NMAK15021U Statistical Methods and Probability in Bioinformatics

Volume 2015/2016
Education

MSc programme in Statistics

Content

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.

Learning Outcome

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.

 

VidSand1, Stat1, Beting
Lectures (2+1 hrs/week), computer and theoretical exercises (3 hrs/week)
  • Category
  • Hours
  • Exam
  • 27
  • Exercises
  • 21
  • Lectures
  • 28
  • Preparation
  • 130
  • Total
  • 206
Credit
7,5 ECTS
Type of assessment
Practical written examination, 27 hours
Take 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.