Course INF282
Methods in Bioinformatics I
Course offered :
- Current semester
- Next semester
Current programmes of study
Course offered by
| Number of credits | 10 |
| Course offered (semester) | Spring |
| Subject overlap | I 283: 10 ECTS |
| Schedule | Schedule |
| Reading list | Reading list |
Language of Instruction
English
Pre-requirements
At least 120 ECTS in computer science, preferably including some mathematics
Learning Outcomes
After completing the course the student can:
- describe how log-odds scoring matrices (e.g. PAM, BLOSUM) are developed
- make programs to align and find patterns in a family of sequences and explain the connection to evolutionary trees
- describe how protein structures can be described and classified, and make programs to discover similarities (patterns) between two or several structures
- use knowledge about use of mass spectrometry in proteomics to develop programs relevant in this work
- understand how DNA-sequencing can be used to characterize genome and gen-expressions, and protein-DNA interactions
- understand use of micro matrices, sequencing and proteomics in functional genome-research, and make programs for some subtasks
Course offered (semester)
Spring
Language of Instruction
English
Aim and Content
The course mainly consists of methods to analyse biological sequences and structures: description and representation, comparison (in pairs and multiple), description and detection of joint properties (motif), classification.
Learning Outcomes
After completing the course the student can:
- describe how log-odds scoring matrices (e.g. PAM, BLOSUM) are developed
- make programs to align and find patterns in a family of sequences and explain the connection to evolutionary trees
- describe how protein structures can be described and classified, and make programs to discover similarities (patterns) between two or several structures
- use knowledge about use of mass spectrometry in proteomics to develop programs relevant in this work
- understand how DNA-sequencing can be used to characterize genome and gen-expressions, and protein-DNA interactions
- understand use of micro matrices, sequencing and proteomics in functional genome-research, and make programs for some subtasks
Pre-requirements
At least 120 ECTS in computer science, preferably including some mathematics
Recommended previous knowledge
INF 280, STAT 101 (Elementary Statistics)
Subject Overlap
I 283: 10 ECTS
Grading Scale
The grading scale used is A to F. Grade A is the highest passing grade in the grading scale, grade F is a fail.