Bioinformatics
MA216: Mathematical Molecular Biology II (5 ECTS)
This module is intended to give students an understanding and knowledge of the application of mathematical or algorithmic methods to defined problems in molecular biology. The focus is primarily on problems involving mutation discovery and evolutionary inference to predict mutation frequencies in a population as they change over time, and how to detect mutations of interest.
Taught in Semester(s) II. Examined in Semester(s) II.
Workload: 110 hours (24 Lecture hours, 6 Tutorial hours, 80 Self study hours).
Module Learning Outcomes.
On successful completion of this module the learner should be able to:
- Associate sets of mutations with other data types.
- Relate methods for quantifying mutation co-occurrence.
- Interpret how mutation patterns change between generations in a population and over evolutionary time between species.
- Assess population mutation data using a spectrum of tools and strategies.
- Identify appropriate and complementary tests for selective processes on genetic data.
Indicative Content
This course introduces aspects of population and evolutionary genetics and includes:
- Detecting mutation, mixing and linkage in DNA sequences.
- Genotype inference across genes in a population.
- How genome assembly algorithms affect mutation screening.
- Inferring population history and coalesence from genetic linkage patterns.
- Estimating population structure and admixture from DNA changes.
- The neutral theory of molecular evolution.
- Testing for adaptive evolution in a population.
- Linking genotype and phenotype data using hierarchical clustering and prediction models.
Module Resources
A primer of population genetics, 3rd edition. Daniel L Hartl, Sinauer Associates
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