profiles of gene activity regulation, gene expression, and their interactive networks. The course focuses on biostatistics and bioinformatics approaches and tools for analyzing, interpreting and presenting the different genomic data, followed by their applications in biomedical and translational research. As genomic data are privacy sensitive, issues related to ethical, legal and societal implications of genomic data will also be introduced. 

The course is built upon the knowledge and competence acquired in the first-year courses of data sciences and basic data analytical skills (statistics and R programming).

Expected learning outcome

General competences: 
1. Knowledge about the needs and opportunities of the specialization when working with and processing data
2. Competences in selecting, applying, and combining the right programming, statistics, and bioinformatics tools and methods to work with data relevant for the specialization
3. Skills in managing complex work and development situations in the areas of data processing and analysis as well as starting up and executing analyses.

Expected learning outcomes
The learning objective of the course is that the student demonstrates the ability to:
1. Understand how high-throughput techniques work in genomic analysis.
2. Know the pipelines for handling different types of genomic data from data preprocessing, quality control, to statistical analysis and bioinformatics. 
3. Become familiar with different types of experiment design and corresponding statistical analysis.
4. Write R scripts and use proper tools for conducting data analysis, illustration, and presentation.
5. Be able to functionally interpret the findings from genomic data analysis using bioinformatics tools.

Content

  1. Introduction to basic concepts of genomics, epigenomics, transcriptomics and proteomics
  2. High-throughput techniques for genomic analysis and types of genomic data
  3. The human genome project and open-source genomic databases
  4. Experiment design in genomic analysis
  5. Pipelines and software for raw data preprocessing and quality control
  6. Statistical models for genomic data analysis
  7. Network analysis of genomic data
  8. Biological pathway analysis
  9. Machine learning for prediction
  10. Application in biomedicine
  11. Ethics of genomic data

Literature

See itslearning for syllabus lists and additional literature references.

Examination regulations

Exam element a)

Timing

June

Tests

Oral exam

EKA

N340106102

Assessment

Second examiner: None

Grading

Pass/Fail

Identification

Student Identification Card - Name

Language

Normally, the same as teaching language

Duration

30 minutes

Examination aids

To be announced during the course

ECTS value

5

Indicative number of lessons

48 hours per semester

Teaching Method

The teaching method is based on three phase model.
  • Intro phase: 24 hours
  • Skills training phase: 24 hours, hereof tutorials: 6 hours and data analysis exercises: 18 hours
Activities during the study phase:
  • Data analysis exercise after lecture.
  • Self study of various parts of the course material.
  • Reflection upon the intro and training sections.

Teacher responsible

Name E-mail Department
Qihua Tan qtan@health.sdu.dk Epidemiologi, Biostatistik og Biodemografi (EBB)

Additional teachers

Name E-mail Department City
Dorthe Almind Pedersen dapedersen@health.sdu.dk Department of Public Health Odense
Mads Thomassen mthomassen@health.sdu.dk KI, OUH, Forskningsenhed for Human Genetik (Odense) Odense
Marianne Nygaard mnygaard@health.sdu.dk Department of Public Health Odense

Timetable

Administrative Unit

Institut for Matematik og Datalogi (datalogi)

Team at Registration

NAT

Offered in

Odense

Recommended course of study

Profile Education Semester Offer period

Transition rules

Transitional arrangements describe how a course replaces another course when changes are made to the course of study. 
If a transitional arrangement has been made for a course, it will be stated in the list. 
See transitional arrangements for all courses at the Faculty of Science.