Course content

The goal of this course is to prepare biotechnology students to an increasingly computational job market. In the last years, we have witnessed several technological advancements in high-throughput multi-omics approaches, which are generating vast amounts of data and shifting the bottleneck in the development of new biotechnological applications from the wet-lab bench to the computer (so-called dry-lab) bench. This goal also aligns with NTNU’s vision of increasing digital strength in education and research.

The content of the course derives from tightly interconnected fields, namely bioinformatics, computational biology, systems biology, and data science. The syllabus is divided in two main parts:

Part I - From sequences to functions:

• The history of genome sequencing
• Methods for genome assembly
• Sequence alignments and phylogenetic trees
• Protein structures and domains
• Functional annotation and ontologies

Part II - From functions to systems:

• Multi-omics technologies
• Data analysis and visualization
• Biological networks and pathways
• Pathway enrichment analysis
• Modeling biological systems

Learning outcome

• Learn the history of bioinformatics and computational biology.
• Understand genome sequencing methods and their limitations.
• Apply computational tools for genome assembly and annotation.
• Explain the principles and applications of sequence alignment.
• Extract information from bioinformatics databases using command line tools.
• Develop scripts to process and analyze different types of datasets.
• Combine different data sources to analyze the function of genes and proteins.
• Understand the features and limitations of multiple omics technologies.
• Contextualize diverse information using biological pathways.
• Create mathematical models of simple biological systems.

Learning methods and activities

The course will consist of:

• Lectures: 3 h/week (theory and active discussion)
• Computer lab: 3 h/week (programming exercises)
• Projects and self-study: approx. 6 h/week

Compulsory assignments

• Group project 1
• Group project 2

Recommended previous knowledge

TDT4110 Information Technology, Introduction or similar introduction to programming (python).

Course materials

Will be given at the start of the course.