NMAK15021U Statistical Methods and Probability in Bioinformatics
MSc programme in Statistics
Statistical methods play an important role within many areas of
bioinformatics. In many cases, problems in bioinformatics have
stimulated the development of novel statistical methods and topics
in applied probability. Bioinformatics is a lively and active
cross-disciplinary research area, driven by biotechnological
advances. There is a big need for people with knowledge about
analysis of genomic data, a primary target area of bioinformatics.
The course is for students in statistics and mathematics who would
like to learn about the statistical methods that are used in the
analysis of genomic data. The course might also be useful for
bioinformatics and computer science students, with a reasonable
background in statistics.
The aim of the course is to provide a fundamental understanding of genomic data, statistical methods and problems in bioinformatics.
In the course we will discuss statistical methods and probabilistic topics that have influenced the way we collect, store and analyse genomic data. The course contains topics such as shotgun sequencing, BLAST, random walk theory, Hidden Markov chains, continuous-time Markov chains (substitution models), methods for reconstructing evolutionary trees and the EM-algorithm for estimating parameters. We will make use of the software package R.
The aim of the course is to provide a fundamental understanding of data, statistical methods and problems in bioinformatics.
At the end of the course, the students should be have acquired the following knowledge, skills and compentences:
Knowledge:
- about the basic statistical and probabilistic elements i the analysis of genomic data
- about existing statistical methods, techniques and algorithms for solving complex problems in the analysis of genomic data
Skills:
- to implement and evaluate statistical methods within the analysis of genomic data
- to apply R for the analysis of genomic data
Compentences to:
- describe and analyse existing problems and statistical methods that are essential in the analysis of genomic data
- apply and explain statistical methods and computational algorithms for the analysis of special types of genomic data
- discuss the use of existing statistical methods and techniques for solving complex problems in the analysis of genomic data
Ewens, W.J. og Grant, G.R. (2005). Statistical methods in Bioinformatics. Springer.
- Category
- Hours
- Exam
- 27
- Exercises
- 21
- Lectures
- 28
- Preparation
- 130
- Total
- 206
As
an exchange, guest and credit student - click here!
Continuing Education - click here!
- Credit
- 7,5 ECTS
- Type of assessment
- Practical written examination, 27 hoursTake home exam
- Exam registration requirements
Three approved assignments out of three.
- Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
One internal examiner
- Re-exam
30 min oral exam without preparation; no aids allowed; If the three mandatory assignments were not approved before the ordinary exam they must be resubmitted and approved at the latest tree weeks before the re-exam. Two internal examiners.
Criteria for exam assesment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.
Course information
- Language
- English
- Course code
- NMAK15021U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 2
- Schedule
- A
- Course capacity
- No limit
- Continuing and further education
- Study board
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Course responsibles
- Carsten Wiuf (4-7f717d6e4875697c7036737d366c73)
Lecturers
Daniele Cappelletti