University of Galway

Course Module Information

Course Modules

Semester 2 | Credits: 5

This course covers applications of probabilistic models and related techniques in genomics and systems biology. Beginning with a review of stochastic processes, the course will consider the use of Hidden Markov models (HMMs) to predict genes and identify genomic regions with shared epigenetic characteristics; the use of continuous-time Markov processes to model molecular evolution; applications of Gibbs sampling to infer haplotypes from genotype data among other models and applications.
(Language of instruction: English)

Learning Outcomes
  1. derive key results that are applied in the course;
  2. decode sequences of symbols generated from a HMM using the Viterbi algorithm;
  3. calculate hidden state probabilities using forward/backward algorithms;
  4. align a pair of DNA or amino acid sequences using a probabilistic model;
  5. apply probabilistic models to describe sequence evolution over a phylogenetic tree;
  6. infer haplotypes from a set of genotype data by hand;
  7. describe several problems in molecular biology/systems biology and explain the application of probabilistic models to solve these problems;
  8. construct a pair-HMM for sequence alignment.
Assessments
  • Written Assessment (70%)
  • Continuous Assessment (30%)
Teachers
Reading List
  1. "Biological Sequence Analysis" by n/a
The above information outlines module MA461: "Probabilistic Models for Molecular Biology" and is valid from 2015 onwards.
Note: Module offerings and details may be subject to change.