The practicals found below have primarily been developed for specific teaching duties of the group. However, you may find some of it useful for other purposes, so now it is made freely available to you here!
Practicals from the DTC statistics module 2017
The links provide a folder with different files. Look for a .pdf-file labeled instructions or project description or something similar to get started.
Markov Random Fields – Denoise an black-white image using Python
Gibbs sampling – Find Motifs in DNA-sequences using MATLAB
Clustering – Identify clusters in Wikipedia articles on birds using R
Diffusion – Solve stochastic differential equation modelling animals or stocks using your favorite programming language
Finance – Financial Forecasting with Linear and Non-Linear models using your favorite programming language
Hidden Markov Models – Modelling sequence evolution along a phylo-genetic tree using Python
Variational Auto-encoder – Perform fast variational inference with neural networks using MATLAB or Python
Practicals from Advanced Bioinformatics Module 2016
Annotation 17.3.2016 – Finding protein coding genes
Coalescent 16.3.2016 – Simulation and visualisation of coalescent trees
Comparative Biology 22.3.16 – Inferring conservation and phylogenetic footprinting
Integrative Genomics 21.3.16 – De Bruijn graph assembly from short read sequences
Phylogenetics 15.3.2016 – Introduction to phylogenies (combinatorics and maximum parsimony)
Recombination 18.3.2016 – Estimation of recombination rates and hotspots in a population using linkage disequilibrium and haplotype mapping
Substitution models 14.3.2016 – Introduction to DNA substitution models
What is a practical?
In our terminology, a practical is a problem set which mostly focuses on the computational aspects. An example could be to download the some software and apply it to analyse a data set.
This distinguishes it from exercises, which is usually solved using pen and paper.
You may also be interested in the exercises.