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Syllabus:

# Statistics in Genetics, 7.5 Credits

Swedish name: Statistiska metoder i genetik

This syllabus is valid: 2017-08-21 and until further notice

Course code: 5MS059

Credit points: 7.5

Education level: Second cycle

Main Field of Study and progress level: Mathematical Statistics: Second cycle, has only first-cycle course/s as entry requirements
Statistics: Second cycle, has only first-cycle course/s as entry requirements
Computational Science and Engineering: Second cycle, has only first-cycle course/s as entry requirements

Established by: Faculty Board of Science and Technology, 2017-12-12

## Contents

In the course, methods for analysing different types of genomic data are studied. The first part covers methods for annotating genomic sequences in order to describe the properties and structures of a genome. Here, methods for finding genes and deciding the equality of sequences, are treated. Multinomia models, Markov models, hidden Markov models and Monte Carlo simulation constitute the base of the described methods. The second part covers methods for studying genetic variation within and between species, evolution and reconstruction of evolutionary mechanisms. The third part of the course covers methods for treating the analysis of high-dimensional genomic expression data (e.g. microarray data), including basic normalization, identification of affected variables and clustering of variables. The course is built around a couple of complex biological problems, where the students identify and formulate specific hypotheses, choose suitable analysis methods, test hypotheses using various software (e.g. BLAST och Bioconductor), and interpret the results. An important part of the course is to make the students well acquainted with the analysis process for complex biological problems.

## Expected learning outcomes

For a passing grade, the student must be able to

Knowledge and understanding

• describe the theory and the assumptions that constitute the fundament for sequence analysis methods based on multinomial models, Markov models, hidden Markov models and Monte Carlo-simulation, and account for the limitations and advantages of the different methods
• account for the principal differences between ab inito-based methods and statistically based methods
• explain the difficulties in analyzing high-dimensional genomic data

Skills

• analyze sequential data, using ab inito-methods and methods based on multinomial models, Markov models, hidden Markov models and Monte Carlo- simulation, including the identification of biologically relevant (functional) open reading frames, and demonstrating global and local sequence equality between two sequences
• analyze sequential data in order to estimating genetic distances with Jukes-Cantor approximations, estimating the ratio between the number of non-synonymous and synonymous mutations in two sequences using the Nei-Gojobori algorithm, and constructing phylogenetiska trees with the neighbor-joining algorithm
• analyze microarray data from two-channel experiments, including data processing, identification of affected variables and clustering of variables
• structure a complex biological problem, identify testable hypotheses, choosing suitable analysis methods, identify and apply suitable software for the conduction of the analyses, and interpret and value the results

Judgement and approach

• value when it is suitable to use different methods for sequence analysis, analysis of genetic variation/evolution, and methods for analysis of high-dimensional genomic data.

## Required Knowledge

The Course requires 90 ECTS including at least 15 ECT in Mathematical Statistics or 90 ECTS including at least 7,5 ECTS in Mathematical Statistics and at least one of the courses Genetics and Gene Technology (5MO018), Gene Expression (5MO019), Structural Biology (5MO022) or Bioinformatics and Genome Analysis (5MO020). Proficiency in English and Swedish equivalent to the level required for basic eligibility for higher studies.

## Form of instruction

The teaching mainly consists of lectures and computer exercises.

## Examination modes

The course is examined  by written home exercises and written presentations of group projects.
On the home assignments and group projects, one of the following judgements is assigned: Fail (U), Pass (3), Pass with merit (4), or Pass with distinction (5). For the whole course, one of the following grades is assigned: Fail (U), Pass (3), Pass with merit (4), or Pass with distinction (5). In order to receive a passing grade on the course, all parts must be completed with a passing judgement. The course grade is decided by the lowest of the judgements on the home assignments and the group projects, and is assigned only when all mandatory examination has been completed.

A student who has received a passing grade on a test is not allowed to retake the test in order to receive a higher grade. A student who has not received a passing grade after participating in two tests has the right to be assigned another examiner, unless there are certain circumstances prohibiting this (see the Higher Education Ordinance, chapter 6, 22§). A request to be assigned another examiner should be adressed to the head of department for the department of mathematics and mathematical statistics. The possibility of being examined based on the current version of the syllabus is guaranteed for at least two years following the student's first participation in the course.

​Credit transfer
All students have the right to have their previous education or equivalent, and  their working life experience evaluated for possible consideration in the corresponding education at Umeå university. Application forms should be adressed to Student services/Degree evaluation office. More information regarding credit transfer can be found on the student web pages of Umeå university, http://www.student.umu.se, and in the Higher Education Ordinance (chapter 6). If denied, the application can be appealed (as per the Higher Education Ordinance, chapter 12) to Överklagandenämnden för högskolan. This includes partially denied applications.

## Other regulations

This course can not be included in a degree together with another course with similar contents. When in doubt, the student should consult the director of study at the department of mathematics and mathematical statistics.

## Literature

### Valid from: 2018 week 3

Christianini M.W
Introduction to Computational Genomics
Cambridge Universtity Press ISBN 978-0-521-67191-0 : 2006 :
Mandatory